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You Can’t Always Get What You Want: The Impact of Prior Assumptions on Interpreting GW190412

2020/06/19 by Michael Zevin, Christopher P. L. Berry, Scott Coughlin +2 · 1 voice
Physics and Astronomy · #Astrophysical Phenomena and Observations #Bayesian inference #Binary black hole #Binary number #Black hole (networking) #Dimensionless quantity #Gamma-ray bursts and supernovae #Inference #Prior probability #Pulsars and Gravitational Waves Research #Robustness (evolution) #astro-ph.HE #astro-ph.SR #gr-qc

paper · pdf · doi:10.3847/2041-8213/aba8ef

published as The Astrophysical Journal Letters 899, L17 (2020) · 12 pages, 2 figures, 1 table, published in ApJL

arxiv published 2020/06/19 · openalex created_date 2020/06/25 · openalex publication_date 2020/08/01 · arxiv created 2020/08/11 · arxiv updated 2020/08/13 · openalex updated_date 2026/08/06

Abstract

Abstract GW190412 is the first observation of a black hole binary with definitively unequal masses. GW190412's mass asymmetry, along with the measured positive effective inspiral spin, allowed for inference of a component black hole spin: the primary black hole in the system was found to have a dimensionless spin magnitude between and (90% credible range). We investigate how the choice of priors for the spin magnitudes and tilts of the component black holes affect the robustness of parameter estimates for GW190412, and report Bayes factors across a suite of prior assumptions. Depending on the waveform family used to describe the signal, we find either marginal to moderate ( :1– :1) or strong (≳ :1) support for the primary black hole being spinning compared to cases where only the secondary is allowed to have spin. We show how these choices influence parameter estimates, and find the asymmetric masses and positive effective inspiral spin of GW190412 to be qualitatively, but not quantitatively, robust to prior assumptions. Our results highlight the importance of both considering astrophysically motivated or population-based priors in interpreting observations and considering their relative support from the data.

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