Search

We construct a semi-analytical model that describes the convective core mass evolution of massive stars experiencing mass loss during the main-sequence stage. We first conduct a suite of 1D stellar evolution calculations to build insight into how convective core masses behave under idealized mass loss. Based on these simulations, we find several universal relations between global properties of the star that hold regardless of the mass loss history. By combining these relations, we construct a semi-analytic framework that can predict the convective core mass evolution for arbitrary mass loss histories and hence the helium core mass at the end of the main sequence. Our formulae improve upon existing methods for predicting the core mass in rapid population synthesis codes., Comment: 12 pages, 10 figures

The production of complex astronomical data is accelerating, especially with newer telescopes producing ever more large-scale surveys. The increased quantity, complexity, and variety of astronomical data demand a parallel increase in skill and sophistication in developing, deciding, and deploying statistical methods. Understanding limitations and appreciating nuances in statistical and machine learning methods and the reasoning behind them is essential for improving data-analytic proficiency and acumen. Aiming to facilitate such improvement in astronomy, we delineate cautionary tales in statistics via six maxims, with examples drawn from the astronomical literature. Inspired by the significant quality improvement in business and manufacturing processes by the routine adoption of Six Sigma, we hope the routine reflection on these Six Maxims will improve the quality of both data analysis and scientific findings in astronomy.

This paper explores the relationship between economic growth and CO$_2$ emissions across European regions from 1990 to 2022, specifically concerning the dynamics of emissions growth rates through different phases of the European Union Emissions Trading System (EU ETS). We find that emissions dynamics exhibit significant volatility influenced by changing policy frameworks. Furthermore, the distribution of emissions growth rates is asymmetric and displays fat tails, suggesting the potential for extreme emissions events. We identify marked disparities across regions: less developed regions experience higher emissions growth rates and greater volatility compared to many developed areas, which show a trend of declining emissions and reduced volatility. Our findings highlight the sensitivity of emissions to policy changes and emphasise the need for clear and effective governance in emissions trading schemes., Comment: 19 pages, 5 figures, 2 tables

Advanced probabilistic programming languages (PPLs) use hybrid inference systems to combine symbolic exact inference and Monte Carlo methods to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are encoded symbolically and variables that are encoded with sampled values, and the heuristics are not necessarily aligned with the performance evaluation metrics used by the developer. In this work, we present inference plans, a programming interface that enables developers to control the partitioning of random variables during hybrid particle filtering. We further present Siren, a new PPL that enables developers to use annotations to specify inference plans the inference system must implement. To assist developers with statically reasoning about whether an inference plan can be implemented, we present an abstract-interpretation-based static analysis for Siren for determining inference plan satisfiability. We prove the analysis is sound with respect to Siren's semantics. Our evaluation applies inference plans to three different hybrid particle filtering algorithms on a suite of benchmarks and shows that the control provided by inference plans enables speed ups of 1.76x on average and up to 206x to reach target accuracy, compared to the inference plans implemented by default heuristics; the results also show that inference plans improve accuracy by 1.83x on average and up to 595x with less or equal runtime, compared to the default inference plans. We further show that the static analysis is precise in practice, identifying all satisfiable inference plans in 27 out of the 33 benchmark-algorithm combinations.

The latest binary black hole population estimates argue for a subpopulation of unequal component mass binaries with spins that are likely small but isotropically distributed. This implies a non-zero probability of detecting spin-induced orbital precession and higher order multipoles moments in the observed gravitational-wave signals. In this work we directly calculate the probability for precession and higher order multipoles in each significant gravitational-wave candidate observed by the LIGO--Virgo--KAGRA collaborations (LVK). We find that only one event shows substantial evidence for precession: GW200129_065458, and two events show substantial evidence for higher order multipoles: GW190412 and GW190814; any evidence for precession and higher order multipoles in other gravitational-wave signals is consistent with random fluctuations caused by noise. We then compare our observations with expectations from population models, and confirm that current population estimates from the LVK accurately predict the number of observed events with significant evidence for precession and higher order multipole moments. In particular, we find that this population model predicts that a binary with significant evidence for precession will occur once in every $\sim 50$ detections, and a binary with significant evidence for higher order multipole moments will occur once in every $\sim 70$ observations. However, we emphasise that since substantial evidence for precession and higher order multipole moments have only been observed in three events, any population model that includes a subpopulation of binaries yielding $\sim 2\%$ of events with detectable precession and higher order multipole moments will likely be consistent with the data., Comment: 13 pages, 5 figures

This paper investigates the effectiveness of the ``low-carbon economy'' expenditures from European Structural and Investment Funds in fostering reductions in greenhouse gas emissions within European regions, focusing on the 2007-2013 and 2014-2020 programme periods. By decomposing emissions time series into trend and cycle components and considering them within a panel data framework, our research highlights that the impacts of low-carbon economy expenditures vary, qualitatively and quantitatively, with the targeted regions' development level. We find significant emissions reductions in developed and transition regions yet less favourable outcomes in less developed areas. Further analysis into specific greenhouse gas emissions types (CO$_2$, CH$_4$, and N$_2$O) reveals inconsistent impacts, underscoring the complexity of achieving emissions reductions. Our findings emphasise the need for tailored environmental strategies that accommodate the economic disparities of regions in the European Union., Comment: 34 pages, 3 figures and 12 tables

