A Rapidly Varying Red Supergiant X-Ray Binary in the Galactic Center

We analyzed multiwavelength observations of the previously identified Galactic center X-ray binary CXO 174528.79–290942.8 (XID 6592) and determine that the near-infrared counterpart is a red supergiant based on its spectrum and luminosity. Scutum X-1 is the only previously known X-ray binary with a red supergiant donor star and closely resembles XID 6592 in terms of X-ray luminosity (L X), absolute magnitude, and IR variability (L IR,var), supporting the conclusion that XID 6592 contains a red supergiant donor star. The XID 6592 infrared counterpart shows variability of ~0.5 mag in the Wide-field Infrared Survey Explorer-1 band (3.4 μm) on timescales of a few hours. Other infrared data sets also show large-amplitude variability from this source at earlier epochs but do not show significant variability in recent data. We do not expect red supergiants to vary by ~50% in luminosity over these short timescales, indicating that the variability should be powered by the compact object. However, the X-ray luminosity of this system is typically ~1000× less than the variable luminosity in the infrared and falls below the Chandra detection limit. While X-ray reprocessing can produce large-amplitude fast infrared variability, it typically requires LX >> LIR,var to do so, indicating that another process must be at work. We suggest that this system may be a supergiant fast X-ray transient (SFXT), and that a large (~1038 ergs s−1), fast (102-4 s) X-ray flare could explain the rapid IR variability and lack of a long-lasting X-ray outburst detection. SFXTs are normally associated with blue supergiant companions, so if confirmed, XID 6592 would be the first red supergiant SFXT, as well as the second X-ray red supergiant binary.A.M. acknowledges support from the Generalitat Valenciana through the grant BEST/2015/242 and from the Ministerio de Educación, Cultura y Deporte through the grant PRX15/00030

Low-Cost Access to the Deep, High-Cadence Sky: the Argus Optical Array

New mass-produced, wide-field, small-aperture telescopes have the potential to revolutionize ground-based astronomy by greatly reducing the cost of collecting area. In this paper, we introduce a new class of large telescope based on these advances: an all-sky, arcsecond-resolution, 1000-telescope array which builds a simultaneously high-cadence and deep survey by observing the entire sky all night. As a concrete example, we describe the Argus Array, a 5m-class telescope with an all-sky field of view and the ability to reach extremely high cadences using low-noise CMOS detectors. Each 55 GPix Argus exposure covers 20% of the entire sky to g=19.6 each minute and g=21.9 each hour; a high-speed mode will allow sub-second survey cadences for short times. Deep coadds will reach g=23.6 every five nights over 47% of the sky; a larger-aperture array telescope, with an \'etendue close to the Rubin Observatory, could reach g=24.3 in five nights. These arrays can build two-color, million-epoch movies of the sky, enabling sensitive and rapid searches for high-speed transients, fast-radio-burst counterparts, gravitational-wave counterparts, exoplanet microlensing events, occultations by distant solar system bodies, and myriad other phenomena. An array of O(1,000) telescopes, however, would be one of the most complex astronomical instruments yet built. Standard arrays with hundreds of tracking mounts entail thousands of moving parts and exposed optics, and maintenance costs would rapidly outpace the mass-produced-hardware cost savings compared to a monolithic large telescope. We discuss how to greatly reduce operations costs by placing all optics in a thermally controlled, sealed dome with a single moving part. Coupled with careful software scope control and use of existing pipelines, we show that the Argus Array could become the deepest and fastest Northern sky survey, with total costs below $20M.Comment: 17 pages, 5 figures, 2 table

The Gravitational-wave Optical Transient Observer (GOTO)

The Gravitational-wave Optical Transient Observer (GOTO) is a wide-field telescope project focused on detecting optical counterparts to gravitational-wave sources. Each GOTO robotic mount holds eight 40 cm telescopes, giving an overall field of view of 40 square degrees. As of 2022 the first two GOTO mounts have been commissioned at the Roque de los Muchachos Observatory on La Palma, Canary Islands, and construction of the second node with two additional 8-telescope mounts has begin at Siding Spring Observatory in New South Wales, Australia. Once fully operational each GOTO mount will be networked to form a robotic, multi-site observatory, which will survey the entire visible sky every two nights and enable rapid follow-up detections of transient sources

BILBY:A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy

Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. It is the method by which gravitational-wave data is used to infer the sources' astrophysical properties. We introduce a user-friendly Bayesian inference library for gravitational-wave astronomy, Bilby. This Python code provides expert-level parameter estimation infrastructure with straightforward syntax and tools that facilitate use by beginners. It allows users to perform accurate and reliable gravitational-wave parameter estimation on both real, freely available data from LIGO/Virgo and simulated data. We provide a suite of examples for the analysis of compact binary mergers and other types of signal models, including supernovae and the remnants of binary neutron star mergers. These examples illustrate how to change the signal model, implement new likelihood functions, and add new detectors. Bilby has additional functionality to do population studies using hierarchical Bayesian modeling. We provide an example in which we infer the shape of the black hole mass distribution from an ensemble of observations of binary black hole mergers

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 mergers 4–6 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

Quantitative modelling of type Ia supernovae spectral time series : constraining the explosion physics

Multiple explosion mechanisms have been proposed to explain type Ia supernovae (SNe Ia). Empirical modelling tools have also been developed that allow for fast, customised modelling of individual SNe and direct comparisons between observations and explosion model predictions. Such tools have provided useful insights, but the subjective nature with which empirical modelling is performed makes it difficult to obtain robust constraints on the explosion physics or expand studies to large populations of objects. Machine learning accelerated tools have therefore begun to gain traction. In this paper, we present riddler, a framework for automated fitting of SNe Ia spectral sequences up to shortly after maximum light. We train a series of neural networks on realistic ejecta profiles predicted by the W7 and N100 explosion models to emulate full radiative transfer simulations and apply nested sampling to determine the best-fitting model parameters for multiple spectra of a given SN simultaneously. We show that riddler is able to accurately recover the parameters of input spectra and use it to fit observations of two well-studied SNe Ia. We also investigate the impact of different weighting schemes when performing quantitative spectral fitting and show that best-fitting models and parameters are highly dependent on the assumed weighting schemes and priors. As spectroscopic samples of SNe Ia continue to grow, automated spectral fitting tools such as riddler will become increasingly important to maximise the physical constraints that can be gained in a quantitative and consistent manner

Gravitational-wave Constraints on the Equatorial Ellipticity of Millisecond Pulsars

We present a search for continuous gravitational waves from five radio pulsars, comprising three recycled pulsars (PSR J0437-4715, PSR J0711-6830, and PSR J0737-3039A) and two young pulsars: the Crab pulsar (J0534+2200) and the Vela pulsar (J0835-4510). We use data from the third observing run of Advanced LIGO and Virgo combined with data from their first and second observing runs. For the first time, we are able to match (for PSR J0437-4715) or surpass (for PSR J0711-6830) the indirect limits on gravitational-wave emission from recycled pulsars inferred from their observed spin-downs, and constrain their equatorial ellipticities to be less than 10-8. For each of the five pulsars, we perform targeted searches that assume a tight coupling between the gravitational-wave and electromagnetic signal phase evolution. We also present constraints on PSR J0711-6830, the Crab pulsar, and the Vela pulsar from a search that relaxes this assumption, allowing the gravitational-wave signal to vary from the electromagnetic expectation within a narrow band of frequencies and frequency derivatives

Enhancing Gravitational-Wave Science with Machine Learning

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors