Whoever they were, they were definitely in Pompeii's upper crust. In 79 A.D., the sky above the city of Pompeii, Italy, turned gray. But it wasn't snow that began falling—it was ash. Mount Vesuvius was erupting. For 12 hours, the volcano rained ash and pumice on the city, and soon the eruptions would worsen. Mount Vesuvius began spewing hot gas and lava fragments (part of multiple pyroclastic surges), killing many of the 20,000 citizens living in Pompeii and nearby Heracleum. Today, both the tragedy and the region more broadly are still a major area of study. Archaeologists recently excavated the Porta Sarno necropolis in Pompeii and discovered a funerary relief featuring statues of a life-sized man and woman. The statues, likely a husband and wife, are part of a larger monumental tomb, consisting of a large wall with several niches. The statues' archaic features and carving quality suggest they were likely made during the late Republic Period—a time of immense social inequality, with elites making their power known through funerary monuments. The presence of the funerary relief and other details on the statues lead researchers to believe that the husband and wife were influential in high society. For instance, the man is depicted wearing a toga that reaches his mid shin. Researchers suggest this was to show off his “calcei patricii,” a type of footwear worn by upper class Romans. The ring on his left hand and intricacy of his eyes and curls were also indicators of high status. Findings from the archaeological dig were published in the E-Journal of the Excavations of Pompeii. Interestingly, some of the woman's accessories suggest she may have been a priestess of Ceres, goddess of fertility, motherly relationships, and agriculture. She is depicted wearing a necklace with a crescent moon in the middle called a lunula. These amulets were typically worn by girls before marriage, but the presence of a lunula on a married woman suggests that she is a priestess, as Ceres was often symbolically connected to the moon. The statue of the woman also showed her holding a laurel asperigillum—a ceremonial tool used to bless spaces. The excavation effort is a joint effort between the University of Valencia and the Pompeii Archaeological Park, one that began in July 2024 and is part of the larger research project Investigating the Archaeology of Death in Pompeii. This is not the first time the Porta Sarno necropolis has been explored, however. The area was also excavated in the 90s for construction of the Circumvesuviana, a railway network in Italy. According to a press release, the 1998 excavations revealed more than 50 cremation monuments marked by stelae and funerary arches, functioning similarly to modern-day headstones. The statues have since been moved to Palestra Grande at the excavation site for restoration. In the future, the statues will be displayed as a part of the larger Being a Woman in Ancient Pompeii exhibition, set to open on April 16. The public will be able to view live-time restoration of the statues as a part of the exhibit. “This campaign is a precious opportunity to expand research and enhancement activities in the area outside the walls of Pompeii” Gabriel Zuchtriegel—Director of the Park—said in the translated press release. Emma Frederickson is a Pace University student by day, journalist by night. She enjoys covering anything from pop culture to science to food. Her work appears in several publications including Biography.com and Popular Mechanics. When she's not writing, Emma can be found hopping between coffee shops on the hunt for the world's best oat milk cappuccino. A 3-Year-Old Discovered a 3,800-Year-Old Amulet Archaeologists Found a 2,000-Year-Old Roman Tomb Two Beachfront Explorers Found an Ancient Dagger A Metal Detectorist Dug Up Two Ancient Daggers Scientists Fix a 200-Year-Old Skeleton Mix-Up Experts Find Missing Piece of Ramesses II Statue A Metal Detectorist Found a Massive Iron Age Hoard Humans' First North American Footsteps Tracked Archaeologists Discovered a 2,200-Year-Old Pyramid A Passerby Discovered an Ancient Viking Artifact Volunteer Archaeologist Finds Centuries-Old Ring Prehistoric Dinosaur Footprints Found in Wales A Part of Hearst Digital Media We may earn commission from links on this page, but we only recommend products we back. ©2025 Hearst Magazine Media, Inc. All Rights Reserved.

Some experts think it could be a “goldmine” for new antibiotics. Gear-obsessed editors choose every product we review. We may earn commission if you buy from a link. Why Trust Us? The human immune system is a remarkable evolutionary tool that is often taken for granted—until, that is, something gets past its cellular defenses. However, this complex system of cells, organs, and proteins is even more complicated than we originally thought. Researchers from the Weizmann Institute of Science in Israel have found a novel mechanism of our immune system we never knew existed. This new piece of the immunity puzzle centers around cellular structures known as proteasomes. These structures are found in every cell in the body, and they're tasked particularly with recycling old proteins. Typically, proteasomes chop up old proteins into smaller chunks by producing chemical reactions that break down peptide bonds. This ensures cellular help and cuts down on the build up of “junk” proteins that are damaged or otherwise unneeded. But in a new study, published in the journal Nature, scientists from the Weizmann Institute of Science discovered that proteasomes perform another important immunological role. Simply put, proteasomes leave behind short protein sequences that helped the immune system identify threats. In this new study, Yifat Merbl and her team discovered that these peptides had the ability to actually kill bacteria on their own. In fact, the researchers say that proteasomes increase the production of bacteria-killing peptides when encountering bacterial infections. “Before now, we knew nothing about the connection between proteasome products and the production of these peptides,” Merbl, senior author of the study, said in a press statement. “In light of our findings, we conducted an extensive series of experiments demonstrating that the proteasomes are key to this defense system.” To pinpoint this new immunization strategy, scientists inhibited human cells from producing proteasomes in one group while leaving them untouched in another, and then infected each cell culture with salmonella. The infection thrived in the former, and died in the latter. Similarly, the scientists destroyed the peptides produced to fight the infection, and found similar results. Then, the team tested the idea on mice infected with a life-threatening bacteria that can cause sepsis and pneumonia. When treated with a peptide-derived treatment, the mice contained less of the bacteria, experienced a decline in tissue damage, and increased their overall chances of survival. The team drilled down even further to figure out what was causing the change in the proteasome's peptide-producing abilities. and pinpointed a control unit called PSME3. In subsequent tests, they confirmed that this subunit was responsible for the increased production of bacteria-fighting peptides. “We saw that infection causes the proteasome to change its protein-cutting mode, ‘favoring' the production of peptides with antibacterial properties,” Merbl said in a press statement. “This peptide database opens a new frontier for developing personalized treatments against infections and other medical conditions.” While experts unaffiliated with the study tell the BBC that such medicinal interventions could be many years away and require immense experimentation, they won't argue that nature has once again provided an opportunity to learn from its incredible immunological powers. Darren lives in Portland, has a cat, and writes/edits about sci-fi and how our world works. You can find his previous stuff at Gizmodo and Paste if you look hard enough. Scientists Took A Huge Step Towards Curing Anthrax Scientists Want to Grow Spare Human Bodies. This Is the Secret to Being a Supercentenarian Man Survives With Titanium Heart for 100 Days Humans May Be Able to Grow New Teeth in 6 Years New Medicine May Help New Teeth Grow Inside the Chernobyl Dogs' Strange Genetic Changes This is How Magic Mushrooms Warp Our Reality Breathing Like This Can Alter Your Consciousness Scientists Found the Speed Limit of Human Thought Unraveling the Burden of Kidney Dialysis This Is Your Brain on Forever Chemicals A Part of Hearst Digital Media We may earn commission from links on this page, but we only recommend products we back. ©2025 Hearst Magazine Media, Inc. All Rights Reserved.

April 4, 2025 3 min read Mathematicians Solve Decades-Old Spinning Needle Puzzle For a long time, the Kakeya conjecture, which involves rotating an infinitely narrow needle, kept mathematicians guessing—until now By Manon Bischoff edited by Daisy Yuhas Sean Gladwell/Getty Images Join Our Community of Science Lovers! It is rare to read about “spectacular progress” or a “once-in-a-century” result in mathematics. That's for good reason: if a problem has not had a solution for many years, then completely new approaches and ideas are usually needed to tackle it. This is also the case with the innocent-looking “Kakeya conjecture,” which relates to the question of how to rotate a needle in such a way that it takes up as little space as possible. Experts have been racking their brains over the associated problems since 1917. But in a preprint paper posted in February, mathematician Hong Wang of New York University and her colleague Joshua Zahl of the University of British Columbia finally proved the three-dimensional version of the Kakeya conjecture. “It stands as one of the top mathematical achievements of the 21st century,” said mathematician Eyal Lubetzky of N.Y.U. in a recent press release. Suppose there is an infinitely narrow needle on a table. Now you want to rotate it 360 degrees so that the tip of the needle points once in each direction of the plane. To do this, you can hold the needle in the middle and rotate it. As it rotates, the needle then covers the surface of a circle. If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. Amanda Montañez But if you are clever, the needle can make its 360-degree journey while taking less space. In 1917 mathematician Sōichi Kakeya wanted to investigate the smallest area required to rotate the needle. For example, by rotating not only the outer end of the needle but also its center, you can obtain an area that corresponds to a triangle with curved sides. Amanda Montañez Years later, mathematician Abram Besicovitch made an unexpected discovery. If you keep moving the needle back and forth like a complex parallel parking maneuver, the surface that the infinitely narrow needle covers can actually have a total area of zero. From there, experts began to wonder what dimension this “Kakeya surface” has. Usually surfaces in a plane, such as a rectangle or a circle, are two-dimensional. But there are exceptions: fractals, for example, can also have fractional dimensions, meaning they can be 1.5-dimensional, for instance. Because the Kakeya surfaces can look very jagged, the question of dimensionality is an obvious one. In fact, it has implications for many other areas of mathematics, including harmonic analysis, which breaks down complicated mathematical curves into sums of simpler functions, and geometric measure theory. In fact, in 1971 mathematician Roy Davies was able to prove that the Kakeya surface is always two-dimensional, even if its area vanishes. But in mathematics, people are interested in general results. The experts wanted to solve the problem in n dimensions—does a needle that is rotated along all n spatial directions always cover an n-dimensional volume? This hypothesis is now known as the Kakeya conjecture. The three-dimensional case proved to be an extremely hard nut to crack. Over the decades, experts have been able to rule out the possibility that a rotating needle covers a volume with less than 2.5 spatial dimensions, but that was as far as they got. Wang and Zahl were not discouraged, however, and worked their way forward step by step. Through painstaking effort, they gradually managed to eliminate all cases in which the covered volume would have a dimension of less than three. In this way, they were finally able to prove the Kakeya conjecture in three spatial dimensions, showing that the volume covered by the needle is always three-dimensional. In the recent press release, mathematician Guido de Philippis of N.Y.U. commented, “I am expecting that their ideas will lead to a series of exciting breakthroughs in the coming years.” This article originally appeared in Spektrum der Wissenschaft and was reproduced with permission. Manon Bischoff is a theoretical physicist and an editor at Spektrum der Wissenschaft, the German-language sister publication of Scientific American. Learn and share the most exciting discoveries, innovations and ideas shaping our world today. Follow Us: Scientific American is part of Springer Nature, which owns or has commercial relations with thousands of scientific publications (many of them can be found at www.springernature.com/us). Scientific American maintains a strict policy of editorial independence in reporting developments in science to our readers. © 2024 SCIENTIFIC AMERICAN, A DIVISION OF SPRINGER NATURE AMERICA, INC.ALL RIGHTS RESERVED.