Type Ia supernovae (SNe Ia) are thermonuclear exploding stars that can be used to put constraints on the nature of our universe. One challenge with population analyses of SNe Ia is Malmquist bias, where we preferentially observe the brighter SNe due to limitations of our telescopes. If untreated, this bias can propagate through to our posteriors on cosmological parameters. In this paper, we develop a novel technique of using a normalising flow to learn the non-analytical likelihood of observing a SN Ia for a given survey from simulations, that is independent of any cosmological model. The learnt likelihood is then used in a hierarchical Bayesian model with Hamiltonian Monte Carlo sampling to put constraints on different sets of cosmological parameters conditioned on the observed data. We verify this technique on toy model simulations finding excellent agreement with analytically-derived posteriors to within $1 \sigma$., Comment: Accepted for the ICML AI for Science Workshop (2024). 13 pages, 5 figures, 2 tables

Surveys in the Milky Way and Large Magellanic Cloud revealed that the majority of massive stars will interact with companions during their lives. However, knowledge of the binary properties of massive stars at low metallicity, which approaches the conditions of the Early Universe, remains sparse. We present the Binarity at LOw Metallicity (BLOeM) campaign - an ESO large programme designed to obtain 25 epochs of spectroscopy for 929 massive stars in the SMC - the lowest metallicity conditions in which multiplicity is probed to date (Z = 0.2 Zsun). BLOeM will provide (i) the binary fraction, (ii) the orbital configurations of systems with periods P < 3 yr, (iii) dormant OB+BH binaries, and (iv) a legacy database of physical parameters of massive stars at low metallicity. The stars are observed with the LR02 setup of the giraffe instrument of the Very Large Telescope (3960-4570A, resolving power R=6200; typical signal-to-noise ratio S/N=70-100). This paper utilises the first 9 epochs obtained over a three-month time. We describe the survey and data reduction, perform a spectral classification of the stacked spectra, and construct a Hertzsprung-Russell diagram of the sample via spectral-type and photometric calibrations. The sample covers spectral types from O4 to F5, spanning the effective temperature and luminosity ranges 6.5

The three dynamically confirmed wind-fed black hole high-mass X-ray binaries (BH-HMXBs) are suggested to all contain a highly spinning black hole (BH). However, based on the theories of efficient angular momentum transport inside the stars, we expect that the first-born BHs in binary systems should have low spins, which is consistent with gravitational-wave observations. As a result, the origin of the high BH spins measured in wind-fed BH-HMXBs remains a mystery. In this paper, we conduct a binary population synthesis study on wind-fed BH-HMXBs at solar metallicity with the use of the newly developed code POSYDON, considering three scenarios for BH accretion: Eddington-limited, moderately super-Eddington, and fully conservative accretion. Taking into account the conditions for accretion-disk formation, we find that regardless of the accretion model, these systems are more likely to have already experienced a phase of Roche-lobe overflow after the BH formation. To account for the extreme BH spins, highly conservative accretion onto BHs is required, when assuming the accreted material carries the specific angular momentum at the innermost stable orbit. Besides, in our simulations we found that the systems with donor stars within the mass range of $10-20\,M_{\odot}$ are prevalent, posing a challenge in explaining simultaneously all observed properties of the BH-HMXB in our Galaxy, Cygnus X-1, and potentially hinting that the accretion efficiency onto non-degenerate stars, before the formation of the BH, is also more conservative than assumed in our simulations., Comment: Submitted to A&A. Comments are welcome!

Stellar models indicate that the core compactness of a star, which is a common proxy for its explodability in a supernova, does not increase monotonically with the star's mass. Rather, the core compactness dips sharply over a range of carbon-oxygen core masses; this range may be somewhat sensitive to the star's metallicity and evolutionary history. Stars in this compactness dip are expected to experience supernovae leaving behind neutron stars, whereas stars on either side of this range are expected to form black holes. This results in a hypothetical mass range in which black holes should seldom form. Quantitatively, when applied to binary stripped stars, these models predict a dearth of binary black holes with component masses $\approx 10 M_\odot - 15 M_\odot$. The population of gravitational-wave signals indicates potential evidence for a dip in the distribution of chirp masses of merging binary black holes near $\approx 10 M_\odot - 12 M_\odot$. This feature could be linked to the hypothetical component mass gap described above, but this interpretation depends on what assumptions are made of the binaries' mass ratios. Here, we directly probe the distribution of binary black hole component masses to look for evidence of a gap. We find no evidence for this feature using data from the third gravitational-wave transient catalogue (GWTC-3). If this gap does exist in nature, we find that it is unlikely to be resolvable by the end of the current (fourth) LIGO-Virgo-KAGRA (LVK) observing run., Comment: 16 pages, 11 figures

