We present Quasarnet, a novel research platform that enables deployment of
data-driven modeling techniques for the investigation of the properties of
super-massive black holes. Black hole data sets — observations and simulations
— have grown rapidly in the last decade in both complexity and abundance.
However, our computational environments and tool sets have not matured
commensurately to exhaust opportunities for discovery with these data. Our
pilot study presented here is motivated by one of the fundamental open
questions in understanding black hole formation and assembly across cosmic time
– the nature of the black hole host galaxy and parent dark matter halo
connection. To explore this, we combine and co-locate large, observational data
sets of quasars, the high-redshift luminous population of accreting black
holes, at z > 3 alongside simulated data spanning the same cosmic epochs in
Quasarnet. We demonstrate the extraction of the properties of observed quasars
and their putative dark matter parent halos that permit studying their
association and correspondence. In this paper, we describe the design,
implementation, and operation of the publicly queryable Quasarnet database and
provide examples of query types and visualizations that can be used to explore
the data. Starting with data collated in Quasarnet, which will serve as
training sets, we plan to utilize machine learning algorithms to predict
properties of the as yet undetected, less luminous quasar population. To that
ultimate goal, here we present the first key step in building the
BH-galaxy-halo connection that underpins the formation and evolution of
supermassive black holes. All our codes and compiled data are available on the
public Google Kaggle Platform.
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