The “Anti-Stratfordians” are putting their faith in an AI authorship test. This story is a collaboration with Biography.com William Shakespeare is undeniably one of the most famous writers in human history. The 39 shows attributed to the “Bard of Avon” have been performed, adapted, and studied innumerable times in the centuries since they debuted, and his 154 sonnets are some of the most quoted poems in the world. The very name Shakespeare has become synonymous with the dramatic arts. But for a segment of the literary community some might call “conspiracy theorists,” it shouldn't be. Not because they believe the plays themselves, like Hamlet and Julius Caesar, are incorrectly placed within the literary canon. Rather, they think they're simply incorrectly labeled; specifically, on the author page. This contingency, known as the Anti-Stratfordians (in reference to Shakespeare's home of Stratford-upon-Avon), argue that The Bard's lack of education and modest upbringing don't square with the vast vocabulary on display in Shakespeare's celebrated plays. “They note that both of Shakespeare's parents were likely illiterate,” Biography.com states in further explaining the stance of the Anti-Stratfordians, “and it seems as if his surviving children were as well, leading to skepticism that a noted man of letters would neglect the education of his own children.” The Anti-Stratfordians also claim that “none of the letters and business documents that survive give any hint of Shakespeare as an author,” and raise questions like “Why was there no public mourning for him when he died?” But these claims can all be refuted to one degree or another by those who believe in Shakespeare's authorship. Shakespeare's modest background? It's ultimately not dissimilar to that of Christopher Marlowe, a peer of Shakespeare's whose authorship of celebrated plays like Doctor Faustus has never been in doubt. In response to the claim of a lack of contemporary records, Biography.com notes that “Tudor officials responsible for ascertaining authorship of plays attributed several works to Shakespeare.” And the claim of a lack of mourning is undercut by no less than Jacobean author Ben Jonson, whose esteemed poem “To the Memory of My Beloved the Author, William Shakespeare” reads: These debates of authorship tend to treat inference as evidence, and as such, can never really be conclusive. But a new study published by Oxford University Press offers new insight into the authorship debate. And it does so by taking the human element out entirely. The study from Zeev Volkovich and Renata Avros, titled “Comprehension of the Shakespeare authorship question through deep impostors approach,” decided to see if a deep neural network could do what centuries of scholars could not: conclusively identify works attributed to, but not written by, William Shakespeare. The duo refer to their methodology for the analysis as “Deep Imposter”: After a process which converted these text segments into numerical signals, the tested texts were clustered into two groups, which can be simplified into a score of 1 or 2. Those texts in cluster 1 would be those determined to be “imposter texts” not composed by the author in question. When Shakespeare's works were run through the aforementioned CNN neural network, a staggering fifteen titles were placed into cluster 1. Those included not just the usual suspects of “Shakespeare Apocrypha” (works with no clear author sometimes attributed to Shakespeare) like A Yorkshire Tragedy and Arden of Faversham, but also some of the most beloved staples of the Shakespeare canon like The Merry Wives of Windsor, The Tragedy of Antony and Cleopatra, and A Midsummer Night's Dream. But before you go scribbling out Shakespeare's name from your copy of King John, understand that this isn't an ironclad system, nor do the study's authors claim it is. Instead, they note that this study was intended to introduce “a novel methodology for investigating the stylistic fingerprints of authorship” in a way that “goes beyond analyzing isolated words, encompassing intricate patterns across multiple linguistic structures.” Earlier tests they cite in their study show that a work appearing in cluster 1 doesn't mean with absolute certainty that it's not written by its attributed author. For example, an early test fed the neural network some works by the authors Charles Dickens and John Galsworthy. “The distribution of works within the clusters accurately reflects their original authorship,” the team behind the study wrote wrote. “Specifically, two of the three sections of ‘A Christmas Carol' are attributed to Charles Dickens. In contrast, only one of the six parts of ‘Flowering Wilderness' is included in this category.” But nobody should come away from reading this study becoming a “one-third of A Christmas Carol” truthers or anything like that. Dickens' authorship of that famous story isn't in doubt, nor is the aforementioned Galsworthy's of Flowering Wilderness. So, what could be causing this misidentification? The study cites another test run, this one feeding the neural network the works of essayist Francis Bacon and playwright Christopher Marlowe. This found a number of Bacon's essays falling into cluster 1. Their explanation? Not some second, false author posing as Bacon, but rather Bacon's own “literary journey.” Bacon reworked and refined his Essays from 1597 to 1625, such that they “span a spectrum of styles, from the straightforward and unadorned to the epigrammatic.” Therefore, a departure in literary style from one work to another doesn't necessarily mean a different authorial hand, but rather an artistic development playing out over years of trial and error, as well as personal growth. Few authors with any prolific volume will sound identical to themselves from years earlier, especially if their work undergoes heavy revisions over time. Particularly in the case of a dramatist, revisions, rewordings, and entire reworkings of plays can occur based on rehearsals, collaborator suggestions, and audience reactions. So, while this method can point out that A Midsummer Night's Dream is linguistically distinct from the bulk of Shakespeare's other work, it can't say for sure whether that's because the play was written by a secret second author, or just a case of throwing in a riff on Apuleius' The Golden Ass to get an extra giggle or two out of an audience—even if it wasn't Shakespeare's usual style. Michael Natale is a news editor for the Hearst Enthusiast Group. His stories have appeared in Popular Mechanics, Best Products, and Runner's World. How Tech Bros Almost Killed America's New Fighter Is America's Hypersonic Missile Finally Ready? Could Salt Help Power Floating Nuclear Reactors? How to Paint a Car ‘Dinosaur' Sightings Are on the Rise in the Congo This Battery Has Practically Unlimited Energy Conscious ‘Alien Minds' Could Be Living Among Us Is AI Actually a Form of Alien Intelligence? The ‘Space Laser' Wars Have Begun Did Human Consciousness Come From Stoned Apes? This Technique Can Alter Your Consciousness The World's First ‘Fighter Drones' Are Coming A Part of Hearst Digital Media We may earn commission from links on this page, but we only recommend products we back. ©2025 Hearst Magazine Media, Inc. All Rights Reserved.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Geoscience (2025)Cite this article Metrics details Ice-sheet mass loss is one of the clearest manifestations of climate change, with Antarctica discharging mass into the ocean via melting or through calving. The latter produces icebergs that can modify ocean water properties, often at great distances from source. This affects upper-ocean physics and primary productivity, with implications for atmospheric carbon drawdown. A detailed understanding of iceberg modification of ocean waters has hitherto been hindered by a lack of proximal measurements. Here unique measurements of a giant iceberg from an underwater glider enable quantification of meltwater effects on the physical and biological processes in the upper layers of the Southern Ocean, a region disproportionately important for global heat and carbon sequestration. Iceberg basal melting erodes seasonally produced winter water layer stratification, normally forming a strong potential energy barrier to vertical exchange of surface and deep waters, while freshwater run-off increases and shoals near-surface stratification. Nutrient-rich deeper waters, incorporating meltwater loaded with terrigenous material, are ventilated to below this stratification maxima, providing a potential mechanism for alleviating critical phytoplankton-limiting components. Regional historical hydrographic data demonstrate similar stratification changes during the passage of another large iceberg, suggesting that they may be an important pathway of aseasonal winter water modification. The global ocean is warming at approximately triple the historical rate1, forcing increases in ice-shelf melting and iceberg calving2,3. Such calving accounts for approximately half the mass discharge (1,300 Gt yr−1) from Antarctic ice sheets, with 90% passing through the western Weddell Sea via ‘iceberg alley'4. Implicated in climate fluctuations, including the modulation of glacial–interglacial cycles5 and Heinrich events6, the hydrographic impact of icebergs is poorly understood and not represented explicitly in climate models, largely due to the sparsity of field measurements of melt rates, spreading and entrainment of iceberg-derived freshwater. Iceberg deterioration and dissolution can cause an appreciable freshwater flux into near-surface layers, strongly modifying upper-ocean stratification7 and near-surface biological productivity8. Differential temperatures and velocities create a turbulent boundary layer for heat transfer9,10, causing side and basal melting, the instigator of substantial upwelling of water from below9,11. Horizontally, meltwater plumes can extend tens of kilometres12,13, with many studies showing noteworthy water and biomass modification within 2 km of the iceberg14,15,16,17,18. Such plumes can supply terrigenous nutrients that can promote phytoplankton growth19,20,21,22. Upwelling can generate an episodic vertical nutrient transport11,16,23, producing a spatially heterogeneous environment with respect to ocean productivity, with algal stock increases often delayed by 6–10 days after iceberg passage, probably due to the interaction of physical and biological processes14,15,16,18. In July 2017, A-68 calved from the Larsen-C ice shelf in the Weddell Sea24, the sixth largest iceberg on record at the time4, with an area of 5,800 km2. Subsequently, A-68 tracked northwards across the Scotia Sea (Fig. 1a), with the largest fragment (named A-68A) losing approximately one-third of its size as it approached South Georgia (SG). As A-68A recirculated to the southeast of SG, it fragmented further, probably triggered by ocean-current shear mechanisms25. Surface meteoric water concentrations exceeded 4% close to SG due to meltwater from A-68A26, with 152 ± 61 Gt of freshwater fluxed within 300 km of SG between November and March 2021 (ref. 27), 27 times that of the annual freshwater outflow from SG28,29. a, The trajectory of iceberg A-68A across the Scotia Sea from 21 January 2020 to 12 February 2021. The iceberg shapes are coloured according to date49. b, The trajectory of iceberg A-68A from 14 February to 22 March 2021 when the glider–iceberg separation was <75 km. The glider was trapped from 14 February to 4 March (transparent). Each coloured triangle (iceberg) and corresponding coloured square (glider) are temporally matched, with the minimum separation distance between the glider and iceberg edge shown below. For both panels, the bathymetry (GEBCO Compilation Group, 2023) is shown with 1,000 m and 0 m isobaths illustrated as blue and black, respectively, and the ACC fronts are overlain using the SEANOE dataset50. SAF, subantarctic front; PF, polar front (PF); SACCF, Southern Antarctic circumpolar current front; SB, southern boundary. In summer, this region of the Southern Ocean (SO) has a highly distinctive density structure with a surface mixed layer (ML) above a winter water (WW) temperature inversion (minima ~125 ± 25 m) caused by the presence of a cold subsurface winter remnant. This acts as a potential energy (PE) barrier to the warm, nutrient-rich circumpolar deep water ((CDW) temperature maxima ~500 m) below30,31. Overcoming this PE barrier can entrain heat, salt and nutrients into the ML, impacting primary productivity and air–sea gas exchange between the deep ocean, surface layers and the atmosphere. These physical changes have direct and indirect impacts on ecosystems and the cycling of nutrients and carbon. Here, we report results from an innovative underwater glider deployment close to, and under, A-68A. When combined with shipboard and satellite measurements, the observations provide sufficient resolution to disentangle the effects of iceberg-derived meltwater from variability induced by the complex hydrographic, frontal and eddy structure of the region, allowing us to interpret and quantify iceberg-influenced ocean properties. Quantification of basal meltwater is compared with satellite-derived estimates, and shipboard data elucidate possible mechanisms of meltwater dispersion. Biogeochemical impacts in the wake of A-68A are examined and we conclude with assessment of historical hydrographic data to assess the regional impacts of meltwater from other giant iceberg transit events. We find consequential impacts from iceberg-induced mixing on stratification and the vertical supply of nutrients, with strong implications for globally important SO processes. To investigate the impact of A-68A meltwater on upper-ocean physics, productivity and biogeochemistry, the RRS James Cook conducted a series of conductivity–temperature–depth (CTD) and lowered acoustic Doppler current profiler (LADCP) profiles in close proximity to A-68A. On 14 February 2021 a Slocum glider was deployed, 23 km from A-68A. Equipped with sensors for physical (temperature, salinity and pressure) and bio-optical (chlorophyll-a and backscatter) measurements, this glider tracked within 75 km of A-68A for 49 days, collecting 265 vertical profiles up to 1 km in depth (Fig. 1). This study focuses on the first 19 days of the deployment, where the glider approached A-68A from the ‘upstream' side relative to the current. Two days after deployment, the glider became trapped under the thinner side of the iceberg relative to its calving edge at a depth of 163 m; this reduced to 112 m after 12 h, likely illustrating the uneven bottom of this giant iceberg. Using satellite altimetry27, the average of draft of A-68A on the 17 February was calculated to be 141 m. Combining these drafts, we obtain an estimated iceberg depth of 139 m. Ocean conditions around SG are characterized by strong interannual variability and high biological productivity, with the surrounding ocean sitting more generally in the high nutrient low chlorophyll SO (regions of the SO where micronutrient iron has been shown to limit phytoplankton growth32). The Southern Antarctic Circumpolar Current Front (SACCF) and the Southern Boundary (SB) loop anticlockwise around the island from the south (Fig. 1), with numerous mesoscale meanders and eddies. Variability in transport and location of these fronts means isolating the effects of iceberg melt within this region can be challenging. The fronts and eddies obfuscate