Galaxy formation and evolution

The study of galaxy formation and evolution is concerned with the processes that formed a heterogeneous universe from a homogeneous beginning, the formation of the first galaxies, the way galaxies change over time, and the processes that have generated the variety of structures observed in nearby galaxies. Galaxy formation is hypothesized to occur from structure formation theories, as a result of tiny quantum fluctuations in the aftermath of the Big Bang. The simplest model in general agreement with observed phenomena is the Lambda-CDM model—that is, that clustering and merging allows galaxies to accumulate mass, determining both their shape and structure. Hydrodynamics simulation, which simulates both baryons and dark matter, is widely used to study galaxy formation and evolution. Commonly observed properties of galaxies Because of the inability to conduct experiments in outer space, the only way to “test” theories and models of galaxy evolution is to compare them with obse

UNITSIM-Galaxies: data release and clustering of emission-line galaxies

Ginevra Favole

New surveys such as European Space Agencys (ESA's) Euclid mission are planned to map with unprecedented precision the large-scale structure of the Universe by measuring the 3D positions of tens of millions of galaxies. It is necessary to develop theoretically modelled galaxy catalogues to estimate the expected performance and to optimize the analysis strategy of these surveys. We populate two pairs of (1 h(-1) Gpc)(3) volume dark matter-only simulations from the UNIT project with galaxies using the Semi-Analytic Galaxy Evolution semi-analytic model of galaxy formation, coupled to the photoionization model GET.EMLINES to estimate their H alpha emission. These catalogues represent a unique suite that includes galaxy formation physics and - thanks to the fixed-pair technique used - an effective volume of similar to (5 h(-1) Gpc)(3), which is several times larger than the Euclid survey. We present the performance of these data and create five additional emission-line galaxy (ELG) catalogues by applying a dust-attenuation model as well as adjusting the flux threshold as a function of redshift in order to reproduce Euclid-forecast dN/dz values. As a first application, we study the abundance and clustering of those model H alpha ELGs: for scales greater than similar to 5 h(-1) Mpc, we find a scale-independent bias with a value of b similar to 1 at redshift z similar to 0.5, that can increase nearly linearly to b similar to 4 at z similar to 2, depending on the ELG catalogue. Model galaxy properties, including their emission-line fluxes (with and without dust extinction) are publicly available.

OXFORD UNIV PRESS2022

Methods for strong gravitational lens detection and analysis using machine learning and high performance computing

Christoph Ernst René Schäfer

The study of strong gravitational lenses is a relatively new scientific field in astronomy with many applications in cosmology. Its unique observables allow astronomers to trace dark matter, determine the expansion of the universe and study galaxy evolution. While strong lenses are relatively rare phenomena, recent and future improvements in observational instrumentation are expected to lead to a sharp increase in the number and quality of their observation. With that increase comes however an observational challenge from the sheer quantity of data the lenses are hidden in. Current classification and modelling techniques are simply not capable of handling the new data in a reasonable timeframe. As a consequence this thesis concerns itself with the development and improvement of numerical methods necessary to find and analyse these newly found lenses, borrowing from deep learning and high performance computing techniques. Deep learning, a field of machine learning, shows significant success in finding lenses in simulations. Performing significantly better than any other classification method, CNN-based lens finders reach almost the required classification efficiency and accuracy for a fully automated Euclid strong lensing pipeline. Its dependency on a varied and complete training set and the difficulty creating it make its immediate application to new surveys however more difficult. Incorporating high performance computing techniques and more concurrent algorithm design into lens analysis results in crucial speed-ups for strong lens analysis. Applied to Lenstool, a popular mass modelling tool for gravitational lenses, the resulting software can be run on modern supercomputers, using Graphics Processing Units (GPU) and CPUs simultaneously. Running it on state-of-the-art hardware makes it possible to reduce computation time to an acceptable level when mass modelling complex cluster lenses.

EPFL2020

On the influence of halo mass accretion history on galaxy properties and assembly bias

Ginevra Favole

Halo assembly bias is the secondary dependence of the clustering of dark matter haloes on their assembly histories at fixed halo mass. This established dependence is expected to manifest itself on galaxy clustering, a potential effect commonly known as galaxy assembly bias. Using the IllustrisTNG300 magnetohydrodynamical simulation, we analyse the dependence of the properties and clustering of galaxies on the specific mass accretion history of their hosting haloes (sMAH). We first show that several halo and galaxy properties strongly correlate with the slope of the sMAH (beta) at fixed halo mass. Haloes with increasingly steeper beta increment their masses faster early on, and their hosted galaxies present larger stellar-to-halo mass ratios, lose their gas faster, reach the peak of their star formation histories at higher redshift, and become quenched earlier. We also demonstrate that beta provides a more stable link to these key galaxy formation properties than other broadly employed halo proxies, such as formation time. Finally, we measure the secondary dependence of galaxy clustering on beta at fixed halo mass. By tracing back the evolution of individual haloes, we show that the amplitude of the galaxy assembly bias signal for the progenitors of z = 0 galaxies increases with redshift, reaching a factor of 2 at z = 1 for haloes of M-halo = 10(11.)(5) -10(12) h(-1) M-circle dot. The measurement of the evolution of assembly bias along the merger tree provides a new theoretical perspective to the study of secondary bias. Our findings have also important implications for the generation of mock catalogues for upcoming cosmological surveys.

OXFORD UNIV PRESS2021

Methods for strong gravitational lens detection and analysis using machine learning and high performance computing

Christoph Ernst René Schäfer

EPFL2020