Heavy-element production in a compact object merger observed by JWST.

The mergers of binary compact objects such as neutron stars and black holes are of central interest to several areas of astrophysics, including as the progenitors of gamma-ray bursts (GRBs)1, sources of high-frequency gravitational waves (GWs)2 and likely production sites for heavy-element nucleosynthesis by means of rapid neutron capture (the r-process)3. Here we present observations of the exceptionally bright GRB 230307A. We show that GRB 230307A belongs to the class of long-duration GRBs associated with compact object mergers4-6 and contains a kilonova similar to AT2017gfo, associated with the GW merger GW170817 (refs. 7-12). We obtained James Webb Space Telescope (JWST) mid-infrared imaging and spectroscopy 29 and 61 days after the burst. The spectroscopy shows an emission line at 2.15 microns, which we interpret as tellurium (atomic mass A = 130) and a very red source, emitting most of its light in the mid-infrared owing to the production of lanthanides. These observations demonstrate that nucleosynthesis in GRBs can create r-process elements across a broad atomic mass range and play a central role in heavy-element nucleosynthesis across the Universe.

The data-driven future of high-energy-density physics.

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics-however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

Designing accurate emulators for scientific processes using calibration-driven deep models.

Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.

Pituitary gigantism: a rare learning opportunity.

INTRODUCTION: Pituitary gigantism is a rare but significant paediatric condition with complexities surrounding diagnosis and management. Transsphenoidal surgery (TSS) is the treatment of choice; however, medical treatment is often considered as adjuvant therapy. CASE: A 10½ -year-old boy presented with tall stature and a height velocity of 11 cm/year. His height was 178.7 cm (+5.8 SD above mean) and insulin-like growth factor-1 (IGF-1) was elevated. An oral glucose tolerance test demonstrated non-suppression of growth hormone (GH). Initial contrast MRI was inconclusive, but C-11 methionine functional positron emission tomography CT identified a 6 mm pituitary microadenoma. A multidisciplinary team clinic held with the family allowed discussion about medical and surgical treatment options. Due to a number of factors including the patient's young age, prepubertal status, a wish to allow him to settle into his new high school and his desire to reach a final height taller than his father's height, it was decided to try medical therapy first with a somatostatin analogue. Pubertal induction was also commenced and bilateral epiphysiodesis surgery performed. Initial response to octreotide was positive; however, 4 months into therapy his IGF-1 was climbing and a repeat GH profile was not fully suppressed. The patient therefore proceeded to have successful TSS excision of the adenoma. CONCLUSION: Rare cases such as this require sharing of knowledge and expertise, so the best possible care is offered. It is often necessary to work across sites and disciplines. Each case requires an individual approach tailored to the patient and their family.

A rapidly changing jet orientation in the stellar-mass black-hole system V404 Cygni.

Powerful relativistic jets are one of the main ways in which accreting black holes provide kinetic feedback to their surroundings. Jets launched from or redirected by the accretion flow that powers them are expected to be affected by the dynamics of the flow, which for accreting stellar-mass black holes has shown evidence for precession1 due to frame-dragging effects that occur when the black-hole spin axis is misaligned with the orbital plane of its companion star2. Recently, theoretical simulations have suggested that the jets can exert an additional torque on the accretion flow3, although the interplay between the dynamics of the accretion flow and the launching of the jets is not yet understood. Here we report a rapidly changing jet orientation-on a time scale of minutes to hours-in the black-hole X-ray binary V404 Cygni, detected with very-long-baseline interferometry during the peak of its 2015 outburst. We show that this changing jet orientation can be modelled as the Lense-Thirring precession of a vertically extended slim disk that arises from the super-Eddington accretion rate4. Our findings suggest that the dynamics of the precessing inner accretion disk could play a role in either directly launching or redirecting the jets within the inner few hundred gravitational radii. Similar dynamics should be expected in any strongly accreting black hole whose spin is misaligned with the inflowing gas, both affecting the observational characteristics of the jets and distributing the black-hole feedback more uniformly over the surrounding environment5,6.

Drawing and the dynamic nature of living systems.

Representing the dynamic nature of biological processes is a challenge. This article describes a collaborative project in which the authors - a philosopher of biology, an artist and a cell biologist - explore how best to represent the entire process of cell division in one connected image. This involved a series of group Drawing Labs, one-to-one sessions, and discussions between the authors. The drawings generated during the collaboration were then reviewed by four experts in cell division. We propose that such an approach has value, both in communicating the dynamic nature of biological processes and in generating new insights and hypotheses that can be tested by artists and scientists.

Competing influences of anthropogenic warming, ENSO, and plant physiology on future terrestrial aridity.

The 2011-2016 Californian drought illustrates that drought-prone areas do not always experience relief once a favorable phase of El Niño-Southern Oscillation (ENSO) returns. In the 21st century, such an expectation is unrealistic in regions where global warming induces an increase in terrestrial aridity larger than the aridity changes driven by ENSO variability. This premise is also flawed in areas where precipitation supply cannot offset the global warming-induced increased evaporative demand. Here, atmosphere-only experiments are analyzed to identify land regions in which aridity is currently sensitive to ENSO, and where projected future changes in mean aridity exceed the range caused by ENSO variability. Insights into the drivers of these aridity changes are obtained in simulations with incremental addition of three different factors to current climate: ocean warming, vegetation response to elevated CO2 levels, and intensified CO2 radiative forcing. The effect of ocean warming overwhelms the range of ENSO-driven temperature variability worldwide, increasing potential evapotranspiration (PET) in most ENSO-sensitive regions. Additionally, ~39% of the regions currently sensitive to ENSO receive less precipitation in the future, independent of the ENSO phase. Aridity increases consequently in 67-72% of the ENSO-sensitive area. When both radiative and physiological effects are considered, the area affected by aridity rises to 75-79% when using PET-derived measures of aridity, but declines to 41% when total soil moisture aridity indicator is employed. This reduction mainly occurs because plant stomatal resistance increases under enhanced CO2 concentrations, which results in improved plant water use efficiency, and hence reduced evapotranspiration and soil desiccation. Imposing CO2-invariant stomatal resistance may overestimate future drying in PET-derived indices.