local hydrography, while iceberg fragments, growlers and brash ice follow current cores identified by sea surface height (SSH) contours, with fragments observed to rotate in eddies identified by circular SSH maxima (Fig. 1b). To differentiate iceberg signals from background hydrography, a gravest empirical mode (GEM) parameterization33 was calculated from historical data (Methods). The temperature and salinity profiles at all glider–iceberg separations are significantly different to that of the GEM (Fig. 2). The profiles are classified into three distinct regimes with average glider to iceberg separations of 15.2 ± 5.3 km (far), 2.6 ± 0.22 km (near) and 0.28 ± 0.21 km (adjacent) (Fig. 2). The stratification (N2) mean for each classification is plotted in Fig. 2c. a,b, The glider realizations of conservative temperature (a) and absolute salinity (b) approaching A-68A from left to right, along with the GEM historical hydrography (black) with bootstrapped 95% confidence intervals (shaded grey) matched by dynamic height. Overlain are the individual glider profiles, coloured according to classification with blue (far), magenta (near) and red (adjacent)), with a running mean (dotted cyan) overlain. The mean glider–iceberg separation for each dynamic height group and classification is shown between a and b. The horizontal green line shows the mean iceberg draft. The data are staggered by an incremental offset for illustration. c, Buoyancy frequency (N2) calculated from the running mean of each glider classification, with appreciable maximal stratification accentuated with horizontal lines (top and bottom are identical). d, The Ri estimated using ship-borne CTD and LADCP (the locations of which are illustrated in Fig. 1), with the vertical dashed green line showing the 0.25 threshold for criticality (top and bottom are identical). The grey shading in c and d is the quantified extent of upwelled basal meltwater (see Basal meltwater contribution). A fresh cold water cap is evident in the adjacent and near profiles at ~9 m and ~16 m depth, respectively. This subducts the warmer saltier surface water, leading to a second peak in stratification at ~44 m and ~63 m depth for adjacent and near profiles, respectively. The far profiles, although offset from the GEM with warmer, saltier surface waters and cooler fresher water to ~200 m, exhibit a similar-shaped profile with a single broad stratification peak at ~73 m depth. In adjacent profiles, this WW layer is eroded, with stratification increasing above and below this well-mixed layer. Near profiles fall between these regimes. The eroded WW layer is also apparent in glider profiles when escaping the trapping event (not shown), but the highly recirculatory flow combined with the presence of numerous iceberg fragments means these profiles cannot be categorized by ‘distance from iceberg' and are excluded from analysis. The WW erosion can be used to quantify the basal meltwater contribution of A-68A when considered in temperature–salinity (T–S) space (Fig. 3, with the inset highlighting the extreme sea surface salinities and temperatures on exiting the trapping event, the slope of which matches that of a freshwater run-off line). Water masses affected by meltwater are apparent in the form of intrusions, where the T–S profiles depart from the classic WW temperature minima. These intrusions are warmer and saltier along isopycnals, consistent with the upwelling of warm and saline water below the WW layer11,34. A T–S diagram, with cast coloured by the distance from A-68A. The contours show the potential density at 0.5 kg m−3 intervals. The red circled cast, at 2.4 km from A-68A, highlights a prominent meltwater intrusion with the associated Gade line (in green) overlain. Density classes indicating WW and CDW are labelled. With A-68A estimated to be moving at a velocity of 0.13 m s−1 in a geostrophic flow of 0.26 m s−1 (Methods), turbulent basal melting will occur, generating a small amount of fresh, cold water, which mixes with a much larger volume of ambient ocean water. This produces water with a characteristic slope in T–S space, known as the Gade line35 (Methods). This water is positively buoyant compared with in situ properties and thus upwells, creating T–S intrusions. Water constrained by ridges/keels under the iceberg may intermittently ‘spill', entraining water as it rises to attain a new level of neutral buoyancy. Such complex interacting processes may result in multiple intrusions in a single profile or variability in their shape. Here, we quantify the meltwater content within intrusions by determining the relative proportions of ambient and Gade line water masses along isopycnals, following ref. 11 (Methods). The mean depth of the ambient T–S source of the intruded waters was 238 ± 7.8 m, with a maximum of 250 m to a minimum of 230 m as the glider moved within 2 km of A-68A, possibly impacted by internal waves induced by the wake of the iceberg. The mean volume of water upwelled was 72.8 ± 15.3 m3 m−2, with a mean meltwater content of 0.52 ± 0.1 m3 m−2 over a mean vertical extent of 106 ± 7.8 m from 197 m up to 91 m (Fig. 2c,d), influencing depths greater than the iceberg draft. Intrusions cease at distances between 2.71 and 5.5 km from A-68A. Assuming the mean meltwater content is advected over an area of 3 km, integration yields an estimated basal meltwater contribution of 1.9 × 108 m3 (Methods). Taking the limits of iceberg and geostrophic flow speed, we obtain advection rates of 6.9 × 108 m3 day−1 and 1.4 × 109 m3 day−1, respectively. Quantifying the freeboard change over time27 yielded 1.7 × 109 m3 day−1 for basal melting, meaning satellite estimates of melt rate are between 1.25 and 2.49 times our in situ estimates. The satellite and in situ estimates of basal melt are thus in broad agreement, especially considering the assumptions and inherent differences in measurement. To understand whether iceberg meltwater is vertically distributed within the water column via turbulent mixing, the Richardson number ((Ri) the ratio of potential to kinetic energy; Methods) is quantified using three ship-borne CTD and LADCP profiles, two at 4.5 km and one at 2 km from A-68A (Fig. 1). When the Ri falls below one-quarter (Fig. 2d, vertical green line), shear is considered sufficient to overcome the stability of stratification and turbulent mixing will likely occur. Buoyant meltwater creates an unstable water column and shear is likely to increase at the boundaries of stable stratification36. Ri is minimal when N2 is small, at the fringes of the stratification maxima in adjacent and near profiles. Active mixing is observed beneath the fresh cold water cap where warmer waters are subducted, and closest to the fully mixed temperature and salinity profiles. There is also active mixing near the base of the meltwater intrusions (grey shading), possibly signifying a boundary layer dragged by the iceberg. The locations of the peaks in Ri suggest that the change in stratification as the distance from the iceberg increases is due to turbulent mixing, likely generated by the upwelling plume, sidewall melt, surface water run-off and iceberg wake, with an influence extending below that of the iceberg draft. SG and its immediate surroundings are situated in a micronutrient-limited region of the Antarctic Circumpolar Current (ACC), with trace metal sources derived from the deep ocean, shelf sediments and glacial flour released from its melting glaciers37. Stratification changes induced by A-68A, the potential for micronutrient injection, loss by cell lysis, grazing, dilution or mixing with deeper marine waters or meltwater could have pronounced biogeochemical impacts that may affect the productivity of the region14. Figure 4b shows low near-surface chlorophyll adjacent to A-68A whereas the backscatter is relatively high, likely illustrative of meltwater releasing particulates while simultaneously diluting in situ standing stocks and/or increased turbidity causing lower light penetration and growth limitation. a, A MODIS Aqua satellite image (see Acknowledgements) from 16 February 2021, overlain with A-68A outlines in February, before (blue) and during (coloured by date) the experimental campaign, with the day shown. The glider positions are coloured by date, except the red point (the last measurement before becoming trapped, 15 min before image acquisition). b, Glider-derived estimates of chlorophyll-a and backscatter plotted against glider–iceberg separation. c, Outlines of A-68A from 14 to 16 February are overlain on satellite altimetry SSH contours, indicating the geostrophic flow direction. From 8 to 12 February, the flow direction is consistent, all coloured blue. Subsequently, as the flow direction backs, the colours follow those of a. The overlain glider positions are coloured by integrated chlorophyll over the top 100 m, with quivers illustrating the full depth mean velocities (quiver key, top right in c). At greater glider–iceberg separations, the near-surface increases in both chlorophyll and backscatter are apparent. Surface chlorophyll maxima at ~16.7 km, when scaled with iceberg velocity, suggest a peak occurring ~36 h after the passage of A-68A. Maximal biomass growth rates at these ocean temperatures are 0.5–1.5 doublings per day38. Using changes in integrated chlorophyll over the top 100 m (Fig. 4c) as a proxy for growth rate yields growth rates at or below these levels, indicating that the localized high biomass could be due to the passage of A-68A and is not suggestive of large advection. Biological production can be enhanced in regions where marine and iceberg-derived nutrients are injected into nutrient-limited near-surface waters, with delays documented in the wake of iceberg passage14,15,16,18, potentially as a result of meltwater dilution and/or upper-ocean layer modifications. With strong near-surface stratification (Fig. 2) and freshwater surface run-off within 1 km of A-68A (Fig. 3), phytoplankton standing stocks could first be diluted by meltwater before stimulation of primary production and new growth occurs. The SG region is known for high heterogeneity in the timing, location and magnitude of phytoplankton blooms39. Therefore, it is not possible to unambiguously attribute the patterns of chlorophyll discussed to the presence of the A-68A iceberg. Moreover, the relation to iceberg aspect is complex owing to the route of the iceberg before measurement and the fragments and brash ice present in the area (Fig. 4a). Nevertheless, the glider depth mean flow is consistent with that of the geostrophic flow direction deduced from the satellite SSH (Fig. 4c), with flow from a predominantly ice clear region to the west for at least 7 days before measurement. This strongly suggests that the biological response is related to the iceberg passage. Figure 5a illustrates the climatological median of the cumulative buoyancy frequency across the WW layer for the South Atlantic region, obtained from historical hydrographic data from January to April (Methods). Historically, this region features relatively high WW stratification, thus recurrent iceberg-driven WW ventilation could be regionally important. a, The climatological median of the WW cumulative buoyancy frequency for January–April 2005–2021 (2015 and 2021 omitted, see text), overlain on bathymetry (GEBCO Compilation Group, 2023). The pink and blue box extents (areal extents given in Methods) illustrate the spatial and temporal overlap between the iceberg and hydrographic profiles. b–g, Conservative temperature (CT) (b and e), absolute salinity (SA) (c and f) and N2 (d and g) coloured accordingly for each month's profiles (February 2021, b–d and April 2015, e–g), with the climatology overlain in grey. The shading illustrates the s.d. Large icebergs transited the region during these months in years 2004, 2015 and 2021. Climatologies that spatiotemporally match these iceberg and/or fragment locations are available for years 2015 and 2021 (see the spatial extents in Fig. 5a), and are shown in Fig. 5b–g, respectively, overlain on the historical climatologies for these extents. The mean separation of iceberg to climatological data profiles was 69.5 ± 10.4 km (minimum 58 km) and 14.5 ± 11.8 km (minimum 1.9 km) for 2015 and 2021, respectively. Thus, 2015 separation is more reflective of the larger separations seen in Fig. 2 compared with that of 2021, and have been coloured accordingly. Appreciable similarities in historical iceberg proximal data to the high-resolution glider data are observed. Stratification maxima is elevated and shallower than the climatological median when icebergs are present (Fig. 5d,g). As the iceberg/measurement separation increases, the maxima deepen and surface waters, initially cooler and fresher than the climatology, become fully mixed. Elevation in stratification due to iceberg passage is also observed below the WW core, possibly due to basal meltwater influence. Through an unprecedented set of high-resolution measurements, the impact of a giant iceberg on the upper water column stratification and biogeochemistry within the ACC has been documented. The results demonstrate the following: Surface meltwater release induces a shallow peak in stratification, pushing warmer and saltier surface waters to greater depths. The WW stratification is eroded, with turbulent mixing transporting a consequential amount of warm and salty CDW from below the iceberg draft. This CDW, containing remineralized and preformed marine nutrients, in addition to nutrient-rich terrigenous material from the iceberg, is upwelled into shallower waters under the shoaled stratification maximum. Between 2.7 and 5.1 km from the iceberg, this cold and fresh surface run-off layer turbulently mixes and the WW profile below reforms, leaving a warmer, saltier surface layer and cooler fresher water below, with a shallower and stronger stratification peak compared with the climatological mean (Fig. 6). Within 2 km of the iceberg, surface chlorophyll is diminished while backscatter remains high. As separation increases, algal standing stocks increase. A schematic illustrating the buoyancy field approaching A-68A. The colour contours are a manual linear colour scale application for each vertical stratification layer and the horizontal profile/iceberg separation in T and S, which are overlain to produce the buoyancy colouring. The T and S line profiles are drawn to scale with 1 °C and 0.5 g kg−1 separation shown. The region of basal meltwater