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, notably in connecting ideas and adhering to logical rules to solve problems. These models have evolved to accommodate various data modalities, including sound and images, known as multimodal LLMs (MLLMs), which are capable of describing images or sound recordings. Previous work has demonstrated that when the LLM component in MLLMs is frozen, the audio or visual encoder serves to caption the sound or image input facilitating text-based reasoning with the LLM component. We are interested in using the LLM's reasoning capabilities in order to facilitate classification. In this paper, we demonstrate through a captioning/classification experiment that an audio MLLM cannot fully leverage its LLM's text-based reasoning when generating audio captions. We also consider how this may be due to MLLMs separately representing auditory and textual information such that it severs the reasoning pathway from the LLM to the audio encoder., Comment: 9 pages

Type Ia supernovae (SNe Ia) are standarizable candles whose observed light curves can be used to infer their distances, which can in turn be used in cosmological analyses. As the quantity of observed SNe Ia grows with current and upcoming surveys, increasingly scalable analyses are necessary to take full advantage of these new datasets for precise estimation of cosmological parameters. Bayesian inference methods enable fitting SN Ia light curves with robust uncertainty quantification, but traditional posterior sampling using Markov Chain Monte Carlo (MCMC) is computationally expensive. We present an implementation of variational inference (VI) to accelerate the fitting of SN Ia light curves using the BayeSN hierarchical Bayesian model for time-varying SN Ia spectral energy distributions (SEDs). We demonstrate and evaluate its performance on both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior -- a multivariate normal and a custom multivariate zero-lower-truncated normal distribution -- and compare them with the Laplace Approximation and full MCMC analysis. To validate of our variational approximation, we calculate the pareto-smoothed importance sampling (PSIS) diagnostic, and perform variational simulation-based calibration (VSBC). The VI approximation achieves similar results to MCMC but with an order-of-magnitude speedup for the inference of the photometric distance moduli. Overall, we show that VI is a promising method for scalable parameter inference that enables analysis of larger datasets for precision cosmology., Comment: 14 pages, 7 figures, 1 table

Wolf-Rayet stars in close binary systems can be tidally spun up by their companions, potentially leaving behind fast-spinning highly-magnetized neutron stars, known as ``magnetars", after core collapse. These newborn magnetars can transfer rotational energy into heating and accelerating the ejecta, producing hydrogen-poor superluminous supernovae (SLSNe). In this {\em{Letter}}, we propose that the magnetar wind of the newborn magnetar could significantly evaporate its companion star, typically a main-sequence or helium star, if the binary system is not disrupted by the {abrupt mass loss and} SN kick. The subsequent heating and acceleration of the evaporated star material along with the SN ejecta by the magnetar wind can produce a post-peak bump in the SLSN lightcurve. Our model can reproduce the primary peaks and post-peak bumps of four example observed multiband SLSN lightcurves, revealing that the mass of the evaporated material could be $\sim0.4-0.6\,M_\odot$ if the material is hydrogen-rich. {We propose that the magnetar could induce strongly enhanced evaporation from its companion star near the pericenter if the orbit of the post-SN binary is highly eccentric, ultimately generating multiple post-peak bumps in the SLSN lightcurves. This ``magnetar-star binary engine" model may offer a possible explanation for the evolution of polarization, along with the origin and velocity broadening of late-time hydrogen or helium broad spectral features observed in some bumpy SLSNe.} The diversity in the lightcurves and spectra of SLSNe may be attributed to the wide variety of companion stars and post-SN binary systems., Comment: Accepted by ApJL, 14 pages, 4 figures, 2 tables

Stars falling too close to massive black holes in the centres of galaxies can be torn apart by the strong tidal forces. Simulating the subsequent feeding of the black hole with disrupted material has proved challenging because of the range of timescales involved. Here we report a set of simulations that capture the relativistic disruption of the star, followed by one year of evolution of the returning debris stream. These reveal the formation of an expanding asymmetric bubble of material extending to hundreds of astronomical units -- an outflowing Eddington envelope with an optically thick inner region. Such envelopes have been hypothesised as the reprocessing layer needed to explain optical/UV emission in tidal disruption events, but never produced self-consistently in a simulation. Our model broadly matches the observed light curves with low temperatures, faint luminosities, and line widths of 10,000--20,000 km/s., Comment: 12 pages + appendices, 15 figures, accepted to ApJL. Data+movies on zenodo at https://doi.org/10.5281/zenodo.11438154 . Movies on YouTube... watch this one first: https://www.youtube.com/watch?v=KlffYCtYxFU then: https://www.youtube.com/watch?v=bPurbeQNgvI&t=19s and finally watch the expanding "bubble" with rho and T at photosphere: https://www.youtube.com/watch?v=D4dyZeUbfvM

