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Using Entropy to Analyze LIGO Black Holes

ORAL

Abstract

By connecting gravitational waves and entropy with the first law of black hole mechanics, we can obtain the entropy of black holes from LIGO-Virgo-KAGRA (LVK) data. We impose thermodynamical constraints based on General Relativity (GR) and the second law of thermodynamics that mergers between two black holes, as an irreversible natural process, will increase the entropy of the universe. We construct a novel framework called BRAHMA to infer the properties and astrophysical implications of binary black hole mergers in LVK. We apply the framework to 10 binary black hole merger events reported by LVK Collaboration. In doing so, we obtain new strong astrophysical insights into the origins of black holes for GW190521 and GW191109, two of the heaviest black hole merger events. For GW191109, the highly negative effective spin does not comply with the uniform distribution of circular binary black hole mergers. The uniform prior imposed increases the effective spin to ~0.2, which is consistent with most gravitational wave black hole populations. Constraints from GR only increase the effective spin slightly, showing black holes with highly negative effective spin still comply with GR. By imposing the constraints from entropy, we also independently prove the existence of black holes inside the pair-instability supernovae mass gap.

Meanwhile, we also perform a systematic investigation of the consistency between phenomenological waveform and ringdown models. Although the NRSur7dq4 waveform generated from numerical relativity is not available for all events, it is proved to be most consistent with entropy. Kerr221 was the most consistent ringdown model across all events. To ensure that every dataset has over 10% of data remaining after imposing the constraints, a filtering cut between 75% and 80% is discovered to be optimal, which is significantly lower than the 90% confidence interval of LIGO detection.

Presenters

  • Siyuan Chen

    Vanderbilt University

Authors

  • Siyuan Chen

    Vanderbilt University

  • Karan Jani

    Vanderbilt University