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Abstract The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies (O(1s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context.However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial.Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes. 

Abstract The observation of transient gravitational waves (GWs) is hindered by the presence of transient noise, colloquially referred to as glitches. These glitches can often be misidentified as GWs by searches for unmodeled transients using the excess-power type of methods and sometimes even excite template waveforms for compact binary coalescences while using matched filter techniques. They thus create a significant background in the searches. This background is more critical in getting identified promptly and efficiently within the context of real-time searches for GW transients. Such searches are the ones that have enabled multi-messenger astrophysics with the start of the Advanced LIGO and Advanced Virgo data taking in 2015 and they will continue to enable the field for further discoveries. With this work we propose and demonstrate the use of a signal-based test that quantifies the fidelity of the time-frequency decomposition of the putative signal based on first principles on how astrophysical transients are expected to be registered in the detectors and empirically measuring the instrumental noise. It is based on the Q-transform and a measure of the occupancy of the corresponding time-frequency pixels over select time-frequency volumes; we call it ‘QoQ’. Our method shows a 40% reduction in the number of retraction of public alerts that were issued by the LIGO-Virgo-KAGRA collaborations during the third observing run with negligible loss in sensitivity. Receiver Operator Characteristic measurements suggest the method can be used in online and offline searches for transients, reducing their background significantly. 

Abstract Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name‘Gravitational Wave Anomalous Knowledge’(GWAK). While the semi-supervised approach to this problem entails a potential reduction in accuracy compared to fully supervised methods, it offers a generalizability advantage by enhancing the reach of experimental sensitivity beyond the constraints of pre-defined signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources. 

An advanced LIGO and Virgo’s third observing run brought another binary neutron star merger (BNS) and the first neutron-star black hole mergers. While no confirmed kilonovae were identified in conjunction with any of these events, continued improvements of analyses surrounding GW170817 allow us to project constraints on the Hubble Constant (H₀), the Galactic enrichment fromr-process nucleosynthesis, and ultra-dense matter possible from forthcoming events. Here, we describe the expected constraints based on the latest expected event rates from the international gravitational-wave network and analyses of GW170817. We show the expected detection rate of gravitational waves and their counterparts, as well as how sensitive potential constraints are to the observed numbers of counterparts. We intend this analysis as support for the community when creating scientifically driven electromagnetic follow-up proposals. During the next observing run O4, we predict an annual detection rate of electromagnetic counterparts from BNS of ${0.43}_{-0.26}^{+0.58}$( ${1.97}_{-1.2}^{+2.68}$) for the Zwicky Transient Facility (Rubin Observatory). 

Abstract We present SN 2023zaw—a subluminous (M_r= −16.7 mag) and rapidly evolving supernova (t_1/2,r= 4.9 days), with the lowest nickel mass (≈0.002M_⊙) measured among all stripped-envelope supernovae discovered to date. The photospheric spectra are dominated by broad Heiand Ca near-infrared emission lines with velocities of ∼10,000−12,000 km s⁻¹. The late-time spectra show prominent narrow Heiemission lines at ∼1000 km s⁻¹, indicative of interaction with He-rich circumstellar material. SN 2023zaw is located in the spiral arm of a star-forming galaxy. We perform radiation-hydrodynamical and analytical modeling of the lightcurve by fitting with a combination of shock-cooling emission and nickel decay. The progenitor has a best-fit envelope mass of ≈0.2M_☉and an envelope radius of ≈50R_⊙. The extremely low nickel mass and low ejecta mass (≈0.5M_⊙) suggest an ultrastripped SN, which originates from a mass-losing low-mass He-star (zero-age main-sequence mass < 10M_⊙) in a close binary system. This is a channel to form double neutron star systems, whose merger is detectable with LIGO. SN 2023zaw underscores the existence of a previously undiscovered population of extremely low nickel mass (<0.005M_☉) stripped-envelope supernovae, which can be explored with deep and high-cadence transient surveys. 

Abstract The multi-messenger detection of the gravitational-wave signal GW170817, the corresponding kilonova AT2017gfo and the short gamma-ray burst GRB170817A, as well as the observed afterglow has delivered a scientific breakthrough. For an accurate interpretation of all these different messengers, one requires robust theoretical models that describe the emitted gravitational-wave, the electromagnetic emission, and dense matter reliably. In addition, one needs efficient and accurate computational tools to ensure a correct cross-correlation between the models and the observational data. For this purpose, we have developed the Nuclear-physics and Multi-Messenger Astrophysics framework NMMA. The code allows incorporation of nuclear-physics constraints at low densities as well as X-ray and radio observations of isolated neutron stars. In previous works, the NMMA code has allowed us to constrain the equation of state of supranuclear dense matter, to measure the Hubble constant, and to compare dense-matter physics probed in neutron-star mergers and in heavy-ion collisions, and to classify electromagnetic observations and perform model selection. Here, we show an extension of the NMMA code as a first attempt of analyzing the gravitational-wave signal, the kilonova, and the gamma-ray burst afterglow simultaneously. Incorporating all available information, we estimate the radius of a 1.4M_⊙neutron star to be$$R=11.9{8}_{-0.40}^{+0.35}$$ $R=11.98_{-0.40}^{+0.35}$km. 

The recent application of neural network algorithms to problems in gravitational-wave physics invites the study of how best to build production-ready applications on top of them. By viewing neural networks not as standalone models, but as components or functions in larger data processing pipelines, we can apply lessons learned from both traditional software development practices as well as successful deep learning applications from the private sector. This paper highlights challenges presented by straightforward but naïve deployment strategies for deep learning models, and identifies solutions to them gleaned from these sources. It then presents HERMES, a library of tools for implementing these solutions, and describes how HERMES is being used to develop a particular deep learning application which will be deployed during the next data collection run of the International Gravitational-Wave Observatories. 

Abstract Using the Zwicky Transient Facility, in 2021 February we identified the first known outburst of the black hole X-ray transient XTE J1859+226 since its discovery in 1999. The outburst was visible at X-ray, UV, and optical wavelengths for less than 20 days, substantially shorter than its full outburst of 320 days in 1999, and the observed peak luminosity was 2 orders of magnitude lower. Its peak bolometric luminosity was only 2 × 10³⁵erg s⁻¹, implying an Eddington fraction of about 3 × 10⁻⁴. The source remained in the hard spectral state throughout the outburst. From optical spectroscopy measurements we estimate an outer disk radius of 10¹¹cm. The low observed X-ray luminosity is not sufficient to irradiate the entire disk, but we observe a surprising exponential decline in the X-ray light curve. These observations highlight the potential of optical and infrared synoptic surveys to discover low-luminosity activity from X-ray transients.