We present LAISS (Lightcurve Anomaly Identification and Similarity Search), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly ZTF Alert Stream via the ANTARES broker, identifying a manageable $\sim$1-5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host-galaxy features within a random forest classifier, tagging transients of rare classes (spectroscopic anomalies), of uncommon host-galaxy environments (contextual anomalies), and of peculiar or interaction-powered phenomena (behavioral anomalies). Moreover, we demonstrate the power of a low-latency ($\sim$ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host-galaxy environments. We use analogs for data-driven discovery, characterization, (re-)classification, and imputation in retrospective and real-time searches. To date we have identified $\sim$50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to: SLSNe, TDEs, SNe IIn, SNe IIb, SNe Ia-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018-2021 and absent from public catalogs ($\sim$1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the "needle in the haystack" in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data., Comment: 44 pages (68 pages with Appendix), 15 figures, accepted to ApJ

Street view imagery (SVI), largely captured via outfitted fleets or mounted dashcams in consumer vehicles is a rapidly growing source of geospatial data used in urban sensing and development. These datasets are often collected opportunistically, are massive in size, and vary in quality which limits the scope and extent of their use in urban planning. Thus far there has not been much work to identify the obstacles experienced and tools needed by the users of such datasets. This severely limits the opportunities of using emerging street view images in supporting novel research questions that can improve the quality of urban life. This work includes a formative interview study with 5 expert users of large-scale street view datasets from academia, urban planning, and related professions which identifies novel use cases, challenges, and opportunities to increase the utility of these datasets. Based on the user findings, we present a framework to evaluate the quality of information for street images across three attributes (spatial, temporal, and content) that stakeholders can utilize for estimating the value of a dataset, and to improve it over time for their respective use case. We then present a case study using novel street view images where we evaluate our framework and present practical use cases for users. We discuss the implications for designing future systems to support the collection and use of street view data to assist in sensing and planning the urban environment.

For many years, it has been claimed that the Galactic ridge X-ray emission at the Galactic Center (GC) is truly diffuse in nature. However, with the advancement of modern X-ray satellites, it has been found that most of the diffuse emission is actually comprised of thousands of previously unresolved X-ray point sources. Further, many studies suggest that a vast majority of these X-ray point sources are magnetic cataclysmic variables (mCVs) and active binaries. One unambiguous way to identify these mCVs and other sources is by detecting their X-ray periodicity. Therefore, we systematically searched for periodic X-ray sources in the inner Galactic disk, including the GC region. We have used data from our ongoing XMM-Newton Heritage survey of the inner Galactic disk ($350^{\circ}\lesssim l\lesssim+7^{\circ}$ and $-1^{\circ}\lesssim b\lesssim +1^{\circ}$) plus the XMM-Newton archival observations of the GC. We computed the Lomb-Scargle periodogram of the light curves for the periodicity search. We fitted the energy spectra of the sources using a simple power-law model plus three Gaussians at 6.4, 6.7, and 6.9 keV for the iron $K$ emission complex. We detected periodicity in 26 sources. For 14 of them, this is the first discovery of periodicity. For the other 12 sources, we found periods similar to those already known, indicating no significant period evolution. We also searched for the Gaia counterparts of the periodic sources to estimate their distances using the Gaia parallax. We found a likely Gaia counterpart for seven sources. We have classified the sources into four categories based on the periodicity, hardness ratio, and the equivalent width of Fe $K$ line emission. Of the 14 sources where we detect the periodicity for the first time, four are likely to be intermediate polars, five are likely to be polars, two are neutron star X-ray binaries, and three are of unknown nature., Comment: 19 pages, 9 figures, accepted for publication in A&A

We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovae (SNae Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN's spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilising truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalise over 4000 latent variables (for a set of $\approx 100$ SNae Ia) to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortisation of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNae Ia from the Carnegie Supernova Project, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model -- e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-Ia contamination -- without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples., Comment: 16 pages, 10 figures; Published in MNRAS; (c): 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society

We introduce the Hawai`i Supernova Flows project and present summary statistics of the first 1218 astronomical transients observed, 669 of which are spectroscopically classified Type Ia Supernovae (SNe Ia). Our project is designed to obtain systematics-limited distances to SNe Ia while consuming minimal dedicated observational resources. This growing sample will provide increasing resolution into peculiar velocities as a function of position on the sky and redshift, allowing us to more accurately map the structure of dark matter. This can be used to derive cosmological parameters such as $\sigma_8$ and can be compared with large scale flow maps from other methods such as luminosity-line width or luminosity-velocity dispersion correlations in galaxies. Additionally, our photometry will provide a valuable test bed for analyses of SNe Ia incorporating near-infrared data. In this survey paper, we describe the methodology used to select targets, collect and reduce data, and calculate distances., Comment: 33 pages, 23 figures

We use rest-frame optical and near-infrared (NIR) observations of 42 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project at low-$z$ and 37 from the RAISIN Survey at high-$z$ to investigate correlations between SN Ia host galaxy dust, host mass, and redshift. This is the first time the SN Ia host galaxy dust extinction law at high-$z$ has been estimated using combined optical and rest-frame NIR data ($YJ$-band). We use the BayeSN hierarchical model to leverage the data's wide rest-frame wavelength range (extending to $\sim$1.0-1.2 microns for the RAISIN sample at $0.2\lesssim z\lesssim0.6$). By contrasting the RAISIN and CSP data, we constrain the population distributions of the host dust $R_V$ parameter for both redshift ranges. We place a limit on the difference in population mean $R_V$ between RAISIN and CSP of $-1.16<\Delta\mu(R_V)<1.38$ with 95% posterior probability. For RAISIN we estimate $\mu(R_V)=2.58\pm0.57$, and constrain the population standard deviation to $\sigma(R_V)<0.90~[2.42]$ at the 68 [95]% level. Given that we are only able to constrain the size of the low- to high-$z$ shift in $\mu(R_V)$ to $\lesssim1.4$ - which could still propagate to a substantial bias in the equation of state parameter $w$ - these and other recent results motivate continued effort to obtain rest-frame NIR data at low and high redshifts (e.g. using the Roman Space Telescope)., Comment: 15 pages, 13 figures, 1 table. Accepted for publication in MNRAS

The availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as an interface to interact with LLMs. Although many such tools have been released, almost all of them focus on general-purpose programming languages. Domain-specific languages, such as those crucial for Information Technology (IT) automation, have not received much attention. Ansible is one such YAML-based IT automation-specific language. Ansible Lightspeed is an LLM-based service designed explicitly to generate Ansible YAML given natural language prompt. This paper first presents the design and implementation of the Ansible Lightspeed service. We then evaluate its utility to developers using diverse indicators, including extended utilization, analysis of user rejected suggestions, as well as analysis of user sentiments. The analysis is based on data collected for 10,696 real users including 3,910 returning users. The code for Ansible Lightspeed service and the analysis framework is made available for others to use. To our knowledge, our study is the first to involve thousands of users in evaluating code assistants for domain-specific languages. We propose an improved version of user acceptance rate and we are the first code completion tool to present N-Day user retention figures. With our findings we provide insights into the effectiveness of small, dedicated models in a domain-specific context. We hope this work serves as a reference for software engineering and machine learning researchers exploring code completion services for domain-specific languages in particular and programming languages in general.

We explore the evolution of massive stars (>8 solar masses) with 1-D models and present analytical fits to the masses and binding energies of the convective portions of their envelopes. These fits are given as functions of total mass, metallicity, and surface temperature (used as a proxy for evolutionary phase). They enable the application of the two-stage common envelope formalism (Hirai & Mandel 2022) in rapid binary population synthesis frameworks. We estimate that the degree of orbital hardening following common-envelope ejection spans 6 orders of magnitude and is a very strong function of the accretor mass, and, to a lesser extent, donor evolutionary phase., Comment: Minor changes (including to fits) to match version accepted for publication in ApJ

Accreting main-sequence stars expand significantly when the mass accretion timescale is much shorter than their thermal timescales. This occurs during mass transfer from an evolved giant star onto a main-sequence companion in a binary system, and is an important phase in the formation of compact binaries including X-ray binaries, cataclysmic variables, and gravitational-wave sources. In this study, we compute 1D stellar models of main-sequence accretors with different initial masses and accretion rates. The calculations are used to derive semi-analytical approximations to the maximum expansion radius. We assume that mass transfer remains fully conservative as long as the inflated accretor fits within its Roche lobe, leading stars to behave like hamsters, stuffing excess material behind their expanding cheeks. We suggest a physically motivated prescription for the mass growth of such "hamstars", which can be used to determine mass-transfer efficiency in rapid binary population synthesis models. With this prescription, we estimate that progenitors of high-mass X-ray binaries and gravitational-wave sources may have experienced highly non-conservative mass transfer. In contrast, for low-mass accretors, the accretion timescale can exceed the thermal timescale by a larger factor without causing significant radial expansion., Comment: 12 pages, 6 figures. Accepted for publication in ApJ Letters

We apply the hierarchical probabilistic SED model BayeSN to analyse a sample of 475 SNe Ia (0.015 < z < 0.4) from Foundation, DES3YR and PS1MD to investigate the properties of dust in their host galaxies. We jointly infer the dust law $R_V$ population distributions at the SED level in high- and low-mass galaxies simultaneously with dust-independent, intrinsic differences. We find an intrinsic mass step of $-0.049\pm0.016$ mag, at a significance of 3.1$\sigma$, when allowing for a constant intrinsic, achromatic magnitude offset. We additionally apply a model allowing for time- and wavelength-dependent intrinsic differences between SNe Ia in different mass bins, finding $\sim$2$\sigma$ differences in magnitude and colour around peak and 4.5$\sigma$ differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean $R_V$ of $\sim$2$\sigma$ significance, demonstrating that both intrinsic and extrinsic differences may play a role in causing the host galaxy mass step. We also consider a model which allows the mean of the $R_V$ distribution to linearly evolve with redshift but find no evidence for any evolution - we infer the gradient of this relation $\eta_R = -0.38\pm0.70$. In addition, we discuss in brief a new, GPU-accelerated Python implementation of BayeSN suitable for application to large surveys which is publicly available and can be used for future cosmological analyses; this code can be found here: https://github.com/bayesn/bayesn., Comment: 24 pages, 8 figures, 3 tables. Accepted for publication in MNRAS. BayeSN code available at https://github.com/bayesn/bayesn

