Supermassive black holes

Quasars and Supermassive Black Holes

My main scientific interest lies in the emergence of cosmic structure in the early Universe. I am particularly interested in the formation and evolution of supermassive black holes (SMBHS), which we can observe during phases of active growth (mass accretion), when they shine as luminous quasars. I study quasars in the first two billion years of the Universe to understand the processes by which SMBHs form, how they grow to their observed masses and how they influence the evolution of their host galaxies. The first massive SMBHs are believed to reside in the most overdense regions of the Cosmic Web and observations of these environments allow us to test large-scale structure formation models. Furthermore, quasars serve as bright beacons to study hydrogen reionization, the last major phase transition of the Universe, in absorption.

The search for the most distant quasars

To understand the formation of supermassive black holes I am searching for the most distant quasars using near-infrared, wide-area photometric surveys. Applying careful completeness analyses we can statistically constrain the quasar number densities to understand the emergence of the first SMBHs.

The Euclid space mission, to be launched in 2023, and the Vera Rubin Observatory with its Legacy Survey of Space and Time, will provide the deepest optical and near-infrared surveys of the extragalactic sky. These surveys will allow us to discover quasars up to redshifts of 10 to truly understand their formation processes and early growth.

I work on developing new quasar selection strategies tailored to these surveys to build the first census of the most distant quasars.

In past work I have designed and executed the Extremely Luminous Quasar Survey (ELQS) to discover the most luminous quasars at z=3-5 for a more accurate measurement of the z=3-5 bright-end quasar luminosity function. This selection strategy applied random forest classification on panchromatic photometric data.

I have also been an integral part of the Pan-STARRS1 distant quasar survey targeting quasars at z>6. Following many observational campaigns in the last few years, we recently increased the discovery sample by a 55 quasars. Based on these number counts, I just presented the most precise estimate of the z~6 quasar luminosity function.

Characterization of supermassive black holes

Spectroscopic follow-up observations of quasars are not only essential to measure the mass of the SMBH and to understand the physics of the accretion process. The spectra also allow us to investigate the chemical make-up of the accreted gas and provide insight into foreground galaxies as well as the state of the intergalactic medium.

I have been leading one of the largest spectroscopic studies of high-redshift quasars, the X-SHOOTER/ALMA Survey of Quasars in the Epoch of Reionization. Based on this sample of 38 reionization-era quasars, z>5.7, we concluded that quasars at these times are accreting more rapidly than their lower-redshift cousins. In addition, the spectra more commonly show large velocity shifts of high-ionization lines, indicative of vigorous (outflowing) gas motion close to the accretion disk. Even 0.8 Gyr after the Big Bang the accreted gas is already chemically enriched, indicating a fast chemical evolution at the centers of these early massive galaxies.

I am also a member of the XQR-30 collaboration, which is centered on a large ESO/X-SHOOTER program to provide high signal-to-noise ratio spectra of 30 quasars at z>5.7. This unprecedented data set has already led to some exciting results: in a study published in Nature, lead by Manuela Bischetti, we show that fast outflowing dense gas is observed at a much higher incidence in the first 1 Gyr after Big Bang, possibly indicating more vigorous feedback effects on the surrounding galactic gas. We furthermore use these spectra to understand the evolution and morphology of cosmic hydrogen reionization. By measuring the Lyman-α transmission we were able to conclude that the epoch of reionization extends to z~5.3, later than originally thought.

Machine learning in Astrophysics

I apply machine learning techniques to search for rare high-redshift quasar candidates in large astronomical datasets. For source classification and photometric redshift estimation (regression problem), I employ:

Random Forests
Support Vector Machines
Bayesian Neural Networks

However, machine learning algorithms can also be useful to generatemodel quasar spectra to constrain survey selection functions or to measure physical properties. With my new group I am to build a new quasar spectral model with the help of one or more of the following algorithms:

Variational Autoencoders
Gaussian Process Latent Variable Model

I am further interested to explore the vast data sets of the Euclid mission and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) to search for rare and exotic objects with unsupervised machine learning methods.

Probing the early Universe with the James Webb Space Telescope

The James Webb Space Telescope (JWST) opens up the near- and mid-infrared sky unhindered by the Earth's atmosphere. It's unprecendent capabilities will revolutionize our understanding of the formation of the first galaxies and supermassive black holes. I am leading the data reduction and analysis of the JWST program "Towards Tomographic Mapping of Reionization Epoch Quasar Light-Echoes with JWST" (PI: J. Hennawi) to characterize the environments of two of the most distant quasars. I am involved as a Co-I in a range of other JWST programs on supermassive black holes and large scale structure, including: