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Abstract Detecting gravitational waves (GWs) from coalescing compact binaries has become routine with ground-based detectors like Advanced LIGO and Advanced Virgo. However, beyond standard sources such as binary black holes and neutron stars and neutron star black holes, no exotic sources revealing new physics have been discovered. Detecting ultracompact objects, such as subsolar mass (SSM), offers a promising opportunity to explore diverse astrophysical populations. However, searching for these objects using standard matched-filtering techniques is computationally intensive due to the dense parameter space involved. This increasing computational demand not only challenges current search methodologies but also poses a significant obstacle for third-generation (3G) ground-based GW detectors. In the 3G detectors, signals are expected to be observed for tens of minutes and detection rates to reach one per minute. This requires efficient search strategies to manage the computational load of long-duration signal search. In this paper, we demonstrate how hierarchical search strategies can address the computational challenges associated with detecting long-duration signals in current detectors and the 3G era. Using SSM searches as an example, we show that optimizing data sampling rates and adjusting the number of templates in matched filtering at each stage of low-frequency searches can improve the signal-to-noise ratio by 6% and detection volume by 10%–20%. This sensitivity improvement is achieved with a 2.5-fold reduction in computational time compared to standard PyCBC searches. We also discuss how this approach could be adapted and refined for searches involving eccentric and precessing binaries with future detectors.
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GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders
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.
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- Award ID(s):
- 2117997
- PAR ID:
- 10502419
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 5
- Issue:
- 2
- ISSN:
- 2632-2153
- Format(s):
- Medium: X Size: Article No. 025020
- Size(s):
- Article No. 025020
- Sponsoring Org:
- National Science Foundation
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