Stars approaching supermassive black holes can be tidally disrupted. Despite being expected to emit X-rays, TDEs have been largely observed in optical bands, which is poorly understood. In this Letter, we simulate the tidal disruption of a $1~M_\odot$ main sequence star on an eccentric ($e=0.95$) orbit with a periapsis distance one or five times smaller than the tidal radius ($\beta = 1$ or $5$) using general relativistic smoothed particle hydrodynamics. We follow the simulation for up to a year post-disruption. We show that accretion disks in eccentric TDEs are masked by unbound material outflowing at $\sim10,000~$km/s. Assuming electron scattering opacity, this material would be visible as a $\sim100~$au photosphere at $\sim10^4~$K, in line with observations of candidate TDEs., Comment: 11 pages, 5 figures, submitted to ApJL

In the context of the Laser Interferometer Space Antenna (LISA), the laser subsystems exhibit frequency fluctuations that introduce significant levels of noise into the measurements, surpassing the gravitational wave signal by several orders of magnitude. Mitigation is achieved via time-shifting individual measurements in a data processing step known as time-delay interferometry (TDI). The suppression performance of TDI relies on accurate knowledge and consideration of the delays experienced by the interfering lasers. While considerable efforts have been dedicated to the accurate determination of inter-spacecraft ranging delays, the sources for onboard delays have been either neglected or assumed to be known. Contrary to these assumptions, analog delays of the phasemeter front end and the laser modulator are not only large but also prone to change with temperature and heterodyne frequency. This motivates our proposal for a novel method enabling a calibration of these delays on-ground and in-space, based on minimal functional additions to the receiver architecture. Specifically, we establish a set of calibration measurements and elucidate how these measurements are utilized in data processing, leading to the mitigation of the delays in the TDI Michelson variables. Following a performance analysis of the calibration measurements, proposed calibration scheme is assessed through numerical simulations. We find that in the absence of the calibration scheme, the assumed drifts of the analog delays increase residual laser noise at high frequencies of the LISA measurement band. A single, on-ground calibration of the analog delays leads to an improvement by roughly one order of magnitude, while re-calibration in space may improve performance by yet another order of magnitude. Towards lower frequencies, ranging error is always found to be the limiting factor for which countermeasures are discussed., Comment: 14 pages, 6 figures

We present GausSN, a Bayesian semi-parametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). GausSN models the underlying light curve non-parametrically using a GP. Without assuming a template light curve for each SN type, GausSN fits for the time delays of all images using data in any number of wavelength filters simultaneously. We also introduce a novel time-varying magnification model to capture the effects of microlensing alongside time-delay estimation. In this analysis, we model the time-varying relative magnification as a sigmoid function, as well as a constant for comparison to existing time-delay estimation approaches. We demonstrate that GausSN provides robust time-delay estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST). We find that up to 43.6% of time-delay estimates from Roman and 52.9% from Rubin-LSST have fractional errors of less than 5%. We then apply GausSN to SN Refsdal and find the time delay for the fifth image is consistent with the original analysis, regardless of microlensing treatment. Therefore, GausSN maintains the level of precision and accuracy achieved by existing time-delay extraction methods with fewer assumptions about the underlying shape of the light curve than template-based approaches, while incorporating microlensing into the statistical error budget. GausSN is scalable for time-delay cosmography analyses given current projections of glSNe discovery rates from Rubin-LSST and Roman., Comment: 22 pages, 13 figures, published in MNRAS

As gravitational wave detectors improve, observations of black hole (BH) mergers will provide the joint distribution of their masses and spins. This will be a critical benchmark to validate formation scenarios. Merging binary BHs formed through isolated binary evolution require both components to be stripped of their hydrogen envelopes before core-collapse. The rotation rates of such stripped stars are constrained by their surface critical rotation, restricting their angular momentum content at core-collapse. We use stripped star models at low metallicities ($Z_\odot/10$, $Z_\odot/50$ and $Z_\odot/250$) to determine the spins of BHs produced by critically rotating stellar progenitors. To study how such progenitors can arise, we consider their formation through chemically homogeneous evolution (CHE). We use a semianalytical model to study the final spins of CHE binaries, and compare our results against available detailed population synthesis models. We find that above BH masses of $\simeq 25M_\odot$, the dimensionless spin of critically rotating stripped stars ($a = Jc/(GM^2$)) is below unity. This results in an exclusion region at high chirp masses and effective spins that cannot be populated by binary evolution. CHE can produce binaries where both BHs hit this limit, producing a pile-up at the boundary of the excluded region. Highly spinning BHs arise from very low-metallicity CHE systems with short delay times, which merge at higher redshifts. On the other hand, the contribution of CHE to merging binary BHs in the third observing run of the LVK collaboration is expected to be dominated by systems with low spins ($\chi_\mathrm{eff}<0.5$) which merge near redshift zero. Owing to its higher projected sensitivity and runtime, the fourth observing run of the LVK collaboration can potentially place constraints on the high spin population and the existence of a limit set by critical rotation., Comment: Submitted to A&A. Abstract abridged