influence is represented with grey shading. These results have widespread implications. First, the SO is a major sink for anthropogenic carbon, regulating climate change by slowing the increase of carbon dioxide in the atmosphere40. A key control on the subduction of carbon is the strength of stratification at the base of the ML, with recent research41 suggesting that the global density contrast across this interface has increased in the past five decades. Iceberg melting and subsequent lateral mixing has been hypothesized to contribute to the WW structure42. This work provides observational evidence that giant icebergs increase stratification at the base of the ML in the SO. Second, the stratification changes around the WW layer, and associated turbulent mixing, provide an important mechanism for modifying WW properties outside of winter. Iceberg modelling studies in Greenlandic fjords43,44,45 support the view that meltwater release drives an overturning circulation, upwelling warmer waters. Here, we build upon this by observing directly the changes at intermediate depths in the SO. These changes will ultimately set the vertical stratification, temperature and salinity that persist in the WW layer into the following season. Third, our results underline the complex impacts of iceberg melt on marine productivity. Key controls include, but are not limited to, micronutrient delivery from the iceberg and upwelled CDW, spatial dilution impacts and changes in stratification from meltwater; shoaling the ML and conceivably enabling storm/front or euphotic zone interactions. The nature of resource limitation is pivotal, with algal population structure influencing cell-size distribution, ecosystem functioning and carbon export, all affecting the marine biosphere21,46. In the coming century, it is likely that the number of deep-drafted icebergs in the SO will increase, particularly in West Antarctica47,48. Since our fieldwork was conducted, the giant iceberg A-76A has transited the region49, and the similarly sized iceberg A-23 reached the northern end of the Weddell Sea, exiting into the Scotia Sea. Individual icebergs vary in draft, translation speed, distance and micronutrient loading. With considerable uncertainty remaining on future freshwater fluxes from icebergs, our study underlines the need to better observe and model the physical and biogeochemical processes. Only through understanding these processes will their impact on both physical and biological carbon pumps be accurately quantified and future estimates of carbon fluxes be refined and represented effectively in both regional and global ocean models. A-68A satellite images (NASA Worldview and Modified Copernicus Sentinel data 2021/Sentinel Hub) with a resolution of 250 m were manually delineated using QGIS software when cloud cover permitted. The centroid of the QGIS polygon shape files were obtained and utilized to estimate the speed and direction of the iceberg. The polygon edge points were then interrogated to obtain the glider separation from the closest point of the iceberg, with interpolation in time and space when no direct measurements were possible due to satellite overpass times or cloud cover. The Teledyne Webb Research Slocum G2 glider (serial number 405) incorporated a pumped Sea-Bird SBE 41 CTD, alongside optical measurements of chlorophyll-a and backscatter. T and S were bin averaged onto 1 dbar levels, the turning points and trapping events of the glider identified and the vertical profiles were obtained. No despiking was undertaken in these quality control steps as it was found that despiking routines erroneously identified outliers that were in fact the iceberg signal, particularly when close to the iceberg edge. To test for thermal lag effects, sequential upwards and downwards temperature profiles were compared using the SOCIB glider toolbox51. Owing to the CTD being pumped, thermal lag effects were very small and corrections were minimal. The corrected data were compared with high-quality CTDs obtained on RRS James Cook (JC211) at the start of the deployment. A small temperature (0.013 °C) and conductivity (0.0335 S m−1) offset was applied, with salinities recalculated. Finally geostrophic flow was estimated using the depth-averaged velocity estimation from each glider profile52. The historical vertical temperature and salinity fields of the region were calculated by applying a GEM projection following ref. 33, utilising data from ship-borne CTDs at the locations shown in Fig. 1. Optimal interpolation of the CTD data53 north of 58° S over the period 1995–2020 was used to produce vertical temperature and salinity fields at 5 dbar levels, as a function of integrated water column density (for example, dynamic height). The dynamic height was extracted relative to 990 dbar, with no seasonal correction for residuals to incorporate all surface variance. These time-invariant GEM fields produce T–S profiles for each dynamic height at each longitude. The 300 m reference level was extracted, then matched with glider positional referenced altimetry derived SSHs (EU Copernicus Marine Service information)54 for T–S comparisons. Ship-borne CTD data with no points between 21 m ≥ x ≤ 1,500 m depth range were excluded, leaving 113 casts for this analysis. CTD casts were collected from RRS James Cook using a Sea-Bird Scientific SBE911plus system, with additional sensors for dissolved oxygen, chlorophyll fluorescence, photosynthetically active radiation and other parameters. In the vicinity of the iceberg, most casts were to 1,000 m, or to 10 m above the seabed if shallower. Water samples were collected to calibrate the conductivity and dissolved oxygen, and for additional biological and biogeochemical parameters. Salinities were analysed with a Guildline Autosal 8400B, calibrated against IAPSO standard seawater batch P164. The CTD rosette was fitted with upwards- and downwards-looking Teledyne RDI Workhorse Monitor 300 kHz LADCPs. These were used to calculate current profiles for each cast using the inverse method55, using LDEO_IX software version 13 available from ref. 56. Three ship-borne CTD and LADCP profiles were used to calculate the Ri, the ratio of potential to kinetic energy, defined as where N2 is the Brunt–Väisälä frequency, or buoyancy frequency, and (dU/dz)2 is the velocity shear. The locations of these casts are illustrated in Fig. 1 as red stars filled in blue, two at 4.5 km ship to iceberg separation and one at 2 km separation. Turbulent entrainment of meltwater at the iceberg's base was calculated following ref. 11 by identifying warm, salty anomalies in T–S space compared with background levels, which are taken to be the profiles at greater glider–iceberg separation that exhibit a well-defined WW layer. For each cast, a Gade line57 is estimated, defined by where ΔT (°C) is the elevation of ambient temperature above the freezing point of water at salinity S (psu), L = 334 kJ kg−1 is the latent heat of fusion of water, and Cp = 4.2 × 103 J kg−1 °C−1 is the specific heat capacity of water. The slope of this meltwater mixing line is the mean Gade estimations over the top 300 m. It is placed at a tangent to the maximum temperature in the T–S intrusion, representing the upper bound of Gade water contribution. This linear approximation is then projected back to find the source depth, which represents the minimum T–S required for the basal melting to produce the observed anomaly, located in the permanent thermocline where there is a minimum in absolute difference of Gade T–S to the ambient T–S. The upper and lower bounds of the intrusion's deviation from the background T–S are noted for each cast, giving the upper and lower limits of the upwelled water. The relative contribution across density levels of ambient water and basal meltwater at each point along the intrusion in T–S space is calculated, that is, if a T–S point lies midway between the ambient water and the basal meltwater estimation, one deduces relative contribution of 50%. The basal meltwater concentration for each equivalent density level point is calculated, and the two of these are multiplied to produce a meltwater fraction at the T–S intrusion density level. The relative contributions are integrated over the vertical extent of the intrusion to obtain the amount of upwelled water content in the intrusion. The meltwater fraction at each T–S intrusion density level is integrated over the vertical extent of the intrusion to obtain the integrated meltwater content. Each cast contribution was then averaged to obtain the estimated mean volume of upwelled water and the mean meltwater content. All casts within 2.71 km of A-68A exhibited intrusions, with four casts on the approach to A-68A deep enough for this T–S analysis to be undertaken. Casts further than 5.5 km did not exhibit intrusions. To quantify a melt rate from the iceberg, a number of assumptions are required. These included a uniform melt rate around the circumference of A-68A, that the iceberg is flat bottomed without pockets of meltwater stored in crevasses beneath and that all melting emanates from the base. The ice density at the base of the iceberg is taken as 915 kg m−3 (ref. 58). Given the observation of intrusions up to 3 km from the edge of the berg, we assume this band is the ‘influence area'; integrating this and the mean meltwater content in the profiles yields an estimated basal meltwater contribution of 1.9 × 108 m3. Assuming only advection of the meltwater (and no diffusion), using limits of 0.13 m s−1 from the iceberg speed and 0.26 m s−1 from the glider depth mean flow speed, we obtain advection rates of 6.9 × 108 m3 day−1 and 1.4 × 109 m3 day−1, respectively. As noted above, satellite altimetry estimates of melt rate are between 1.25 and 2.49 times our in situ estimates. For the altimetry-based meltwater estimate, the measurements were extrapolated into summer and include the meltwater release from all children icebergs that calved from A-68A after 28 November 2020. The glider estimate, in contrast, refers to meltwater only around the remaining biggest piece in February 2021. While altimetry detects thickness change and therefore meltwater when it is created, oceanographic methods detect meltwater when it is released and distributed into the water column. Moreover, for the glider, the antecedent differential meltwater due to wake influence, leading edge melt, stratification depth influence, meltwater ‘shading' (where meltwater pools on the downstream side of an iceberg) and the glider being trapped and released from the shallower side of the once grounded glacier will affect the assumption of uniform melt rate around the circumference of A-68A. Manufacturer calibrations were used to derive bio-optical properties. The volume scattering function (in m−1 sr−1) data were filtered according to ref. 59. Values of volume scattering function above 0.001 m−1sr−1, negative values and profiles of anomalously low-volume scattering function (maximum value of 0.0001 m−1 sr−1 or less) were removed from the dataset. The volume scattering function values were smoothed using a five-point median and seven-point mean filter. The optical particle backscattering (bbp) was calculated by correcting for scattering within a seawater medium (assuming an angle of 124° and for the wavelength of 650 nm) and integrating across all backwards angles using an assumed angular dependency for marine particles60,61. The chlorophyll data from each profile were despiked (to remove negative values and outliers above 10 mg m−3), before being dark-corrected by subtracting the median chlorophyll below 300 m from each value62. The chlorophyll data were corrected for quenching from all daylight profiles (local sunrise to sunset plus 2.5 h) based on published methods63. Briefly, quenching was assumed to occur above the depth of the maximum chlorophyll-a:bbp ratio within the ML, and was corrected for at each depth above that by multiplying the maximum chlorophyll-a:bbp with the corresponding bbp value, assuming that the algal population involved has a constant chlorophyll-a:bbp ratio. Hydrographic profiles south of 40° S for the period 2004 to 2021 (ref. 64) were compiled from the combined datasets, Argo floats65, tagged marine seals66, ship-based CTD casts and glider profiles67. We detected the presence of WW in each hydrographic profile following the definition in ref. 68, where a temperature inversion of less than 2 °C lies below the ML, and WW bounds are defined as the position of the maximum temperature gradients above and below the temperature minima. We computed the cumulative buoyancy frequency of WW as the sum of \({N}^{2}=-\frac{g}{\rho }\frac{\updelta \rho }{\updelta z}\) across the WW layer, which is proportional to the PE of the water column, and grid onto 0.5° × 0.5° median climatologies for the months of January, February, March and April. Subsequently, data from the years 2004, 2015 and 2021 were extracted from the dataset, since they sample years of known large iceberg proximity9,27,48,49,69,70. The box extents of large icebergs and/or fragments in this region are defined as 34–37° W, 55.5–57.5° S for February 2021 (3° × 2° coverage due to extreme fragmentation and distribution of A-68's constituents49) and 34.5–35.5° W, 53–54° S for April 2015 (1° × 1° coverage coinciding with iceberg B-17a)69. No data coincided with the iceberg pathway in 2004. In these box extents, hydrographic profiles comprised 16 ship-based profiles during February 2021 and 2 Argo float profiles during April 2015. The climatological data for the same regions contained 70 profiles (constituting 27 Argo profiles, 16 ship-based CTDs and 27 Marine Mammals Exploring the Oceans Pole to Pole profiles) for 2021 and 7 Argo profiles for April 2015. Satellite images are available from the NASA Worldview and Modified Copernicus Sentinel data 2021/Sentinel Hub via the NASA Worldview application at https://worldview.earthdata.nasa.gov/, part of the NASA Earth Observing System Data and Information System (EOSDIS). 