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Introducing QUOTAS as a new research platform for the data-driven discovery of supermassive black holes

Nature Astronomy volume 7, pages 879–881 (2023)Cite this article

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Observations across different wavelengths over the past two decades point to the existence of central SMBHs in most, if not all galaxies. The masses of central SMBHs appear to be correlated with the properties of their host galaxies, namely the luminosity and stellar mass of the inner-most regions. Therefore, the modelling of the formation and evolution of SMBHs is now integrated into the larger question of how structure forms and galaxies assemble in the Universe, as they are now understood to play an important role in the modulation of star formation in galactic nuclei. One key open question in understanding the assembly history of SMBHs is the nature of their underlying deeper connection to their parent dark matter haloes and their host galaxies. The empirical correlations are believed to arise causally from physical processes that operate at the earliest epochs, where our current observational data is sparse. QUOTAS offers a novel platform to probe the origin and evolution of this fundamental halo–galaxy–SMBH connection out to the highest redshifts and fill in crucial gaps in the cosmic inventory of SMBHs.

The formation of black holes and their mass assembly history is now integrated into the standard cosmological model that describes structure formation in the Universe. Semi-empirical models that track the growth and evolution of SMBHs rely on two quantities derived from observations as input constraints: the quasar luminosity function (QLF) and the black hole mass function (BHMF). The QLF provides a census of the number of sources at a given redshift and absolute magnitude and is one of the key inputs to current conceptual models of BH growth. Coupled with detailed multi-wavelength studies of individual objects, population studies of quasars spanning a range of luminosities that reveal aspects of their continual evolution across multiple cosmic epochs are crucial for building evolutionary models. Large quasar samples derived from ambitious astronomical surveys have enabled the study of the cosmological evolution of the QLF. The QLF and BHMF drive theoretical modelling, and together they encapsulate the growth history of SMBHs through accretion. Their importance was demonstrated in early modelling work, and it continues to be vigorously studied in the field (for example, see early work by Haehnelt, Natarajan and Rees7 and reviews by Volonteri8 and Natarajan9). Observationally determined QLFs are then compared with those predicted by theoretical models of BH growth. Deriving the QLF requires taking specifications of each observational survey into account and this is done meticulously for all sources in QUOTAS. Calibrated with data, the best-fit models are extrapolated and then used to predict QLFs down to fainter luminosities than observed limits and out to larger redshifts than current detections. The validation of predictions is in turn iteratively used to hone models further. As well as observational biases, there are considerable degeneracies in current theoretical models, arising from uncertainties in our knowledge of the physics of gas accretion onto SMBHs, and in the dynamics of SMBH mergers, in addition to our incomplete understanding of the formation channels for the first seed black holes. The faint end of the QLF — the amplitude and the slope — holds the key to discriminating between models and disentangling ‘nature’, the initial conditions for seeding, from ‘nurture’, evolutionary processes, as this is where the largest differences between scenarios appear10. Larger volumes of data will not only allow determination of the faint end of the QLF but will also provide insights into additional statistical properties and enable the derivation of new higher-order metrics that will provide more stringent constraints on theoretical models. QUOTAS by design allows easy computation of both the QLF and BHMF as demonstrated in the workflow examples on the Google Kaggle site.

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Fig. 1: The distribution on the sky of quasars collated in QUOTAS.
Fig. 2: Dark matter halo population demographics.

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Acknowledgements

P.N. gratefully acknowledges the invitation to Google’s Science Festival SciFoo in 2018, where she first hatched this idea, and thanks S. Sarma and B. Subirana at MIT for early discussions. She acknowledges Alphabet-X for technical support and computational resources for this project. P.N. and K.S.T. thank R. Ebert at the Infrared Processing and Analysis Center (IPAC) at the California Institute of Technology for his help with accessing the NED database. K.S.T. thanks F. 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 US Department of Energy. This manuscript has been authored by Fermi Research Alliance, LLC under contract no. DE-AC02-07CH11359 with the US Department of Energy, Office of Science, Office of High Energy Physics. 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.

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Authors and Affiliations

  1. Department of Astronomy, Yale University, New Haven, CT, USA

    Priyamvada Natarajan

  2. Black Hole Initiative, Harvard University, Cambridge, MA, USA

    Priyamvada Natarajan

  3. Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Kwok Sun Tang

  4. Institute for Astronomy & Royal Observatory, University of Edinburgh, Edinburgh, UK

    Sadegh Khochfar

  5. Fermi National Accelerator Laboratory, Batavia, IL, USA

    Brian Nord

  6. Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL, USA

    Brian Nord

  7. Department of Astronomy & Astrophysics, University of Chicago, Chicago, IL, USA

    Brian Nord

  8. Department of Astronomy & Astrophysics and Institute for Gravitation and the Cosmos, Pennsylvania State University, University Park, PA, USA

    Steinn Sigurdsson

  9. Sandbox@Alphabet, Mountain View, CA, USA

    Joe Tricot, Daniel George & Jack Hidary

  10. Department of Physics, University of Miami, Coral Gables, FL, USA

    Nico Cappelluti

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Natarajan, P., Tang, K.S., Khochfar, S. et al. Introducing QUOTAS as a new research platform for the data-driven discovery of supermassive black holes. Nat Astron 7, 879–881 (2023). https://doi.org/10.1038/s41550-021-01478-x

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