Artificial general intelligence (AGI) does not yet exist, but given the pace of technological development in artificial intelligence, it is projected to reach human-level intelligence within roughly the next two decades. After that, many experts expect it to far surpass human intelligence and to do so rapidly. The prospect of superintelligent AGI poses an existential risk to humans because there is no reliable method for ensuring that AGI goals stay aligned with human goals. Drawing on publicly available forecaster and opinion data, the author examines how experts and non-experts perceive risk from AGI. The findings indicate that the perceived risk of a world catastrophe or extinction from AGI is greater than for other existential risks. The increase in perceived risk over the last year is also steeper for AGI than for other existential threats (e.g., nuclear war or human-caused climate change). That AGI is a pressing existential risk is something on which experts and non-experts agree, but the basis for such agreement currently remains obscure.

HEX-P is a probe-class mission concept that will combine high spatial resolution X-ray imaging ($<10"$ FWHM) and broad spectral coverage (0.2-80 keV) with an effective area far superior to current facilities' (including XMM-Newton and NuSTAR). These capabilities will enable revolutionary new insights into a variety of important astrophysical problems. We present scientific objectives and simulations of HEX-P observations of the Galactic Center (GC) and Bulge. We demonstrate the unique and powerful capabilities of the HEX-P observatory for studying both X-ray point sources and diffuse X-ray emission. HEX-P will be uniquely equipped to explore a variety of major topics in Galactic astrophysics, allowing us to (1) investigate broad-band properties of X-ray flares emitted from the supermassive black hole (BH) at Sgr A* and probe the associated particle acceleration and emission mechanisms; (2) identify hard X-ray sources detected by NuSTAR and determine X-ray point source populations in different regions and luminosity ranges; (3) determine the distribution of compact object binaries in the nuclear star cluster and the composition of the Galactic Ridge X-ray emission; (4) identify X-ray transients and measure fundamental parameters such as BH spin; (5) find hidden pulsars in the GC; (6) search for BH-OB binaries and hard X-ray flares from young stellar objects in young massive clusters; (7) measure white dwarf (WD) masses of magnetic CVs to deepen our understanding of CV evolution and the origin of WD magnetic fields; (8) explore primary particle accelerators in the GC in synergy with future TeV and neutrino observatories; (9) map out cosmic-ray distributions by observing non-thermal X-ray filaments; (10) explore past X-ray outbursts from Sgr A* through X-ray reflection components from giant molecular clouds.

Ultraluminous X-ray sources (ULXs) represent an extreme class of accreting compact objects: from the identification of some of the accretors as neutron stars to the detection of powerful winds travelling at 0.1-0.2 c, the increasing evidence points towards ULXs harbouring stellar-mass compact objects undergoing highly super-Eddington accretion. Measuring their intrinsic properties, such as the accretion rate onto the compact object, the outflow rate, the masses of accretor/companion -- hence their progenitors, lifetimes, and future evolution -- is challenging due to ULXs being mostly extragalactic and in crowded fields. Yet ULXs represent our best opportunity to understand super-Eddington accretion physics and the paths through binary evolution to eventual double compact object binaries and gravitational wave sources. Through a combination of end-to-end and single-source simulations, we investigate the ability of HEX-P to study ULXs in the context of their host galaxies and compare it to XMM-Newton and NuSTAR, the current instruments with the most similar capabilities. HEX-P's higher sensitivity, which is driven by its narrow point-spread function and low background, allows it to detect pulsations and broad spectral features from ULXs better than XMM-Newton and NuSTAR. We describe the value of HEX-P in understanding ULXs and their associated key physics, through a combination of broadband sensitivity, timing resolution, and angular resolution, which make the mission ideal for pulsation detection and low-background, broadband spectral studies., Comment: 24 pages, 9 figures. Accepted for publication in Frontiers of Astronomy and Space Science. Minor corrections included

