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Gohar Dashyan

Lead Recherche et Éthique

PhD Thesis

Baryonic feedback processes in dwarf galaxies and satellites (2019)

Understanding galaxies calls for the understanding of their appearance, which results from their stellar content, and stars form out of cold gas. Feedback processes can impede star formation: by heating the gas or preventing it from cooling and forming stars; by triggering galactic outflows that eject the gas; and by preventing the inflow of gas. In the lowest mass galaxies – dwarf galaxies –powerful and luminous stellar explosions, supernovae (SNe), are thought to be the predominant feedback process. However, recent work suggests that SN feedback is too weak. Moreover, several puzzles surrounding dwarf galaxies challenge our understanding of dark matter – of which the exact nature that is still unknown –, and these discrepancies might be solved by feedback processes. Besides, Active Galactic Nuclei (AGN) – i.e. growing massive black holes –, are another source of feedback in galaxies. The effect of AGN in dwarf galaxies is often thought to be minimal, because their growth is suppressed. But there is growing evidence that their presence in dwarf galaxies might be stronger than once thought. First, I show, using analytical methods, that the capacity of AGN to trigger outflows in dwarf galaxies is stronger than that of SNe in most of the parameter space. I then show numerically, that the AGN of a galaxy can influence the evolution of a neighboring galaxy. Finally, I assess the impact of the injection of cosmic rays by SNe: I find that it reduces the rate of star formation and increases the efficiency of SNe at driving winds, bringing simulated wind properties of dwarf galaxies much closer to observations, but the effect is not sufficient to fully match the observations.

Co-supervision of two CIFRE theses on the company side (Safran.AI)

Main publications

In connection with the thesis defended In connection with the duties of Research Team Manager at Safran.AI
Research fields : self-supervised learning, computer vision