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Further funding for sampling around iceberg A-68 was provided by the Government of South Georgia and the South Sandwich Islands and the UK Government Blue Belt Programme. Time dedicated to analysis and presentation of this work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 821001 (SOCHIC, 10.3030/821001), and a Wallenberg Academy Fellowship (WAF 2015.0186) and the Swedish Research Council (VR 2019-04400) grant. We thank the officers and crew of RRS James Cook and the scientists and technicians from the National Oceanography Centre for their invaluable assistance with conducting the cruise and collecting these data. We thank the officers and crew of MV Pharos SG for assistance recovering the glider. We thank G. Stephenson for his assistance applying the T–S intrusion analysis. We acknowledge the use of imagery from the NASA Worldview application (https://worldview.earthdata.nasa.gov/), part of the NASA Earth Observing System Data and Information System (EOSDIS). Finally, we thank the reviewers for their invaluable contributions to help strengthen this paper. British Antarctic Survey, Cambridge, UK Natasha S. Lucas, J. Alexander Brearley, Katharine R. Hendry, E. Povl Abrahamsen, Michael P. Meredith & Geraint A. Tarling Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden Theo Spira The Arctic University of Norway (UiT), Tromso, Norway Anne Braakmann-Folgmann You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar N.S.L.: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization and writing—original draft preparation. J.A.B.: conceptualization, formal analysis, investigation, resources, supervision, validation and writing—review and editing. K.R.H.: formal analysis, investigation, validation and writing—review and editing. T.S.: data curation, investigation, methodology and software. A.B.-F.: formal analysis and validation. E.P.A.: investigation, data curation, formal analysis and investigation. M.P.M.: conceptualization, investigation, project administration, resources, supervision, validation and writing—review and editing. G.A.T.: conceptualization, investigation, funding acquisition, project administration, resources, supervision, validation and writing—review and editing. Correspondence to Natasha S. Lucas. The authors declare no competing interests. Nature Geoscience thanks Mattias Cape and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Lucas, N.S., Brearley, J.A., Hendry, K.R. et al. Giant iceberg meltwater increases upper-ocean stratification and vertical mixing. Nat. Geosci. (2025). https://doi.org/10.1038/s41561-025-01659-7 Download citation Received: 03 June 2024 Accepted: 06 February 2025 Published: 04 April 2025 DOI: https://doi.org/10.1038/s41561-025-01659-7 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Advertisement Nature Geoscience (Nat. Geosci.) ISSN 1752-0908 (online) ISSN 1752-0894 (print) © 2025 Springer Nature Limited Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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However, the country is exceptionally well placed to change course, expand research data resources and adopt unique designs that integrate research and healthcare. Here, we present Precision Omics Initiative Sweden (PROMISE)1 and its three pillars: to generate extensive new large-scale multi-omics data with integration of selected existing research, registry and healthcare data; to maximize research use of ‘-omics' data created in healthcare; and to develop an improved data-access framework. This is a preview of subscription content, access via your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $29.99 / 30 days cancel any time Subscribe to this journal Receive 12 print issues and online access $209.00 per year only $17.42 per issue Buy this article Prices may be subject to local taxes which are calculated during checkout Kämpe, A. et al. Preprint at SSRN https://doi.org/10.2139/ssrn.4992987 (2024). Uhlén, M. et al. Science 347, 1260419 (2015). PubMed Google Scholar Government Offices of Sweden. Regerinskansliet https://go.nature.com/4hrlIaV (7 November 2024). Fioretos, T. et al. Nat. Med. 28, 1980–1982 (2022). CAS PubMed Google Scholar Bergström, G. et al. J. Intern. Med. 278, 645–659 (2015). PubMed PubMed Central Google Scholar Späth, F. et al. Int. J. Epidemiol. 16, dyaf004 (2024). Google Scholar Brueffer, C. et al. EMBO Mol. Med. 12, e12118 (2020). CAS PubMed PubMed Central Google Scholar Nunes, L. et al. Nature 633, 137–146 (2024). CAS PubMed PubMed Central Google Scholar Tesi, B. et al. Lancet Reg. Health Eur. 39, 100881 (2024). PubMed PubMed Central Google Scholar Stranneheim, H. et al. Genome Med. 13, 40 (2021). PubMed PubMed Central Google Scholar Fröbert, O. et al. N. Engl. J. Med. 369, 1587–1597 (2013). PubMed Google Scholar Download references The authors thank Barncancerfonden, J. Vallon-Christersson, M. Friedman, U. Landegren and others in the precision medicine and multi-omics community in Sweden and abroad for invaluable discussions that have contributed to the ideas formulated here. The authors receive research and infrastructure funding from the Swedish Research Council, the Swedish Society for Medical Research, the Knut and Alice Wallenberg Foundation, Vinnova, Forskningsrådet i Sydöstra Sverige, Region Östergötland, the Swedish Cancer Society, the Swedish Brain Foundation, the Swedish Heart Lung Foundation, the Swedish Childhood Cancer Fund, Region Skåne, the European Research Council, Region Stockholm, the Sjöberg Foundation, Radiumhemmets forskningsfonder, SciLifeLab, National Institutes of Health, the Erling Persson Foundation, Uppsala University, the Mrs. Berta Kamprad Foundation and the Göran Gustafsson Foundation. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden Anders Kämpe, Maria Johansson Soller, Ann Nordgren, Fulya Taylan, Anna Wedell, Richard Rosenquist & Anna Lindstrand Department of Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden Anders Kämpe, Ann Nordgren, Fulya Taylan, Valtteri Wirta, Richard Rosenquist & Anna Lindstrand Department of Gene Technology, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden Sanna Gudmundsson, Valtteri Wirta & Tuuli Lappalainen Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA Sanna Gudmundsson Clinical Genomics, SciLifeLab, Linköping, Sweden Colum P. Walsh Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden Colum P. Walsh Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden Kerstin Lindblad-Toh SciLifeLab, Uppsala University, Uppsala, Sweden Kerstin Lindblad-Toh, Åsa Johansson, Adam Ameur, Tove Fall, Jessica Nordlund, Mia Wadelius, Päivi Östling & Tobias Sjöblom Broad Institute of MIT and Harvard, Cambridge, MA, USA Kerstin Lindblad-Toh Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden Åsa Johansson, Anna Clareborn, Adam Ameur, Daniel Eriksson, Ulf Gyllensten & Tobias Sjöblom Department of Clinical Genetics, Pathology and Molecular Diagnostics, Skåne University Hospital, Lund, Sweden Anders Edsjö, Thoas Fioretos & Hans Ehrencrona Division of Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Anders Edsjö Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden Thoas Fioretos & Hans Ehrencrona Clinical Genomics, SciLifeLab, Lund, Sweden Thoas Fioretos Department of Medical Sciences, Uppsala University, Uppsala, Sweden Tove Fall, Jessica Nordlund, Johan Sundström & Mia Wadelius Department of Clinical Sciences, Lund University, Helsingborg, Sweden Paul W. Franks Precision Health University Research Institute, Queen Mary University of London, London, UK Paul W. Franks The Swedish Network Against Cancer, Stockholm, Sweden Margareta Haag Department of Cell and Molecular Biology, NBIS, SciLifeLab, Uppsala University, Uppsala, Sweden Anna Hagwall & Bengt Persson Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden Janne Lehtiö & Päivi Östling Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden Yi Lu & Patrik K. E. Magnusson Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden Erik Melén Department of Diagnostics and Intervention, Oncology, Umeå University, Umeå, Sweden Beatrice Melin & Bethany Van Guelpen Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden Karl Michaëlsson Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden Ann Nordgren & Per Sikora Department of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden Ann Nordgren Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden Lao H. Saal Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden Jochen M. Schwenk Bioinformatics and Data Centre, University of Gothenburg, Gothenburg, Sweden Per Sikora The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia Johan Sundström Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden Bethany Van Guelpen Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden Anna Wedell Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden Valtteri Wirta Department of Obstetrics and Gynecology, University of Gothenburg, Gothenburg, Sweden Bo Jacobsson Department of Obstetrics and Gynecology, Sahlgrenska University Hospital, Western Health Care Region, Gothenburg, Sweden Bo Jacobsson Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway Bo Jacobsson New York Genome Center, New York, NY, USA Tuuli Lappalainen You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar Correspondence to Richard Rosenquist, Anna Lindstrand or Tuuli Lappalainen. T.F. is co-founder, board member and scientific advisor to Qlucore and Lead Biologics and co-founder and scientific advisor to Cantargia. P.F. is a paid member of advisory boards for Novo Nordisk, Sidra Health, Zoe and ABC Labs; has received research funding (paid to institution) from numerous pharmaceutical companies as part of the Innovative Medicines Initiative of the European Union; and is co-chair of the Precision Medicine in Diabetes Initiative and the US National Institute of Diabetes and Digestive and Kidney Diseases Working Group of Council on Heterogeneity in Diabetes. L.S. is co-founder and shareholder of SAGA Diagnostics and advisor to DoMore Diagnostics and has received honoraria from AstraZeneca. J.S. has conducted contract research (paid to institution) for Capitainer and Luminex and has received speaker/travel fees from Roche Diagnostics, Olink and Luminex. E.M. has received advisory board or lecture honoraria from ALK, AstraZeneca, Chiesi and Sanofi. J.S. has direct or indirect stock ownership in companies (Anagram kommunikation, Sence Research, Symptoms Europe and MinForskning) providing services to companies and authorities in the health sector, including Amgen, AstraZeneca, Bayer, Boehringer, Eli Lilly, Gilead, GSK, Göteborg University, Itrim, Ipsen, Janssen, Karolinska Institutet, LIF, Linköping University, Novo Nordisk, Parexel, Pfizer, Region Stockholm, Region Uppsala, Sanofi, STRAMA, Takeda, TLV, Uppsala University, Vifor Pharma and WeMind. B.V.G. has received speaker honoraria from AstraZeneca. T.S. is co-founder, shareholder and board member of Oncodia. R.R. has received honoraria from AbbVie, AstraZeneca, Illumina, Janssen, Lilly and Roche. A.L. has received speaker honoraria from Illumina and Pacific Biosciences. T.L. is a scientific advisor to and has equity in Variant Bio and has received speaker honoraria from Abbvie and Merck. Reprints and permissions Kämpe, A., Gudmundsson, S., Walsh, C.P. et al. Precision Omics Initiative Sweden (PROMISE) will integrate research with healthcare. Nat Med (2025). https://doi.org/10.1038/s41591-025-03631-9 Download citation Published: 04 April 2025 DOI: https://doi.org/10.1038/s41591-025-03631-9 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Advertisement Nature Medicine (Nat Med) ISSN 1546-170X (online) ISSN 1078-8956 (print) © 2025 Springer Nature Limited Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Astronomy (2025)Cite this article Metrics details Double white dwarf binaries are a leading explanation of the origin of type Ia supernovae, but no system exceeding the Chandrasekhar mass limit (1.4 M⊙) has been found that will explode anywhere close to a Hubble time. Here we present the super-Chandrasekhar mass double white dwarf WDJ181058.67+311940.94 whose merger time (22.6 ± 1.0 Gyr) is of the same order as a Hubble time. The mass of the binary is large, combining to 1.555 ± 0.044 M⊙, while being located only 49 pc away. We predict that the binary will explode dynamically by means of a double detonation that will destroy both stars just before they merge, appearing as a subluminous type Ia supernova with a peak apparent magnitude of about mV = −16 (200,000 times brighter than Jupiter). The observationally derived birth rate of super-Chandrasekhar mass double white dwarfs is now at least 6.0 × 10−4 yr−1 and the observed rate of type Ia supernovae in the Milky Way from such systems is approximately 4.4 × 10−5 yr−1, whereas the predicted type Ia supernova rate in the Milky Way from all progenitor channels is about sixty times larger. Hence, WDJ181058.67+311940.94 mitigates the observed deficit of massive double white dwarfs witnessed in volume-complete populations, but further evidence is required to determine the majority progenitors of type Ia supernovae. Binaries comprising at least one white dwarf are the progenitors of type Ia supernovae1,2. Type Ia supernovae show an absence of hydrogen in their spectra and are caused by the thermonuclear explosion of a carbon–oxygen white dwarf. Nuclear fusion transforms a substantial amount of, or the entire, white dwarf into heavier elements and ejects them into the interstellar medium. However, the stellar type of the companion to the white dwarf in type Ia progenitors remains largely unclear (see, for example, refs. 3,4,5). The substantial population size of double white dwarf binaries has naturally led to them being one of the leading progenitor candidates to explain the abundance of type Ia supernovae6,7. These systems form on compact orbits with an orbital period on the timescale of hours to days8 (orbital separations of hundredths to tenths of astronomical units) following a series of mass transfer events9. The gradual loss of orbital angular momentum through gravitational wave radiation draws the two stars closer until the orbital period of massive double white dwarfs is a couple of minutes, initiating unstable mass transfer and leading to the demise of the system10. Although many compact double white dwarfs have been discovered on the brink of coalescence (see, for example, refs. 11,12,13), we have had no direct evidence that these systems exist in nearby, volume-complete populations14,15,16, which casts doubt on whether double white dwarfs can account for a large percentage of the observed type Ia supernova rates. Current synthetic models of the population indicate that super-Chandrasekhar mass limit double white dwarfs are indeed suspected to be scarce17,18,19. However, based on the models of ref. 17 we expect about 150 compact double white dwarf binaries to have total masses that exceed 1.5 M⊙ within 100 pc, about one quarter of which merge in under a Hubble time. There has been only one super-Chandrasekhar mass double white dwarf binary discovered (NLTT 12758) (ref. 20), but its 1.15 d period means that the two stars will come into contact in about 10 Hubble times. There are a handful of other candidate subluminous type Ia progenitors that are double white dwarfs that have total masses smaller than the Chandrasekhar mass limit (see, for example, refs. 21,22,23,24,25,26), two white dwarf+hot subdwarf systems that exceed 1.4 M⊙ and have an impending supernova fate27,28, and one other white dwarf+hot subdwarf that is also a strong candidate29,30. Growing observational evidence supports hot subdwarfs as some of the products of binary evolution31, but, although more super-Chandrasekhar mass systems have been discovered, the binaries much less densely populate the Galaxy32. The observed rate of type Ia supernovae initiated from the white dwarf+hot subdwarf channel is expected to be at least (1.5–7) × 10−5 yr−1 (ref. 27), whereas the rate of type Ia supernovae in the Galaxy from all progenitors is about 2.8 ± 0.6 × 10−3 yr−1 (refs. 4,19,33,34) as inferred through observations of explosions in other galaxies of similar redshift. Multiple other evolutionary scenarios have been suggested as causes for normal and peculiar type Ia supernovae4 having different companion compositions, but the extent to which they contribute towards the missing fraction of type Ia supernovae is unclear. This ambiguity on the nature of type Ia progenitors is cosmologically problematic. A primary reason is that, until we confirm the leading progenitors of a type Ia, supernova, systematic errors in the distances derived to other galaxies could lead to inaccurate measurements, which is particularly troublesome for galaxies at high redshifts34,35. In addition, the details of the ejecta velocity and its constituents are important for star formation36 and the dynamics of gas in galaxies37. Not only does the discovery of a local, compact, super-Chandrasekhar mass double white dwarf have the ability to resolve the dearth of systems in the observed sample, but a sample of such systems has the power to reduce the uncertainty of this cosmologically fundamental event. WDJ181058.67+311940.94 was first discovered as part of the DBL survey26 which searches for double-lined double white dwarfs using medium-resolution spectra (R ≈ 8,000–9,000). Fits to these identification spectra indicated the source to be a double white dwarf binary with a high total mass. Afterwards, we launched an observational campaign to acquire time-series spectroscopy of the source to confirm the masses derived through the atmospheric parameters and resolve the orbital period. We obtained phase-resolved radial velocities of WDJ181058.67+311940.94 with the following instruments and telescopes: the Intermediate-dispersion Spectrograph and Imaging System (ISIS) on the 4.2 m William Herschel Telescope (WHT); the Intermediate Dispersion Spectrograph (IDS) on the 2.5 m Isaac Newton Telescope; the Fibre-fed Echelle Spectrograph (FIES) and Alhambra Faint Object Spectrograph and Camera (ALFOSC) on the 2.56 m Nordic Optical Telescope (NOT); and a continuous observing window of 4.5 h using the UV-Visual Echelle Spectrograph (UVES) on the 8.2 m Very Large Telescope (VLT). The UVES data was used for an improved accuracy of the atmospherically derived masses from spectral fits because of its full visible coverage. Precise radial velocity measurements of the target were simultaneously obtained and with this an unambiguous determination of the orbital period. As a further test for consistency of the atmospheric solution with a unique dataset, we also fitted a two-star solution to a previously published Hubble Space Telescope (HST) Cosmic Origins Spectrograph (COS) spectrum38. The resultant stellar parameters found by fitting each dataset are quoted in Table 1 and spectral fits to the optical and ultraviolet data are plotted in Fig. 1. Considering the measurements from all datasets, we find stellar parameters of \({T}_{{\rm{eff}},1}=17,26{0}_{-880}^{+1380}\) K, log g1\(=8.35{0}_{-0.052}^{+0.066}\) dex, M1 = 0.834 ± 0.039 M⊙ for the primary (more massive) star and Teff,2 = \(20,00{0}_{-2000}^{+400}\) K, log g2 =\(8.16{4}_{-0.030}^{+0.027}\) dex, M2 = 0.721 ± 0.020 M⊙ for the secondary (less massive) star, leading to a total system mass of MT = 1.555 ± 0.044 M⊙. Teff, log g and M refer to the effective temperature, the surface gravity and the mass of each component, respectively. Left, the HST/COS ultraviolet spectrum with the synthetic spectrum from the hybrid HST/COS with Pan-STARRS photometry fit for a two-star model overlaid in red. The corresponding atmospheric parameters of the DA white dwarfs are Teff,1 = 18,630 K, log g1 = 8.307 dex, Teff,2 = 18,010 K, log g2 = 8.178 dex. Middle, a single UVES spectrum from Hα to H8 with the synthetic spectral model for atmospheric parameters Teff,1 = 17,230 K, log g1 = 8.408 dex, Teff,2 = 20,190 K, log g2 = 8.151 dex, overplotted in red. We remind the reader that all Balmer lines up to H11 were fitted but are omitted from the plot for clarity. Right, the observed fluxes in Pan-STARRS (black circles) and the synthetic photometry in each filter for the same atmospheric parameters (orange crosses). The percentage flux residual between the data and the combined flux is found below. The fluxes contributed from the more massive (red) and less massive (green) stars are included for the ultraviolet (dashed) and optical (solid line) fits. The Gaia parallax measurement with a Gaussian prior was an independent variable to scale the observations from an Eddington to an absolute flux perceived in the Solar System. All other data was used exclusively for radial velocity measurements at Hα to precisely quantify the motion of the stars across all orbital phases and to improve the precision of the period. A Lomb–Scargle periodogram of all radial velocity measurements, which was optimized for physical limits of the system, revealed one clear peak representing the orbital period. The binary parameters are quoted in Table 2 and our phase-folded radial velocity (RV) curve with the best-fit orbital solution is depicted in Fig. 2. The best-fitting orbital parameters were orbital period P = 14.23557 ± 0.00002 h, semi-amplitudes K1 = 93.9 ± 2.0 km s−1 and K2 = 95.7 ± 2.1 km s−1, and velocity offsets γ1 = 50.0 ± 1.5 km s−1 and γ2 = 53.5 ± 1.6 km s−1. Being double lined, the mass ratio was independently solvable without knowledge of the orbital inclination using q = M2/M1 = K1/K2, such that the orbitally derived value was q = 0.98 ± 0.03. This result is in best agreement with the star masses derived from the HST/COS spectrum, which was q = 0.90 ± 0.02. Our derived masses from the VLT/UVES spectra yielded a lower q = 0.82 ± 0.02, with the mass of the less massive star being near identical to the ultraviolet, and the adopted value, taking into account all measurements, indicated a mass ratio of q = 0.86 ± 0.04. The surface gravity of the hotter, less massive star was nearly identical across fits to all datasets. This is unsurprising given that it contributes more flux than the cooler white dwarf, whereas its temperature difference between the ultraviolet and optical datasets primarily arises from the fitting of the slope of the spectral energy distribution across the ultraviolet. Forcing the orbitally derived q = 0.98 ± 0.03 in the atmospheric fit led to an increase in the surface gravity of the secondary and a decrease in the surface gravity of the primary, to fit the broadness of the Balmer line profiles well. The secondary would thus be more massive, and the primary less massive, and as such, including a mass ratio of approximately one, all evidence points towards WDJ181058.67+311940.94 being a super-Chandrasekhar mass double white dwarf. The hotter star (black) and the cooler star (red). The RV curves are plotted showing the velocity of the two stars across a full orbit, binned into 80 evenly spaced phase bins. In faded colours and with crossed markers are the RVs that were masked in searching for an orbital solution, which are also listed in Supplementary Table 1. One-sigma error bars are given as the standard deviation of 1,000 bootstrapping iterations. The critical time at which the two stars reach closest approach can be calculated using39 where a0 is the semimajor axis of the binary at the present day and, for WDJ181058.67+311940.94, a0 = 0.01601 ± 0.00015 A.U. This indicates that the stars will come into contact in 22.6 ± 1.0 Gyr, whereas the less massive component will begin Roche lobe overflow and initiate mass transfer approximately 100 yr before the demise of binary. To understand the fate of the binary system, we simulated its interaction when it was just about to merge using the star masses obtained through spectral fits to the VLT/UVES data. Videos of this simulation are presented in the Supplementary Information (Supplementary Videos 1 and 2). The spectral fits indicate that carbon–oxygen cores are appropriate for both white dwarfs, so we consider it highly unlikely that an accretion induced collapse will occur. This would require the more massive white dwarf to have a mass higher than 1.2 M⊙ with an oxygen–neon core to avoid dynamical ignition before or during the merger. We used the moving-mesh code AREPO40,41,42 in a similar set-up to previous work43. The stars were given realistic composition profiles and placed in corotation before applying an accelerated inspiral term that removed angular momentum in the same way as gravitational waves. We switched on a live nuclear reaction network with 55 isotopes43,44 at an orbital period of 39 s as the temperature of the accretion stream at the impact spot approached that required for a thermonuclear runaway. We show an overview of the dynamic evolution of the binary system in Fig. 3. The interaction of the accretion stream with the surface of the primary white dwarf ignites a helium detonation close to the point of interaction (second column of Fig. 3). The helium detonation then wraps around the primary white dwarf and sends a shock wave into its core that converges at a single point. This ignites a second detonation that completely destroys the primary white dwarf. When the shock wave of its explosion hits the secondary white dwarf, the double detonation mechanism repeats itself. The shock wave from the detonation of the primary ignites a helium detonation near the surface of the secondary which drives a shock wave into its core. It is sufficient to ignite the core detonation, destroying the secondary white dwarf as well. The first column shows the time when we stop the accelerated inspiral and continue to evolve the binary system self-consistently. The second and third columns show the time when the helium detonation ignites on the surface of the primary white dwarf, and the time when the shock wave that is driven into the core of the primary by the helium detonation converges in a single point. The fourth column shows the same shock convergence in the core of the secondary white dwarf. The top row shows slices of density in the plane of rotation and the three below are zoomed insets at the point of interest. Top to bottom, density, temperature and kinetic energy density. The shock convergence points in both white dwarfs occur at densities high enough to be very likely to ignite a carbon detonation and destroy the white dwarf. There is no bound remnant and the ejecta of the explosion contain the total mass of the initial binary, having a total explosion energy of 1.2 × 1051 erg. We show the structure and composition of the ejecta in Fig. 4. The outermost layers of ejecta are the ashes of the helium detonation of the primary white dwarf. They consist mostly of intermediate mass elements, dominated by silicon, sulfur and argon. Below them sit the ashes of the carbon–oxygen core of the primary white dwarf. Again they consist mostly of intermediate mass elements, but also contain 0.13 M⊙ of iron group elements, in particular, 0.10 M⊙ of radioactive 56Ni that will power the light curve. The resulting supernova has a maximum brightness in the B band of MB = −16.4 (mB = −14.7) and a maximum brightness in the V band of MV = −17.8 (mV = −16.1), and is most likely to appear as a subluminous type Ia supernova. Slices of density (left column) and mean atomic weight (right column) of the supernova ejecta in homologous expansion for a time (texp) 100 s after ignition of the first helium detonation. The top row shows slices in the original plane of rotation and the bottom row shows slices perpendicular to it. The outer layers are close to spherically symmetrical, although significant deviations from spherical symmetry exist in the plane of rotation. The iron group elements (including 56Ni) are essentially all produced in the explosion of the primary white dwarf and form a half-sphere around the ejecta of the secondary white dwarf. We can use WDJ181058.67+311940.94 to observationally predict the number of super-Chandrasekhar mass double white dwarfs in the Milky Way. We start by assuming that WDJ181058.67+311940.94 and NLTT 12758 are the only two within 49 pc and make the rudimentary assumption that double white dwarfs are evenly scattered around the Milky Way having a cylindrical disk with radius Rmax = 15 kpc and scale height hz = 300 pc. The white dwarf birth rate is estimated to be approximately 1.4 × 10−12 pc−3 yr−1 (ref. 45). There are 1,076 white dwarfs within the volume-complete 40 pc Gaia sample16 and extrapolated to 49 pc we would have 1,978 white dwarfs. This means that the birth rate of super-Chandrasekhar mass double white dwarfs in the Galaxy becomes greater than approximately 6.0 × 10−4 yr−1. Moreover, we can also calculate an observed rate of type Ia supernovae arising from super-Chandrasekhar mass double white dwarfs using WDJ181058.67+311940.94 (Tc = 22.6 ± 1.0 Gyr) and NLTT 12758 (Tc = 139 ± 9 Gyr). The frequency of the two events combined imply a supernova rate of about once every 19 Gyr within 49 pc or (1.04 ± 0.04) × 10−16 yr−1 pc−3. When fully extrapolated with the cylindrical disk approximation, the observed rate of type Ia supernovae from super-Chandrasekhar mass double white dwarfs in the Milky Way hence becomes at least (4.4 ± 0.2) × 10−5 yr−1, although the quoted uncertainty does not account for uncertainties on the Galactic model. This result serves as a minimum based on the 49 pc population as