Accurate knowledge of the interstellar medium (ISM) at high Galactic latitudes is crucial for future cosmic microwave background (CMB) polarization experiments due to extinction, albeit low, remaining a foreground larger than the anticipated signal in these regions. We develop a Bayesian model to identify a region of the Hertzsprung-Russell (HR) diagram suited to constrain the single-star extinction accurately at high Galactic latitudes. Using photometry from Gaia, 2MASS and ALLWISE together with parallax from Gaia, we employ nested sampling to fit the model to the data and analyse the posterior over stellar parameters for both synthetic and real data. Charting low variations in extinction is complex due to both systematic errors and degeneracies between extinction and other stellar parameters. The systematic errors can be minimised by restricting our data to a region of the HR diagram where the stellar models are most accurate. Moreover, the degeneracies can be significantly reduced by including astrophysical priors and spectroscopic constraints. We show accounting for the measurement error of the data and the assumed inaccuracies of the stellar models are critical in accurately recovering small variations in extinction. We compare our posterior to stellar parameters from the LAMOST and Gaia ESO spectroscopic surveys and demonstrate that a full posterior solution is necessary to understand both the extinction parameter and the effective temperature. We conclude by showing that under reasonable prior assumptions and using the posterior mean extinction for each star in a sample we can produce a dust map similar to other benchmark maps., Comment: 27 pages, 19 figures

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances; from the beginnings of inspirals that will venture into the ground-based detectors' view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA's discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome., Comment: 239 pages, 11 figures, white paper from the LISA Consortium Waveform Working Group, invited for submission to Living Reviews in Relativity, updated with comments from community

Large language models (LLMs) have become remarkably good at improving developer productivity for high-resource programming languages. These models use two kinds of data: large amounts of unlabeled code samples for pre-training and relatively smaller amounts of labeled code samples for fine-tuning or in-context learning. Unfortunately, many programming languages are low-resource, lacking labeled samples for most tasks and often even lacking unlabeled samples. Therefore, users of low-resource languages (e.g., legacy or new languages) miss out on the benefits of LLMs. Cross-lingual transfer uses data from a source language to improve model performance on a target language. It has been well-studied for natural languages, but has received little attention for programming languages. This paper reports extensive experiments on four tasks using a transformer-based LLM and 11 to 41 programming languages to explore the following questions. First, how well does cross-lingual transfer work for a given task across different language pairs. Second, given a task and target language, how should one choose a source language. Third, which characteristics of a language pair are predictive of transfer performance, and how does that depend on the given task. Our empirical study with 1,808 experiments reveals practical and scientific insights, such as Kotlin and JavaScript being the most transferable source languages and different tasks relying on substantially different features. Overall, we find that learning transfers well across several programming languages., Comment: 15 pages, 9 figures, 8 tables

These notes started to educate ourselves and to collect some background for our future work, with the hope that perhaps they will be useful to others also. Many if not all results are more or less elementary or available in the literature, but we need to fill some holes (which are undoubtely statements so trivial that the authors we use do not consider them holes at all) or make straightforward extensions, and then we do the proofs in sufficient detail for reference. Topics include random fields and stochastic processes as random elements in Hilbert spaces, Karhunen-Lo\`{e}ve explansion and random orthonormal series, laws of large numbers, white noise, convergence of the Ensemble Kalman Filter (EnKF), and the ensemble Kalman Transform Filter (ETKF)., Comment: 69 pages, 3 figures, 62 references

We prove optimal Fourier restriction estimates for the unit sphere in two-dimensional Euclidean space. Our results yield weighted versions of the 2D Restriction Conjecture involving the weight functions $(1+|x|)^\alpha(1+|y|)^\beta$ and $(1+|x|+|y|)^\gamma$ with $\alpha,\beta,\gamma\geq 0$.

The inner hundred parsecs of the Milky Way hosts the nearest supermassive black hole, largest reservoir of dense gas, greatest stellar density, hundreds of massive main and post main sequence stars, and the highest volume density of supernovae in the Galaxy. As the nearest environment in which it is possible to simultaneously observe many of the extreme processes shaping the Universe, it is one of the most well-studied regions in astrophysics. Due to its proximity, we can study the center of our Galaxy on scales down to a few hundred AU, a hundred times better than in similar Local Group galaxies and thousands of times better than in the nearest active galaxies. The Galactic Center (GC) is therefore of outstanding astrophysical interest. However, in spite of intense observational work over the past decades, there are still fundamental things unknown about the GC. JWST has the unique capability to provide us with the necessary, game-changing data. In this White Paper, we advocate for a JWST NIRCam survey that aims at solving central questions, that we have identified as a community: i) the 3D structure and kinematics of gas and stars; ii) ancient star formation and its relation with the overall history of the Milky Way, as well as recent star formation and its implications for the overall energetics of our galaxy's nucleus; and iii) the (non-)universality of star formation and the stellar initial mass function. We advocate for a large-area, multi-epoch, multi-wavelength NIRCam survey of the inner 100\,pc of the Galaxy in the form of a Treasury GO JWST Large Program that is open to the community. We describe how this survey will derive the physical and kinematic properties of ~10,000,000 stars, how this will solve the key unknowns and provide a valuable resource for the community with long-lasting legacy value., Comment: This White Paper will be updated when required (e.g. new authors joining, editing of content). Most recent update: 24 Oct 2023