A cutting-edge Bayesian model for the observed distribution of Supermassive Black Hole spins drawn from X-ray reflection.

Understanding the evolution of Supermassive Black Hole (SMBH) growth across cosmic time and mass scales is a long-lasting aim of modern astronomy. According to the no-hair theorem of General Relativity, at a fundamental level, astrophysical black holes are characterised by their mass (M) and spin. X-ray reflection and reverberation methods have thus crucially provided spin constraints for dozens of SMBHs in the range log(M/M_Sun) = 6 - 10 (Bambi+21). The high spins of most low-mass SMBHs in the observed population suggest they grow via coherent accretion, whilst the most massive SMBHs, with moderate upper spin bounds, may grow through incoherent accretion and/or SMBH-SMBH mergers. I will first present a Bayesian study of SMBH spin in which we infer model parameters for a phenomenological spin distribution function from the observational estimates. I will then introduce how such a distribution varies over mass scales, including the low-mass SMBH spin estimates from Mallick+22. I will then discuss our findings in the context of what drives SMBH growth over cosmic timescales by comparing with existing semi-analytic and numerical models of galaxy and SMBH growth and co-evolution. Importantly, our Bayesian model includes several spin-dependent observational biases that affect our interpretation of the observed distribution, including the expected overrepresentation of SMBHs with high-to-maximal spins due flux-limiting effects. Finally, I will discuss the applicability of our model in preparation for upcoming spin surveys with future X-ray observatories, including the AXIS probe-class X-ray mission being proposed for a 2032 launch, whose deep survey (7 Ms) will complement the view of SMBH growth expected to be unveiled with next-generation gravitational-wave observatories such as LISA.