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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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Bayesian inference for gravitational waves from binary neutron star mergers in third-generation observatories
Third-generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced order models for ∼90minute long gravitational-wave signals, covering the observing band (5−2048Hz), speeding up inference by a factor of ∼1.3×10^4 compared to the calculation times without reduced order models. The reduced order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to the Earth's rotation, and spin-induced orbital precession. We show how reduced order modeling can accelerate inference on data containing multiple, overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories.
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- Award ID(s):
- 1836814
- PAR ID:
- 10279716
- Date Published:
- Journal Name:
- Physical review letters
- ISSN:
- 1092-0145
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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