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Durham e-Theses
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Cosmological redshift surveys, big data and semi-analytical galaxy formation models

MANZONI, GIORGIO (2022) Cosmological redshift surveys, big data and semi-analytical galaxy formation models. Doctoral thesis, Durham University.

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The focus of this thesis is to connect observations from different galaxy redshift surveys to physically motivated models of galaxy formation. The Physics of the Accelerating Universe Survey (PAUS) and the VIMOS Public Extragalactic Redshift Survey (VIPERS) are the two major surveys used in this thesis. For both, I use the semi-analytical model GALFORM to create a physically motivated scenario and explore the evolution of some observational galaxy properties starting from an epoch when the Universe was approximately half of the current age. In particular, for PAUS, we used GALFORM implemented in the Planck Millennium N-body simulation, to build a mock PAUS galaxy catalogue on an observer’s past lightcone. The increased mass resolution and the higher frequency of simulation outputs allow us to make improved galaxy property predictions. We compared the mock catalogue predictions with the observed number counts, redshift distributions and the redshift evolution of the observer frame colours. The red and blue populations of the model galaxies are roughly in agreement with the observed ones.
The interest in galaxy colours and the relation to their star formation histories lead us to exploit VIPERS for a galaxy evolution study. In particular, we analysed the evolution of the rest frame colour-magnitude relation tracking the evolution of the bright edge with the aim of deriving constraints on the quenching of star formation activity in galaxies in 0.5 < z < 1.1. Through the modelling of parametrised star formation histories (SFHs) we estimated the average time-scale of the suppression of the star formation. We showed that modelling a fast suppression of the star formation activity in galaxies, we are able to reproduce better the observational data. We created a physically motivated mock with GALFORM and we obtained a similar qualitative trend. We tested that AGN feedback, as the main quenching process in GALFORM, is crucial in reproducing the trend of the bright-edge.
As part of the Centre for Doctoral Training (CDT), I developed data analysis techniques, during an internship with Procter & Gamble (P&G) and with the National Health Service (NHS), in two projects described in this thesis. In the P&G project the aim was to model the density of the laundry powder in order to understand the chemical ingredients that mostly contribute to the density and hence changing the industrial process accordingly. In the NHS project, we aimed at establishing new normative ranges for vital signs of newborn babies. The results of this project have the potential of helping doctors in making critical decisions for the treatment that can save the life of babies in critical conditions.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Keywords:Computational cosmology; Galaxy formation model; GALFORM; Extragalactic astronomy; Galaxy redshift syrveys; colour-magnitude diagram; Quenching of star formation; Procter and Gamble; P&G; National Health Sevice; NHS; Centre for doctoral training; CDT; Peder Norberg; Carlton Baugh; Giorgio Manzoni; babies; laundry powder; data science; big data; machine learning; Durham University; IDAS; ICC; CEA
Faculty and Department:Faculty of Science > Physics, Department of
Thesis Date:2022
Copyright:Copyright of this thesis is held by the author
Deposited On:04 Jul 2022 14:58

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