5. Which supermassive black holes shape the evolution of the galaxy where they reside?


ESA supervisor: Matteo Guainazzi


Astrophysical black holes (BHs) cover a wide range of masses. Many of them accrete matter, either from a companion mass (if “stellar-mass” BHs in binary systems) or from the interstellar gas and dust (if “supermassive” BHs at the core of Active Galactic Nuclei, AGN). Efficient physical processes convert the gravitational energy into X-ray radiation. In stellar-mass BHs, such a radiation is variable on human time-scales, defining (X-ray Binary, XRB) accretion states. On the other hand, variability in AGN is much slower due to their mass and size. 

The “states” of stellar-mass accreting BHs allow us to well understand the nature of the accretion flow and of energetic outflows expelled by the BH, either in the form of collimated relativistic jets or uncollimated slower winds. While we cannot use the same technique with slowly varying AGN, one can map states in XRBs with populations of different types of AGN, using our deep understanding of the rich phenomenology in the former systems to unveil the nature of the accretion flow, disks and winds in the latter systems.

 This project aims at exploiting a sample of AGN with prominent jet/wind signatures developed during a joint master project with the University of Leiden in 2022, and investigate their accretion state through of the ratio of the thermal radiation produced by the accretion disc (primarily in UV) against the non-thermal radiation produced by relativistic electrons close to the BH (primarily in X-rays). To achieve this goal, the student will employ thousands of AGN from 4XMM-DR10, the latest version of the catalogue of serendipitous sources detected by the EPIC instrument (X-ray Charge Coupled Device) on board the ESA’s observatory XMM-Newton.

Project duration: 6 months.

Desirable expertise or programming language:

No formal pre-requisites for this project exist. Knowledge of matter-radiation process (e.g., photoelectric absorption, photoionization collisional and radiative de-excitation, radiative recombination) would be an asset. The project will require creating automated meta-analysis scripts to reduce and analyse data of a large number of (~a few hundreds). Knowledge of a programming language (Python, IDL, etc.) would be therefore an asset.


To apply for this project please fill in an online application form through the following link.