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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 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. 

ABSTRACT We present a comprehensive, configurable open-source software framework for estimating the rate of electromagnetic detection of kilonovae (KNe) associated with gravitational wave detections of binary neutron star (BNS) mergers. We simulate the current LIGO-Virgo-KAGRA (LVK) observing run (O4) using current sensitivity and uptime values as well as using predicted sensitivites for the next observing run (O5). We find the number of discoverable kilonovae during LVK O4 to be $${ 1}_{- 1}^{+ 4}$$ or $${ 2 }_{- 2 }^{+ 3 }$$, (at 90 per cent confidence) depending on the distribution of NS masses in coalescing binaries, with the number increasing by an order of magnitude during O5 to $${ 19 }_{- 11 }^{+ 24 }$$. Regardless of mass model, we predict at most five detectable KNe (at 95 per cent confidence) in O4. We also produce optical and near-infrared light curves that correspond to the physical properties of each merging system. We have collated important information for allocating observing resources for search and follow-up observations, including distributions of peak magnitudes in several broad-bands and time-scales for which specific facilities can detect each KN. The framework is easily adaptable, and new simulations can quickly be produced in response to updated information such as refined merger rates and NS mass distributions. Finally, we compare our suite of simulations to the thus-far completed portion of O4 (as of 2023, October 14), finding a median number of discoverable KNe of 0 and a 95 percentile upper limit of 2, consistent with no detections so far in O4.