QUOTAS: A new research platform for the data-driven investigation of black holes



Priyamvada Natarajan Department of Astronomy, Yale University, New Haven CT, USA, Kwok Sun Tang UIUC, Robert McGibbon, University of Edinburgh, Sadegh Khochfar University of Edinburgh, Brian Nord FNAL and KICP, Steinn Sigurdsson Penn State University, Joe Tricot [email protected], Nico Cappelluti University of Miami, Daniel George [email protected], Jack Hidary [email protected]


We present QUOTAS, a novel research platform for the data-driven investigation of super-massive black hole populations. While supermassive black hole 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 black hole 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 we expand simulation volumes that successfully replicate halo populations beyond the training set. Training machine learning algorithms 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 machine learning simulated quasars at $z \sim 3$, reveal that while the recovered Black Hole Mass Functions and clustering are in good agreement, simulated supermassive black holes fail to accrete, shine and grow at high enough rates to match observed quasars. We conclude that sub-grid models of mass accretion and supermassive black hole feedback implemented in Illustris-TNG300 do not reproduce their observed mass growth. QUOTAS, demonstrates the power of machine learning, both for analyzing large complex datasets, and offering a unique opportunity to interrogate our theoretical model assumptions. We deploy machine learning 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.


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