it remains possible that other systems exist within the same radius. Evidently, the magnitude of super-Chandrasekhar mass systems approaches the (2.8 ± 0.6) × 10−3 yr−1 rate predicted for all evolutionary channels leading to a type Ia (refs. 4,33,34), but we must recall that these two systems are set to come together in more than a Hubble time and consider that the present observed supernova rate from these systems is about sixty times smaller. As such, the rates from Milky Way progenitors through the hot subdwarf binary channel and the double white dwarf channel are about the same, together accounting for about 3% of the Galactic rate. Synthetic populations suspect that around 60% of the Galactic birth rate of type Ia progenitors comes from the double degenerate channel46,47, as is the case for WDJ181058.67+311940.94. The large missing fraction is especially mysterious given the high completion rate of the 40 pc sample of white dwarfs16. Contribution to the double white dwarf type Ia rate from sub-Chandrasekhar mass limits detonation could at least be a partial solution to make up for the deficit, where a mass–period distribution of double white dwarfs in a volume/magnitude limited sample serves as a means to put this to the test26. To date, there have been no sub-Chandrasekhar mass type Ia progenitor candidates inside a 50 pc radius, so ongoing efforts are crucial to properly quantify the number of massive double white dwarf binaries in our local neighbourhood and the Milky Way. We have presented a compact, super-Chandrasekhar mass double white dwarf binary which will merge in close to a Hubble time, having an orbital period of 14.24 h. With a total mass of 1.555 ± 0.044 M⊙, WDJ181058.67+311940.94 is the most massive double white dwarf binary confirmed to date. We predict it to explode as a quadruple detonation and be destroyed completely. With all the mass ejected and a total explosion energy of 1.2 × 1051 erg, but only 0.1 M⊙ of 56Ni in the ejecta, it will appear as a subluminous type Ia supernova with a peak apparent magnitude of approximately mB = −14.7 and mV = −16.1. The lack of observational evidence of compact and massive double white dwarf binaries has long troubled the theory that double white dwarfs are the dominating evolutionary channel of type Ia detonations3. WDJ181058.67+311940.94 provides tentative observational evidence that super-Chandrasekhar mass systems with short merger times do exist in the Milky Way, and when combined with the close proximity of 49 pc the rate of super-Chandrasekhar mass double white dwarfs born in the Milky Way is at least 6.0 × 10−4 yr−1. This draws closer the disparity between the observed and predicted birth rates of super-Chandrasekhar mass systems, although the observed rate is still approximately two times smaller. However, there remains a large deficit in the rate of type Ia supernovae from the progenitor systems. A small fraction of the Milky Way rate is accounted for, now with an equal contribution from double white dwarf and white dwarf+hot subdwarf binaries. Being discovered through a medium-resolution search of overluminous double white dwarfs26, which up to a magnitude limit of G < 17 mag is approximately 20% complete, it is entirely plausible that more super-Chandrasekhar mass double white dwarfs reside in our Galactic neighbourhood and that we have the spectroscopic ability to resolve the formation channel of type Ia supernovae. Deeper completeness through photometric and spectroscopic surveys in the coming years, as well the inauguration of space-based gravitational wave detectors in the next decade, will be pivotal in detecting ultracompact binaries on the cusp of detonation48,49. Combined efforts surveying type Ia progenitors across the full range of orbital periods will be the ultimate means to accurately quantify the contribution of double white dwarfs to type Ia supernovae. WDJ181058.67+311940.94 was first discovered as part of the DBL survey26 using medium-resolution spectra (R = 8,800) on the 4.2 m WHT with the ISIS. Two other ISIS exposures were taken on the nights 13 and 14 April 2019 using the R600B and R1200R gratings with a 1.2″ slit resulting in a spectral resolution of R = 3,000 at Hα and these spectra are included in the full orbital analysis of the double white dwarf. The blue and red set-ups had a wavelength calibration accuracy of approximately 3 km s−1 and 2 km s−1, respectively. We conducted a continued observational campaign to derive phase-resolved RVs of the double white dwarf binary. We utilized the 2.5 m Isaac Newton Telescope (INT) with the IDS over the nights 4–7 September 2019 (11 exposures, 1,800 s each) and 24 September 2019 (4 exposures, 900 s each) with the Red+2 detector and a 1. 2″ slit width, resulting in a spectral resolution of R = 6,300. Further phase-resolved spectra were taken with the INT on the nights 25 and 26 August 2024 with the H1800V grating at a resolution of R = 9,400 (20 exposures, 1,500 s each). An arc lamp exposure was taken every 45 min of observing time and the science images were wavelength calibrated by interpolation of the nearest two arcs. The wavelength calibration accuracy per frame was approximately 2 km s−1. Bias, flat field and spectrophotometric flux standard star images were taken on all nights and applied in the reduction. All data from the WHT and the INT were reduced using the MOLLY suite50 using an optimal extraction algorithm51. These data were supplemented with 18 exposures of length 1,500 s on the 2.56 m NOT using the FIES in low-resolution mode (R = 25,000), having a wavelength calibration accuracy of approximately ±150 m s−1. Observations were obtained through a staff queue at random times, typically being two consecutive exposures, and through a NOT fast-track proposal. All FIES data were reduced using its automated data reduction pipeline, FIEStool52. We also obtained five exposures with the NOT ALFOSC with a 0.5″ slit width, producing spectra at R = 10,000 with wavelength range 6,330–6,870 Å on 1 and 2 June 2024. The data were reduced with the PYPEIT Python package53. A continuous observing window of 4.5 h was obtained through directors discretionary time on the 8.2 m VLT with the UVES. Each exposure lasted for 730 s with a readout time between exposures of 45 s, totalling 20 exposures. We employed an observing set-up of the dichroic 1 mode with central wavelengths of 3,900 Å and 5,640 Å for the blue and red arms, giving a wavelength range that covered the full visible spectrum apart from gaps of 80 Å at 4,580 Å and 5,640 Å. A slit width of 1.0” and a 2 × 2 binning granted a spectral resolution R = 20,000 and the wavelength calibration accuracy was approximately 200 m s−1 (refs. 54,55). In deriving final RV errors for these data (Supplementary Table 1), the wavelength calibration error was added in quadrature to the statistical error. We used the package WD-BASS56 to fit atmospheric parameters to the spectra from VLT/UVES. For synthetic spectra, we utilized the 3D-NLTE model grid introduced in ref. 26, which was constructed using the 3D-LTE models of ref. 57 with a further NLTE correction factor applied using the NLTE and LTE synthetic spectra described in ref. 58. The two stars were scaled using temperature–log g–radius relationships with the evolutionary track models of ref. 59 when M ≤ 0.393 M⊙, of ref. 60 when 0.393 < M < 0.45 M⊙, and the hydrogen-rich envelope evolutionary sequences of ref. 61 otherwise. These boundaries come from the expectation that a white dwarf with a mass below 0.45 M⊙ has a helium core and those larger have a carbon–oxygen core. The model spectra were converted from an Eddington flux to that observed at Earth and reddened with an extinction coefficient A(V) = 0.0312 mag (ref. 62) and colour excess E(B − V) = A(V)/3.1 using the reddening curves of ref. 63. We applied an atmospheric fitting technique that is very similar to that described in ref. 26 by linearly normalizing and fitting the Balmer spectral lines of the UVES data using a Markov Chain Monte Carlo algorithm, maximizing the likelihood for a best-fit solution. We also utilized Pan-STARRS photometry64 to perform a hybrid fit using both datasets simultaneously. With high signal-to-noise ratio data, we were able to fit all Balmer lines from Hα to H11. Furthermore, to give the photometric and spectroscopic data a similar weight, we applied an extra weighting (×1,000) to the photometric fit. Without this weighting, the spectra would have overdominated the best-fit solution. Only spectra taken at the times where a distinct double-line splitting was evident at Hα were modelled to avoid fitting degeneracies between the two stars, of which there were 10 each (a total of 20). In deriving errors, we individually fitted each red-arm spectrum that revealed a double-lined Hα split along with the nearest-in-time blue-arm spectrum while weighting the photometry by ×100. Then we took the standard deviation of all measurements to be the error in the star's surface gravity and temperature. The new best-fit atmospheric parameters are stated in Table 1, which are entirely consistent with previous values26. We performed an independent spectroscopic fit using a previously published HST spectrum38. WDJ181058.67+311940.94 was observed for a single 1,000 s exposure using the COS on 19 February 2022. The observation had a central wavelength of 1,291 Å with the G130M grating, giving a resolution of R = 12,000–16,000 and a wavelength range of 1,130–1,430 Å with a gap at 1,278–1,288 Å due to the positioning of the two detector segments. Given the vastly different methods and the fact that WDJ181058.67+311940.94 is not double lined at Lyman-α in the ultraviolet data, no RVs were extracted, but the predicted RVs of the two stars at the centre of exposure (−37.8 km s−1 for the more massive and 139.6 km s−1 for the less massive star, respectively) were fixed in the fitting procedure. Our spectral fitting method was identical to that presented in ref. 38 with the only exceptions being that a second hydrogen-rich atmosphere white dwarf is included in the model, in which we adopt A(V) = 0.0312 mag and in which the mid-exposure RV of the two stars is considered. A hybrid (spectroscopic and photometric) fit was performed with no extra error weighting applied using the HST/COS spectrum and photometry from Pan-STARRS g, r, i, z, y (ref. 64), fixing the distance to Gaia DR3 parallax. Updated model atmospheres65 with a white dwarf mass–radius relationship61 were used to fit the absolute fluxes. In addition, strong absorption lines affecting the continuum were masked in the COS spectrum38. To address the inconsistencies reported between ultraviolet and optical parameters38, a systematic offset of 1% in Teff and 0.1 dex in log g were added to the ultraviolet values of both stars in the hybrid fitting, whereas trial values in the optical were unchanged. The best-fit model to the spectra are shown in Fig. 1, and the results of our atmospheric fitting are given in Table 1 and compared with the optical solution. We found a total mass of 1.537 ± 0.018 M⊙ through this analysis, which again is consistent with previous values26. To provide a final adopted value from the atmospheric fitting inclusive of the results from the optical and the ultraviolet datasets, we concatenated the distributions obtained for each parameter to then quote the median and 68% confidence interval on the Teff and log g and interpolated these parameters to obtain masses. The adopted values are quoted in Tables 1 and 2. WD-BASS56 was again used to obtain RVs for all of the optical spectra. The best-fit synthetic spectrum agrees with the data extremely well (Fig. 1), but even with the correction of NLTE effects to the model gridline cores, the synthetic model flux is overpredicted in the line cores of Hα. To obtain the most accurate template for RV extraction possible, we fitted a Gaussian model to the Hα line cores of both stars combined with a four-term polynomial to model the broader wings of Hα, all within 10 Å of the Hα centre. The centre of a Hα absorption was isolated as the splitting of the two stars is most apparent around the non-thermal equilibrium line cores and hence the stars are most easily disentanglable. This method best modelled the shape of the spectral area around the line cores for the high signal-to-noise ratio and high resolution UVES spectra, but not for all other data sources. Instead, we took the result of the best-fit synthetic spectrum and added an extra Gaussian component at the line cores of Hα for both stars (following the method described in Section 4.4 of ref. 26), which improved the line-core shape significantly. The Gaussians were fitted to all relevant spectra simultaneously and this final template spectrum was then used for RV extraction in WD-BASS. We started by fitting the RV of both stars to each spectrum by taking the median of 1,000 bootstrapping iterations and taking errors as the standard deviation of this bootstrapped posterior distribution. With the full set of 82 RV measurements (Supplementary Table 1), we then searched for an orbital period, P, by minimizing the χ2 of equation (2) for trial semi-amplitudes (K1, K2) and velocity offsets (γ1, γ2) of each star using a least squares algorithm, where Upper bounds on the semi-amplitudes Kmax,1 and Kmax,2 were set for a trial period by applying an edge-on (i = 90∘) inclination for a 1.4 M⊙ + 0.15 M⊙ double white dwarf in a Keplerian orbit (the maximum and observed minimum mass of a white dwarf, respectively). There is no indication of eccentricity from the RVs, so the orbit was assumed to be circularized (e = 0). In the process, we noticed a deviation from Keplerian motion around conjunction which is caused by degeneracy in the fitted RVs as the stars spectrally overlap. This is unsurprising as the velocity resolution of the ALFOSC, ISIS and IDS data was around 30–40 km s−1, whereas in the higher resolution FIES spectra a lower signal-to-noise ratio led to the same degeneracies. We decided to ignore these RVs when fitting the orbital motion by masking measurements that were within 15 km s−1 of the RV of each star at conjunction. All RVs from the UVES spectra within this range were utilized as its high signal-to-noise combined with twice the velocity resolution did not cause any noticeable deviation. With the final periodogram, two prominent peaks appeared at very similar solutions; we adopted the solution with a 14.2356 h period and another at 14.2308 h, but the second one