Kavli Affiliate: Brian Nord
| First 5 Authors: Priyamvada Natarajan, Kwok Sun Tang, Robert McGibbon, Sadegh Khochfar, Brian Nord
| Summary:
We present QuasarNet, 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 QuasarNet, 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. QuasarNet, 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. QuasarNet, and all related materials are publicly
available at the Google Kaggle platform.
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