My research centers on the origins, growth, and evolution of supermassive black holes and the galaxies that host them. Below is an overview of some of the projects my collaborators and I are currently pursuing.
In the large-scale structure of our cosmos, matter is not distributed uniformly but arranged in a vast "cosmic web" of filaments, sheets, and nodes. The nodes of this cosmic web are extremely overdense regions where dark matter halos collapse earliest and grow most massive - representing the peaks of our universe.
They act as the gravitational anchors for clusters of galaxies and are believed to be the birthplaces of the first supermassive black holes.
Distant quasars are expected to reside preferentially in these cosmic peaks. I am part of the JWST GTO team EIGER ("Emission-line galaxies and Intergalactic Gas in the Epoch of Reionization") and the PI of an upcoming 110-hour JWST Cycle 4 program MOENCH ("Measuring Obscured and Emergent Galactic Nuclei across Cosmic History") - which are both named after beautiful mountain peaks in the Alps - to study these cosmic peaks around quasars in the early universe.
I am also leading a large program with the Chandra X-ray Observatory to find hidden accreting black holes in these quasars' environment.
Ultra-violet radiation from accreting black holes ionizes the intergalactic gas around quasars, carving out highly ionized bubbles in their surroundings.
Any changes in a quasar's luminosity produce outward-propagating ionization gradients, known as the quasars' light echoes.
Using Lyman-alpha tomography, we can trace these variations in the opacity of the intergalactic medium by means of deep spectroscopic observations of galaxies in the background of luminous quasars, which we obtain with JWST in Cycle 2 and 3 as part of the MASQUERADE - "Mapping a Super-luminous Quasar's Extended Radiative Emission" - programs (PI: Eilers).
Lyman-alpha tomography allows us to create three-dimensional maps of a quasar's light echo, similar to a CT scan, and enables us to set constraints on the quasar's lifetime and geometric obscuration.
Additionally, these ionized bubbles around quasars represent also a unique laboratory to study the ionizing properties of high-redshift galaxies without attenuation by the intergalactic medium.
While luminous quasars reveal only the tip of the iceberg, a large yet undetected population of supermassive black holes is expected to be growing in hidden environments.
These obscured black holes are shrouded in thick cocoons of gas and dust that block most optical and ultraviolet light.
Yet, they may represent the dominant phase of black hole growth, when accretion might be the most efficient.
In the last couple of years, JWST detected a mysterious, previously unknown population of galaxies dubbed "Little Red Dots", which might be hosting such a rapdily accreting black hole in their core.
The lifetime of quasars denotes the timescale on which supermassive black holes grow and actively accrete material from their surrounding accretion disk.
In our standard black hole growth picture, we expect this timescale to last about a billion years in order to grow a supermassive black hole from a small stellar remnant initial black hole seed.
Yet new techniques to constrain these black hole growth timescales, such as measuring the extent of the "proximity zones" observed in quasar spectra, suggest that quasar lifetimes only last about a million years - orders of magnitude shorter than expected.
I am the PI of a JWST Cycle 2 proposal BEES ("Black Hole Extended Emission Search") to observe the extended nebular emission around quasars in order to estimate the quasar lifetimes independently and provide new clues about possible mechanisms that could explain these puzzlingly short black hole growth timescales.
Measuring the masses of supermassive black holes in distant quasars is challenging, because the bright light from the accretion disk outshines the host galaxy by orders of magnitude.
For most quasars, especially in the early universe, we rely on simple scaling relations between emission line widths and luminosities, which carry large uncertainties.
To overcome this, we are developing new machine-learning approaches of multi-model data sets that learn the information on quasar properties that are encoded in their spectra.
By modeling the spectra jointly with the quasars' physical properties in a low-dimensional space, we aim to predict accurate black hole masses and luminosities, even when parts of the spectra are missing or noisy.