Impact of selection biases on tests of general relativity with gravitational-wave inspirals

Creators: Magee, Ryan; Isi, Maximiliano; Payne, Ethan; Chatziioannou, Katerina¹; Farr, Will M.; Pratten, Geraint; Vitale, Salvatore

1. California Institute of Technology

Abstract

Tests of general relativity with gravitational-wave observations from merging compact binaries continue to confirm Einstein’s theory of gravity with increasing precision. However, these tests have so far been applied only to signals that were first confidently detected by matched-filter searches assuming general relativity templates. This raises the question of selection biases: What is the largest deviation from general relativity that current searches can detect, and are current constraints on such deviations necessarily narrow because they are based on signals that were detected by templated searches in the first place? In this paper, we estimate the impact of selection effects for tests of the inspiral phase evolution of compact binary signals with a simplified version of the gstlal search pipeline. We find that selection biases affect the search for very large values of the deviation parameters, much larger than the constraints implied by the detected signals. Therefore, combined population constraints from confidently detected events are mostly unaffected by selection biases, with the largest effect being a broadening at the $\sim 10 %$ level for the $- 1 PN$ term. These findings suggest that current population constraints on the inspiral phase are robust without factoring in selection biases. Our study does not rule out a disjoint, undetectable binary population with large deviations from general relativity or stronger selection effects in other tests or search procedures.

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Acknowledgement

We thank Reed Essick for helpful discussions. We thank Leo Tsukada for discussions on gstlal. The Flatiron Institute is funded by the Simons Foundation. K. C. was supported by National Science Foundation (NSF) Grant No. PHY-2110111. G. P. gratefully acknowledges support from Royal Society University Research Fellowship No. URF\R1\221500 and No. RF\ERE\221015. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under Cooperative Agreement No. PHY-1764464. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center, a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by NSF Grants No. PHY-0757058 and No. PHY-0823459. This research has made use of data or software obtained from the Gravitational Wave Open Science Center, a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This paper carries LIGO Document No. LIGO-P2300381. The filtering was performed with the gstlal library [44–47], built on the lalsuite software library [78]. Our hierarchical analysis utilizes numpyro [79,80], JAX [81], astropy [82–84], numpy [85], and scipy [86]. The plots shown in this work use matplotlib [87], seaborn [88], arViz [89], and corner [90].

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