Francesco is leading the high redshift quasar search efforts with Euclid mission data. He is using machine learning models (e.g., XGBoost) to select quasar candidates in Euclid data. The training set for the selection is built from synthethic quasar spectra, that were generated using a generative machine learning model (Variational Auto Encoder) we are developing in the group.

Laura has been leading campaigns to identify high-redshift quasars using semi-supervised machine learning techniques. Within the group she is exploring contrastive learning methods to identify quasars missed by our standard supervised selection strategies.

Radha's current work focuses on the demographics of luminous quasars at redshifts z~3.5 to z~6. Based on a large (58) sample of near-infrared spectroscopy of extremely luminous quasars, she is studying their physical properties (e.g., black hole mass, accretion rate) to derive the black hole mass function at z~3.5. Extending this work to z~6 will allow her to study the evolution of the quasar population over the first billion years of the Universe.

Katharina's current project is embedded in the collaborative work around the JWST Treasury Program COSMOS-3D. She is working with JWST/NIRCam wide field slitless spectroscopy to provide a census of galaxies and faint active galactic nuclei at high redshift. The project aims to map the growth of galaxie and supermassive black holes in the early Universe.

Tatsuyuki works at the intersection of machine learning and astronomy. He is developing and applying modern machine learning algorithms for a more unbiased quasar census in the Euclid Wide Survey. He is currently developing a multi-modal probabilistic autoencoder to select quasar candidates using anomaly scores.

Lasse is interested in the enigma of Little Red Dots (LRD) and is starting his PhD by characterising the spectroscopic properties of a LRD at z∼7.3 in detail. In particular, he will be using JWST NIRSpec and MIRI observations to analyse the rest-frame UV and optical properties of this object.

Violet is a Master of Physics student at Hamburg University. She is working on a project to identify quasars in the Euclid data using supervised machine learning techniques. She focuses on a redshift range of z~5.5 to z~6.5, to unveil luminous quasars that might have been missed previously.

Laurenz is a Master of Physics student at Hamburg University. He is working on the characterization of broad-absorption lines troughs in hyper-luminous quasars at Cosmic Noon. In order to model the spectral continuum, he is improving QUEST (Quasar Unsupervised Encoder and Synthesis Tool) for a more accurate reconstruction of the quasar emission.

Arthur's Masters project explored the use of self-supervised representation learning applied to 1D spectroscopic data. In this context he adapted the "Bootstrap Your Own Latent" (BYOL) algorithm.

Lea's thesis project focused on the radio properties of extremely luminous quasars. She is investigated the shapes of the radio spectral energy distributions in a sample of 58 quasars at redshift z∼3.5 using data from different radio surveys (e.g., LOFAR, FIRST, VLASS).

In his Bachelor thesis Niklas looked for quasars with similar properties to J0100+2802, the most luminous quasar known at z∼6.3. He used the spectra of SDSS quasars to identify low-redshift analogues and evaluate claims of lensing magnification in the J0100+2802 spectrum.