Priyamvada Natarajan Department of Astronomy, Yale University, New Haven CT, USA, Kwok Sun Tang Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL, USA, Robert McGibbon Institute for Astronomy & Royal Observatory, University of Edinburgh, Blackford Hill, Edinburgh, UK, Sadegh Khochfar Institute for Astronomy & Royal Observatory, University of Edinburgh, Blackford Hill, Edinburgh, UK, Brian Nord Fermi National Accelerator Laboratory, Batavia, IL, USA Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL Department of Astronomy & Astrophysics, University of Chicago, IL, Steinn Sigurdsson Department of Astronomy & Astrophysics and Institute for Gravitation and the Cosmos, Pennsylvania State University, University Park, PA, Joe Tricot Sandbox@Alphabet, Mountain View, CA, USA, Nico Cappelluti Department of Physics, University of Miami, Coral Gables, FL, USA, Daniel George Sandbox@Alphabet, Mountain View, CA, USA, Jack Hidary Sandbox@Alphabet, Mountain View, CA, USA

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 $z \geq 3$ 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 first science results comparing observational and ML simulated quasars at $z \sim 3$, reveal that while the recovered Black Hole Mass Functions and clustering are in good agreement, simulated SMBHs fail to accrete, shine and grow at high enough rates to match observed quasars. We conclude that sub-grid models of mass accretion and SMBH feedback implemented in Illustris-TNG300 do not reproduce their observed mass growth. QUOTAS, demonstrates the power of ML, both for analyzing large complex datasets, and offering a unique opportunity to interrogate our theoretical model assumptions. We deploy ML again to derive and devise an optimal survey strategy for bringing the undetected lower luminosity quasar population into view. QUOTAS, and all related materials are publicly available at the Google Kaggle platform.