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Published May 15, 2023 | Published
Journal Article Open

Accelerating tests of general relativity with gravitational-wave signals using hybrid sampling

Abstract

The Advanced LIGO/Virgo interferometers have observed ∼100 gravitational-wave transients enabling new questions to be answered about relativity, astrophysics, and cosmology. However, many of our current procedures for computing these constraints will not scale well with the increased size of future transient catalogs. We introduce a novel hybrid sampling method in order to more efficiently perform parameterized tests of general relativity with gravitational-wave signals. Applying our method to the binary black hole merger GW150914 and simulated signals we find that our method is approximately an order of magnitude more efficient than the current method with conservative settings for our hybrid analysis. While we have focused on the specific problem of measuring potential deviations from relativity, our method is of much wider applicability to any problem that can be decomposed into a simple and more complex model(s).

Additional Information

© 2023 American Physical Society. We thank Sylvia Biscoveanu, Max Isi, Nathan Johnson-McDaniel, Ralph Smith, Salvatore Vitale, and Alan Weinstein for helpful discussions and comments. C. T. is supported by an MKI Kavli Fellowship. J. G. is supported by Grants No. PHY-1764464 and No. 2207758. N. W. acknowledges support from the National Science Foundation (NSF) and the Park Scholarships program at NC State. We are grateful to the LIGO Caltech SURF program where this project began which is supported by the NSF REU program. 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 computing resources provided by the California Institute of Technology and supported by National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. The analysis in this work made use of data available from the Gravitational Wave Open Science Center [81]. This analysis used the following software: numpy [82], scipy [83], matplotlib [84], corner.py [85], pandas [86,87], lalsimulation [65], bilby [45,47], dynesty [46], ptemcee [49]. We provide analysis scripts, notebooks, and some data.

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Published - PhysRevD.107.104056.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 20, 2023