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Ferreira, Leonardo (2023) The Cosmic Evolution of Galaxy Structure and Morphology at 0.5 < z < 8. PhD thesis, University of Nottingham.

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Abstract

This thesis is prepared in two parts. In the first half (Chapter 2 and Chapter 3) we discuss the evolution of galaxy mergers at 0.5 < z < 3.0 in all the CANDELS fields based on a supervised deep learning model trained on the IllustrisTNG cosmological simulations. The second half is dedicated to the rest-frame optical morphological evolution of galaxies from z = 1.5 to 8 as observed by JWST in the SMACS 0723 field, and in the early observations of the CEERS program.

In Chapter 2 we describe a supervised deep learning framework designed for the classification of high redshift galaxy mergers based on data from the IllustrisTNG cosmological simulations. We generate a large dataset of before mergers/post- mergers/non-mergers galaxy mocks labeled with information from IllustrisTNG 300-1 merger trees. These imaging data are prepared to be CANDELS-like and are then used to train deep learning models capable of achieving 90% of accuracy within the simulations. Using these them we describe the evolution of the galaxy merger fractions and rates in the CANDELS fields and we discuss how these deep learning classifications are related to visual classifications. We report the first agreement of galaxy merger rates between galaxy pair statistics methods and morphologically selected mergers, with R(z) = 0.02 ± 0.004 × (1 + z)2.76±0.21, showing that the highest merger rates are found at the highest redshifts.

We tackle the challenging problem of separating recently coalesced galaxy mergers from non-interacting highly star forming galaxies in Chapter 3. These two populations present ambiguous morphologies due to asymmetric features. We refine our methods reported in Chapter 2 for this particular question, generating a dataset of TNG100-1 post-mergers and star forming galaxies at 0.5 < z < 3.0, including a full radiative transfer treatment with the SKIRT code, producing ∼ 160,000 images with realistic morphologies. We explore the relative populations of post-mergers and non interacting star forming galaxies in this redshift range. We show that the population of high redshift asymmetric galaxies are more likely to be of post-merger origin than their low redshift counterparts. The interpretabil- ity of our models is discussed by exploring the feature space extracted from the mock imaging and the real CANDELS galaxies. We show that for this particu- lar problem, deep learning models provide an 30% improvement over quantitative morphology methods.

We focus on the early release JWST observations of the SMACS 0723 cluster in Chapter 4. We report the first ever morphological study of rest-frame optical structure in 1.5 < z < 6 with NIRCam, within the wavelength range λ = 0.9μm - 4.4μm. We conduct visual classifications and quantitative morphology measure- ments on a sample of ∼ 200 galaxies previously detected with HST. We report a surprising mismatch between the number of disk galaxies detected with HST and JWST. Around ten times more disks are found. We briefly discuss the implica- tions of this result and how it fits in the galaxy formation and evolution evolution

Over Chapter 5 we expand the framework of Chapter 4 to the early CEERS JWST observations that have overlap with the EGS observations from the CAN- DELS fields with HST. We release to the community the biggest sample of visually classified galaxies observed with JWST to-date, with 4265 galaxies that are both observed by HST and JWST. With this dataset, we carefully discuss the evolu- tion of the Hubble sequence up to z ∼ 8, finding that it is already present at the earliest of times for low to intermediate mass galaxies, while evolution driven by mergers is observed for massive galaxies. We detail the quantitative morphology characteristics of this sample, and how it correlates with visual optical morphology.

We finish with a brief discussion on the results presented in this thesis, how the merger evolution at 0.5 < z < 3.0 and the general morphological evolution at z > 3.0 are linked, and what are the next steps to explore this connection further.

Item Type:	Thesis (University of Nottingham only) (PhD)
Supervisors:	Conselice, Christopher J
Keywords:	galaxies, galaxy mergers, deep learning, machine learning
Subjects:	Q Science > QB Astronomy
Faculties/Schools:	UK Campuses > Faculty of Science > School of Physics and Astronomy
Item ID:	73395
Depositing User:	Ferreira, Leonardo
Date Deposited:	26 Jul 2023 04:40
Last Modified:	26 Jul 2023 04:40
URI:	https://eprints.nottingham.ac.uk/id/eprint/73395

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