Thesis Topic: The physical conditions of Star-Formation/AGN feedback’s end run: gas and dust in the Circum-Galactic medium (CGM): two clusters as test-bench
Thesis Supervisors: Paola Andreani and Carlos De Breuck
The CGM is a key ingredient to understand galaxy formation as it represents the location where galaxies exchange material with their Cosmological environment. Inflows from the CGM to the galaxy provide the fuel for their growth, sustaining star formation. Such inflows are predicted by galaxy formation models, but due to their very extended nature and faint emission, they are difficult to detect observationally. Outflows powered by AGN and star formation are depositing chemically enriched material in the CGM. These outflows may be easier to detect, but it is often unclear if they possess sufficient energy to escape the galaxy potential well, or if they will simply “rain back” onto their originating galaxies.
In this thesis, we will concentrate on the thermal dust continuum and (sub)mm lines, and in particular on the [CI] and CO lines observed with ACA/ALMA and APEX. These lines provide key information on the energy sources of the observed CGM gas. The use of these lines to study the CGM has taken off recently, though the physical interpretation is clearly not yet there, partially because a coherent treatment of the [CI] and CO emitting regions within the giant molecular clouds is difficult to achieve in “vanilla” large velocity gradient (LVG) models. In particular the lower density H2 gas irradiated by cosmic rays is often missing, while they can yield significant amounts of CO-poot/[CI]-rich gas.
The student will be exposed to both observations and modelling, which is essential to form a critical mind to understand the limits of the data and the physical interpretation thereof. ESO is an ideal place to perform this work with the high concentration of ALMA experts with whom the student can interact. Conversely, the student will become the local expert on the SPLENDIFITO code, which may well lead to new collaborations with other staff/fellow/students both at ESO and in the neighbouring institutes.
The proposed astrochemical grid used in the SPLENDIFITO code will consist of hundreds of thousands of different simulations. Considering all those simultaneously for solving a particular observation will be very time consuming. The student can therefore approach this problem by using modern machine-learning techniques that will dramatically accelerate the efficiency of the proposed code. Such knowledge of handling big data is highly valuable across many different fields of Astrophysics and beyond.