The Accretion History of AGN I: Supermassive Black Hole Population Synthesis Model

TL;DR

This paper develops a neural network-based population synthesis model that accurately reproduces observed X-ray properties of AGN, revealing that about half of all AGN are Compton-thick within certain redshifts.

Contribution

It introduces a novel neural network approach to fit an X-ray luminosity function that matches multiple observational constraints, improving upon previous models.

Findings

Approximately 50% of AGN within z ≈ 0.1 are Compton-thick.

The model successfully reproduces X-ray number counts and spectra.

Existing XLFs cannot simultaneously fit all observed X-ray constraints.

Abstract

As matter accretes onto the central supermassive black holes in active galactic nuclei (AGN), X-rays are emitted. We present a population synthesis model that accounts for the summed X-ray emission from growing black holes; modulo the efficiency of converting mass to X-rays, this is effectively a record of the accreted mass. We need this population synthesis model to reproduce observed constraints from X-ray surveys: the X-ray number counts, the observed fraction of Compton-thick AGN [$\log$ (N$_{\rm H}$/cm$^{\rm -2}$) $>$ 24] and the spectrum of the cosmic X-ray background (CXB), after accounting for selection biases. Over the past decade, X-ray surveys by {\it XMM-Newton}, {\it Chandra}, \textit{NuSTAR} and \textit{Swift}-BAT have provided greatly improved observational constraints. We find that no existing X-ray luminosity function (XLF) consistently reproduces all these observations. We take the uncertainty in AGN spectra into account, and use a neural network to compute an XLF that fits all observed constraints, including observed Compton-thick number counts and fractions. This new population synthesis model suggests that, intrinsically, 50$\pm$9\% (56$\pm$7\%) of all AGN within z $\simeq$ 0.1 (1.0) are Compton-thick.