Search for: All records

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.

 

In 2017, the LIGO and Virgo gravitational-wave (GW) detectors, in conjunction with electromagnetic (EM) astronomers, observed the first GW multimessenger astrophysical event, the binary neutron star (BNS) merger GW170817. This marked the beginning of a new era in multimessenger astrophysics. To discover further GW multimessenger events, we explore the synergies between the Transiting Exoplanet Survey Satellite (TESS) and GW observations triggered by the LIGO–Virgo–KAGRA Collaboration (LVK) detector network. TESS's extremely wide field of view (∼2300 deg²) means that it could overlap with large swaths of GW localizations, which often span hundreds of square degrees or more. In this work, we use a recently developed transient detection pipeline to search TESS data collected during the LVK’s third observing run, O3, for any EM counterparts. We find no obvious counterparts brighter than about 17th magnitude in the TESS bandpass. Additionally, we present end-to-end simulations of BNS mergers, including their detection in GWs and simulations of light curves, to identify TESS's kilonova discovery potential for the LVK's next observing run (O4). In the most optimistic case, TESS will observe up to one GW-found BNS merger counterpart per year. However, TESS may also find up to five kilonovae that did not trigger the LVK network, emphasizing that EM-triggered GW searches may play a key role in future kilonova detections. We also discuss how TESS can help place limits on EM emission from binary black hole mergers and rapidly exclude large sky areas for poorly localized GW events.

 

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. 

The Wide-Field Infrared Transient Explorer (WINTER) is a new 1 deg²seeing-limited time-domain survey instrument designed for dedicated near-infrared follow-up of kilonovae from binary neutron star (BNS) and neutron star–black hole mergers. WINTER will observe in the near-infraredY,J, and short-Hbands (0.9–1.7μm, toJ_AB= 21 mag) on a dedicated 1 m telescope at Palomar Observatory. To date, most prompt kilonova follow-up has been in optical wavelengths; however, near-infrared emission fades more slowly and depends less on geometry and viewing angle than optical emission. We present an end-to-end simulation of a follow-up campaign during the fourth observing run (O4) of the LIGO, Virgo, and KAGRA interferometers, including simulating 625 BNS mergers, their detection in gravitational waves, low-latency and full parameter estimation skymaps, and a suite of kilonova lightcurves from two different model grids. We predict up to five new kilonovae independently discovered by WINTER during O4, given a realistic BNS merger rate. Using a larger grid of kilonova parameters, we find that kilonova emission is ≈2 times longer lived and red kilonovae are detected ≈1.5 times further in the infrared than in the optical. For 90% localization areas smaller than 150 (450) deg², WINTER will be sensitive to more than 10% of the kilonova model grid out to 350 (200) Mpc. We develop a generalized toolkit to create an optimal BNS follow-up strategy with any electromagnetic telescope and present WINTER’s observing strategy with this framework. This toolkit, all simulated gravitational-wave events, and skymaps are made available for use by the community.