Probing The Dark Matter Halo of High-redshift Quasar from Wide-Field Clustering Analysis
Hao Meng, Huanian Zhang, Guangping Ye (HUST)

TL;DR
This study uses wide-field clustering analysis of high-redshift quasars to estimate their dark matter halo masses, bias parameters, and duty cycles, providing insights into black hole growth in the early universe.
Contribution
It presents the first clustering analysis of a large sample of high-redshift quasars using machine learning-selected candidates to estimate their halo properties and duty cycles.
Findings
Dark matter halo mass estimated at log(M_h/M_sun)=12.13-12.45
Bias parameter found to be approximately 15-24
Quasar duty cycle estimated at 0.0002-0.0021
Abstract
High-redshift quasars have been an excellent tracer to study the astrophysics and cosmology at early Universe. Using 577 spectroscopically confirmed high-redshift quasars and 1,796 highly reliable photometric quasar candidates (all with , median ) selected via machine learning, we perform wide-field clustering analyses to investigate the large-scale environment of these objects. We construct the projected auto correlation function of those high-redshift quasars that is weighted by its predicted probability of being a true high-redshift quasar, from which we derive the bias parameter and the typical dark matter halo mass of those quasars. The dark matter halo mass of quasars estimated from the projected auto correlation function is (), with the bias parameter of ($24.18 \pm…
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