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The Importance of Priors on LIGO-Virgo Parameter Estimation: the Case of Primordial Black Holes

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arxiv 2008.12320 v3 pith:6YWVXX4C submitted 2020-08-27 astro-ph.CO gr-qchep-th

The Importance of Priors on LIGO-Virgo Parameter Estimation: the Case of Primordial Black Holes

classification astro-ph.CO gr-qchep-th
keywords blackprimordialeventsholespriorsmassaccretionaffected
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The black holes detected by current and future interferometers can have diverse origins. Their expected mass and spin distributions depend on the specifics of the formation mechanisms. When a physically motivated prior distribution is used in a Bayesian inference, the parameters estimated from the gravitational-wave data can change significantly, potentially affecting the physical interpretation of certain gravitational-wave events and their implications on theoretical models. As a case study we analyze primordial black holes, which might be formed in the early universe and could comprise at least a fraction of the dark matter. If accretion is not efficient during their cosmic history, primordial black holes are expected to be almost non-spinning. If accretion is efficient, massive binaries tend to be symmetrical and highly spinning. We show that incorporating these priors can significantly change the inferred mass ratio and effective spin of some binary black hole events, especially those identified as high-mass, asymmetrical, or spinning by a standard analysis using agnostic priors. For several events, the Bayes factors are only mildly affected by the new priors, implying that it is hard to distinguish whether merger events detected so far are of primordial or astrophysical origin. In particular, if binaries identified by LIGO/Virgo as strongly asymmetrical (including GW190412) are of primordial origin, their mass ratio inferred from the data can be closer to unity. For GW190412, the latter property is strongly affected by the inclusion of higher harmonics in the waveform model.

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