QUOTAS: A new research platform for the data-driven investigation of black holes
Priyamvada Natarajan (1), Kwok Sun Tang (2), Robert McGibbon (3),, Sadegh Khochfar (3), Brian Nord (4,5,6), Steinn Sigurdsson (7), Joe Tricot, (8), Nico Cappelluti (9), Daniel George (8), Jack Hidary (8) ((1), Department of Astronomy, Yale University, New Haven CT, USA

TL;DR
QUOTAS is a new platform that uses machine learning to analyze and compare observational and simulated super-massive black hole data, revealing gaps in current models and aiding in survey planning.
Contribution
The paper introduces QUOTAS, a novel data-driven platform that integrates machine learning with black hole research, enabling large-scale analysis and model testing.
Findings
Simulated SMBHs match observed mass functions and clustering.
Current models do not reproduce SMBH growth rates.
ML helps optimize survey strategies for faint quasars.
Abstract
We present QUOTAS, a novel research platform for the data-driven investigation of super-massive black hole (SMBH) populations. While SMBH data sets -- observations and simulations -- have grown rapidly in complexity and abundance, our computational environments and analysis tools have not matured commensurately to exhaust opportunities for discovery. Motivated to explore BH host galaxy and the parent dark matter halo connection, in this pilot version of QUOTAS, we assemble and co-locate the high-redshift, luminous quasar population at alongside simulated data of the same epochs. Leveraging machine learning algorithms (ML) we expand simulation volumes that successfully replicate halo populations beyond the training set. Training ML on the Illustris-TNG300 simulation that includes baryonic physics, we populate the larger LEGACY Expanse dark matter-only box with quasars. Our…
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Taxonomy
TopicsStatistics Education and Methodologies · Computational Physics and Python Applications
