TY - JOUR
T1 - QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes
AU - Natarajan, Priyamvada
AU - Tang, Kwok Sun
AU - McGibbon, Robert
AU - Khochfar, Sadegh
AU - Nord, Brian
AU - Sigurdsson, Steinn
AU - Tricot, Joe
AU - Cappelluti, Nico
AU - George, Daniel
AU - Hidary, Jack
N1 - Funding Information:
P.N. gratefully acknowledges support from the Black Hole Initiative (BHI) by grants from the Gordon and Betty Moore Foundation and the John Templeton Foundation. P.N. thanks her colleagues at the BHI and members of the next generation Event Horizon Telescope (ngEHT) Science Working Group on Black Holes and their Cosmic Context for many useful conversations on evolving black hole populations and is grateful to Alphabet-X for technical support and computational resources for this project. P.N. acknowledges conversations on databases with Sanjay Sarma and Brian Subirana during the early stages of this project. P.N. and K.S.T. thank Rick Ebert at the Infra-Red Processing and Analysis Center (IPAC) at the California Institute of Technology for his help with accessing the NED database. K.S.T. thanks Frank Wang at Google for his help with the Google Cloud Platform. S.K. acknowledges use of the ARCHER UK National Super-computing Service ( http://www.archer.ac.uk ) for running the LEGACY simulation. B.N. acknowledges support from the Fermi National Accelerator Laboratory, managed and operated by Fermi Research Alliance, LLC under contract No. DE-AC02-07CH11359 with the U.S. Department of Energy. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. S.S. acknowledges the Aspen Center for Physics where parts of this work were done, which is supported by National Science Foundation grant PHY-1607611.
Publisher Copyright:
© 2023. The Author(s). Published by the American Astronomical Society.
PY - 2023/7/25
Y1 - 2023/7/25
N2 - We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)
AB - We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)
KW - Digital Sky Survey
KW - Quasar Luminosity Function
KW - Active Glactic Nuclei
KW - Broad-Line Region
KW - Oscillation Spectroscopic Survey
KW - Near-Infrared Spectroscopy
KW - Large-Scale Structure
KW - Data Release
KW - Cosmological Simulations
KW - Illustris Simulation
UR - http://www.scopus.com/inward/record.url?scp=85165700702&partnerID=8YFLogxK
U2 - 10.3847/1538-4357/acd9ce
DO - 10.3847/1538-4357/acd9ce
M3 - Article
AN - SCOPUS:85165700702
SN - 0004-637X
VL - 952
SP - 1
EP - 25
JO - Astrophysical Journal
JF - Astrophysical Journal
IS - 2
M1 - 146
ER -