could be rejected owing to a gravitational redshift difference that would be a strong outlier from that expected in the atmospheric solution. Returning to equation (2) with the final orbital solution and taking into account all combinations of masses from the atmospheric analysis, we conclude that WDJ181058.67+311940.94 has an inclination i ≈ 35–45 deg. We analysed the TESS66 light curve of WDJ181058.67+311940.94 in all cadences to search for any photometric signature of photometric variability with Lomb–Scargle67,68 and boxed-least-squares periodograms but found no variation on the orbital period. For an eclipse to be witnessed in this system, the inclination would have to be above 89.64 deg and photometric variability from ellipsoidal modulation or irradiation is minute for a system with 14.24 h orbital period. The Doppler beaming from the two stars is nullified by their opposing motion of near-identical RV amplitudes and a similar flux contribution69, and hence non-eclipsing forms of variability are not expected. We created two white dwarfs from the premain sequence phase using the stellar evolution code MESA70,71,72,73,74,75, evolving them to carbon–oxygen white dwarfs of 0.87 M⊙ and 0.71 M⊙. These masses align with observations from VLT/UVES spectra fitting. Compared with previous merger simulations, using self-consistent models evolved in MESA allowed us to start from realistic composition profiles. In particular, the two white dwarfs have a helium shell of 8 × 10−3 M⊙ (for the 0.71 M⊙ white dwarf) and 3 × 10−3 M⊙ (for the 0.87 M⊙ white dwarf). We then created two three-dimensional white dwarfs in hydrostatic equilibrium with the same masses and abundance profiles in AREPO. We resolved the white dwarfs with cells with a roughly constant mass of 10−7 M⊙ and used a passive scalar to resolve the helium shells of both white dwarfs even better with a mass resolution of 10−8 M⊙. We relaxed both white dwarfs in isolation for ten dynamical timescales, actively dampening any gas velocities for the first half of this time. The density and composition profiles of the relaxed white dwarfs, in particular close to the surface, well resembled the initial one-dimensional profiles obtained from MESA. We put both white dwarfs into a binary system in corotation with an initial period of 73 s. At this period, the separation is about 1.5 times larger than the separation where the secondary white dwarf will fill its Roche lobe. We applied an accelerated inspiral term that removes angular momentum in the same way as gravitational waves, but on a much faster timescale. In this way, we obtained a binary system in equilibrium when mass transfer started on a scale that we could resolve in the simulation. At this time, the physical system would have transferred mass at a low rate for possibly hundreds of years, but the total mass transferred is likely to be negligible. The secondary white dwarf eventually started filling and then overfilling its Roche lobe, and we stopped the accelerated inspiral when the density at the inner Lagrange point between the white dwarfs reached 2 × 104 g cm−3. Only then did the density in the accretion stream become large enough to dynamically affect the surface of the primary white dwarf43,76,77. The binary system had now shrunk to a separation of 0.03 R⊙ and an orbital period of 39 s. We then continued to evolve the binary system conservatively and switched on a live nuclear reaction network with 55 isotopes43,44. After evolving the binary system conservatively for 55 s, the interaction of the accretion stream with the surface of the primary white dwarf ignited a helium detonation close to the point of interaction (second column of Fig. 3), which is consistent with previous simulations of more massive white dwarf binaries77,78,79. As in the classic double detonation scenario where the helium detonation is caused by instabilities in a massive helium shell80,81, the helium detonation wraps around the primary white dwarf. It sends a shock wave into the core of the white dwarf, that converges in a single point at a density of 9.6 × 106 g cm−3. Because of a lack of numerical resolution, the simulation does not self-consistently ignite a carbon detonation there, but resolved ignition simulations indicated that, at this density, we expect a detonation to form at the convergence point82,83. To model the ignition of the detonation when the shock converges in the simulation, we set the temperature of 178 cells that contained 1.8 × 10−5 M⊙ around the convergence point to 5 × 109 K. This injected 4.8 × 1046 erg (which is negligible compared with the energy release of the whole simulation) and ignited the detonation. The detonation completely destroyed the primary white dwarf. When the shock wave of its explosion hit the secondary white dwarf, the double detonation mechanism repeated itself. The shock wave ignited a helium detonation that drove a shock wave into the core and converged at a density of 8.5 × 106 g cm−3. In this case, carbon burning started at the convergence point, but not strongly enough to start a detonation. We again ignited a detonation at the convergence point by setting the temperature of 708 cells that contained 6.9 × 10−5 M⊙ to 6 × 109 K, which injected 8.2 × 1047 erg and was sufficient to ignite the detonation that then destroyed the secondary white dwarf as well. The total explosion energy was 1.2 × 1051 erg. The core of the secondary white dwarf ignited 4.2 s after the core of the primary white dwarf. At this time, the ashes of the primary white dwarf had already expanded far beyond the secondary white dwarf. So when the latter exploded as well, its ejecta expanded into and remained in the centre of the ejecta of the primary white dwarf43. The outermost layers of ejecta were the ashes of the helium detonation of the primary white dwarf. The centre of the ejecta consisted of the ashes of the secondary white dwarf, which contained 0.25 M⊙ of oxygen, 0.4 M⊙ of intermediate mass elements and only 0.01 M⊙ of iron group elements, with a roughly equal fraction of 56Ni and 54Fe. We obtained preliminary synthetic light curves from spherically averaging the ejecta and computing light curves with the Monte Carlo radiation transport code ARTIS84,85. The resulting supernova had a maximum brightness in the B band of MB = −16.4 (mB = −14.7) and a maximum brightness in the V band of MV = −17.8 (mV = −16.1), which is consistent with traditional double detonation models of single white dwarfs with a similar mass to our primary white dwarf, because the secondary white dwarf does not produce any significant amount of radioactive 56Ni (refs. 86,87,88). That said, our explosion is likely to have avoided the imprint of thick helium shells on light curves and spectra88,89,90. It most likely appeared as a subluminous type Ia supernova. However, the obvious large-scale asymmetries visible in Fig. 4 indicate that three-dimensional synthetic observables will be needed to make any reliable statement about the expected display of this supernova79,88. These will be presented and discussed as part of a larger sample of merger simulations in the future. 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D.J. also acknowledges support from the Agencia Estatal de Investigación del Ministerio de Ciencia, Innovación y Universidades (MCIU/AEI) and the European Union NextGenerationEU/PRTR with reference CNS2023-143910 (https://doi.org/10.13039/501100011033). This research received funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme no. 101002408 (MOS100PC). S.T. acknowledges support from the Netherlands Research Council NWO (grant VIDI 203.061). A.B. is a Postdoctoral Fellow of the Natural Sciences and Engineering Research Council (NSERC) of Canada. The study was based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme 113.27QU. The Isaac Newton Telescope and the William Herschel Telescope are operated on the island of La Palma by the Isaac Newton Group of Telescopes in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. The study was also based on observations made with the Nordic Optical Telescope, owned in collaboration by the University of Turku and Aarhus University, and operated jointly by Aarhus University, the University of Turku and the University of Oslo, representing Denmark, Finland and Norway, the University of Iceland and Stockholm University at the Observatorio del Roque de los Muchachos, La Palma, Spain, of the Instituto de Astrofísica de Canarias. The data presented here were obtained in part with ALFOSC, which is provided by the Instituto de Astrofísica de Andalucia (IAA) under a joint agreement with the University of Copenhagen and NOT. In addition, this research was based on observations made with the NASA/ESA Hubble Space Telescope obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555. These observations are associated with programme 16642. Department of Physics, University of Warwick, Coventry, UK James Munday, Ingrid Pelisoli, Snehalata Sahu, Pier-Emmanuel Tremblay, Mark Magee & Antoine Bédard Max-Planck-Institut für Astrophysik, Garching, Germany Ruediger Pakmor & Abinaya Swaruba Rajamuthukumar Instituto de Astrofísica de Canarias, La Laguna, Spain David Jones Departamento de Astrofísica, Universidad de La Laguna, La Laguna, Spain David Jones Nordic Optical Telescope, Rambla José Ana Fernández Pérez, Breña Baja, Spain David Jones Department of Astrophysics/IMAPP, Radboud University, Nijmegen, the Netherlands Gijs Nelemans Institute for Astronomy, KU Leuven, Leuven, Belgium Gijs Nelemans SRON, Netherlands Institute for Space Research, Leiden, the Netherlands Gijs Nelemans Anton Pannekoek Institute for Astronomy, University of Amsterdam, Amsterdam, the Netherlands Silvia Toonen Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA Tim Cunningham You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar J.M. and R.P. carried out most of the modelling and analysis and wrote the majority of the paper. S.S. performed spectral fitting for the ultraviolet data. A.S.R. performed MESA modelling for accurate stellar compositions. I.P. and P.-E.T. supervised the project. D.J. played an integral part in obtaining spectra of the target. G.N., M.M., S.T., A.B. and T.C. provided much insight and many discussions throughout all stages of the project. All authors contributed with comments to the writing of the paper. Correspondence to James Munday. The authors declare no competing interests Nature Astronomy thanks Dongdong Liu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Table 1. The simulated type Ia detonation of WDJ181058.67+311940.94. The orbital period at the start of this animation is 39 s. A detonation first occurs on the surface of the accretor. The convergence of the helium shell detonation sends a shock wave towards the star's core, causing the detonation and complete destruction of the white dwarf. The mechanism then repeated on the secondary white dwarf, ultimately destroying both stars. The simulated type Ia detonation of WDJ181058.67+311940.94 viewed in the co-rotating reference frame. From top to bottom, the panels show the density, temperature and kinetic energy density. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Munday, J., Pakmor, R., Pelisoli, I. et al. A super-Chandrasekhar mass type Ia supernova progenitor at 49 pc set to detonate in 23 Gyr. Nat Astron (2025). https://doi.org/10.1038/s41550-025-02528-4 Download citation Received: 31 October 2024 Accepted: 10 March 2025 Published: 04 April 2025 DOI: https://doi.org/10.1038/s41550-025-02528-4 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. 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Groundbreaking research by physicists at The City College of New York is being credited for a novel discovery regarding the interaction of electronic excitations via spin waves. The finding by the Laboratory for Nano and Micro Photonics (LaNMP) team headed by physicist Vinod Menon could open the door to future technologies and advanced applications such as optical modulators, all-optical logic gates, and quantum transducers. The work is reported in the journal Nature Materials. The researchers showed the emergence of interaction between electronic excitations (excitons -- electron hole pairs) mediated via spin waves in atomically thin (2D) magnets. They demonstrated that the excitons can interact indirectly through magnons (spin waves), which are like ripples or waves in the 2D material's magnetic structure. "Think of magnons as tiny flip-flops of atomic magnets inside the crystal. One exciton changes the local magnetism, and that change then influences another exciton nearby. It's like two floating objects pulling toward each other by disturbing water waves around them," said Menon. To demonstrate this, the Menon group utilized a magnetic semiconductor, CrSBr which the group had previously shown to host strong light-matter interaction (Nature, 2023). Post-doctoral fellows Biswajit Datta and Pratap Chandra Adak led the research along with graduate students Sichao Yu and Agneya Dharmapalan in collaboration with the groups at the CUNY Advanced Science Research Center, University of Chemistry and Technology -- Prague, RPTU -- Kaiserslautern, Germany and NREL, USA. "What is especially exciting about this discovery is that the interaction between excitons can be controlled externally using a magnetic field, thanks to the tunable magnetism of 2D materials. That means we can effectively switch the interaction on or off, which is hard to do with other types of interactions," said Datta. "One particularly exciting application enabled by this discovery is in the development of quantum transducers -- devices that convert quantum signals from one frequency to another, such as from microwave to optical. These are key components for building quantum computers and enabling the quantum internet." said Adak, another lead author of this work. The work at CCNY was supported by U.S. Department of Energy -- Office of Basic Energy Sciences, The Army Research Office, The National Science Foundation and The Gordon and Betty Moore Foundation. Story Source: Materials provided by City College of New York. Note: Content may be edited for style and length. Journal Reference: Cite This Page: Stay informed with ScienceDaily's free email newsletter, updated daily and weekly. Or view our many newsfeeds in your RSS reader: Keep up to date with the latest news from ScienceDaily via social networks: Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?