Exploring the history of star formation in galaxies and its environmental dependence at high redshift

The first stars formed in the early universe and shortly after assembled into the first galaxies. Since then, galaxies have been subject to a variety of processes, both internal and external, that affect their ability to form stars. At low redshift, environment plays a large role in inhibiting star formation, however it is less clear what effect it has at high redshift. This is predominantly due to the difficulty of determining the nature of the high redshift environment from uncertain redshift measurements, and the small coverage of high redshift surveys leading to poor sampling of the cosmic variance. In this thesis I use a variety of numerical approaches to various aspects of this problem. In the first section I use a semi-analytic model to study the relationship between observed galaxy surface overdensity and the probability of coinciding with a protocluster, the pre-collapse progenitors of galaxy clusters, and make recommendations for optimum measurement apertures for their identification. In the second section I use a suite of hydrodynamic simulations of galaxy clusters, across a range of descendant halo masses, to study the galaxy evolution in their protocluster progenitors in detail. I characterise the star-forming sequence, studying it’s difference in protocluster and field environments, as well as within dense groups in the collapsing protocluster. In the final section I use a novel approach to estimate the star formation history of galaxies. Rather than studying the high redshift environment directly, I estimate when the stars in a low redshift galaxy were formed using population synthesis techniques. In this work I couple this with hydrodynamical simulations in order to provide more informative priors on the shape of the star formation history, which typically imposes strong biases on inferred properties, such as the total stellar mass, in more traditional approaches

Recalibrating the cosmic star formation history

The calibrations linking observed luminosities to the star formation rate depend on the assumed stellar population synthesis model, initial mass function, star formation and metal enrichment history, and whether reprocessing by dust and gas is included. Consequently the shape and normalisation of the inferred cosmic star formation history is sensitive to these assumptions. Using v2.2.1 of the Binary Population and Spectral Synthesis (\bpass) model we determine a new set of calibration coefficients for the ultraviolet, thermal-infrared, and, hydrogen recombination lines. These ultraviolet and thermal infrared coefficients are 0.15-0.2 dex higher than those widely utilised in the literature while the H$\alpha$ coefficient is $\sim 0.35$ dex larger. These differences arise in part due to the inclusion binary evolution pathways but predominantly reflect an extension in the IMF to 300 $M_{\odot}$ and a change in the choice of reference metallicity. We use these new coefficients to recalibrate the cosmic star formation history, and find improved agreement between the integrated cosmic star formation history and the in-situ measured stellar mass density as a function of redshift. However, these coefficients produce new tension between star formation rate densities inferred from the ultraviolet and thermal-infrared and those from H$\alpha$.Comment: 7 pages, 8 figures, accepted for publication in MNRA

Mapping circumgalactic medium observations to theory using machine learning

We present a random forest (RF) framework for predicting circumgalactic medium (CGM) physical conditions from quasar absorption line observables, trained on a sample of Voigt profile-fit synthetic absorbers from the SIMBA cosmological simulation. Traditionally, extracting physical conditions from CGM absorber observations involves simplifying assumptions such as uniform single-phase clouds, but by using a cosmological simulation we bypass such assumptions to better capture the complex relationship between CGM observables and underlying gas conditions. We train RF models on synthetic spectra for H I and selected metal lines around galaxies across a range of star formation rates, stellar masses, and impact parameters, to predict absorber overdensities, temperatures, and metallicities. The models reproduce the true values from SIMBA well, with normalized transverse standard deviations of 0.50–0.54 dex in overdensity, 0.32–0.54 dex in temperature, and 0.49–0.53 dex in metallicity predicted from metal lines (not H I), across all ions. Examining the feature importance, the RF indicates that the overdensity is most informed by the absorber column density, the temperature is driven by the line width, and the metallicity is most sensitive to the specific star formation rate. Alternatively examining feature importance by removing one observable at a time, the overdensity and metallicity appear to be more driven by the impact parameter. We introduce a normalizing flow approach in order to ensure the scatter in the true physical conditions is accurately spanned by the network. The trained models are available online

Nebular Line Emission During the Epoch of Reionization

Nebular emission lines associated with galactic HII regions carry information about both physical properties of the ionised gas and the source of ionising photons as well as providing the opportunity of measuring accurate redshifts and thus distances once a cosmological model is assumed. While nebular line emission has been extensively studied at lower redshift there are currently only few constraints within the epoch of reionisation (EoR, $z>6$), chiefly due to the lack of sensitive near-IR spectrographs. However, this will soon change with the arrival of the Webb Telescope providing sensitive near-IR spectroscopy covering the rest-frame UV and optical emission of galaxies in the EoR. In anticipation of Webb we combine the large cosmological hydrodynamical simulation Bluetides with photoionisation modelling to predict the nebular emission line properties of galaxies at $z=8\to 13$. We find good agreement with the, albeit limited, existing direct and indirect observational constraints on equivalent widths though poorer agreement with luminosity function constraints.Comment: 17 pages, accepted to MNRAS, significant modification from v1.0 data available at https://stephenmwilkins.github.io/BluetidesEmissionLines_Public

Reproducing sub-millimetre galaxy number counts with cosmological hydrodynamic simulations

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society. © 2021 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Matching the number counts of high-$z$ sub-millimetre-selected galaxies (SMGs) has been a long standing problem for galaxy formation models. In this paper, we use 3D dust radiative transfer to model the sub-mm emission from galaxies in the SIMBA cosmological hydrodynamic simulations, and compare predictions to the latest single-dish observational constraints on the abundance of 850$\mathrm{\mu m}$-selected sources. We find excellent agreement with the integrated 850$\mathrm{\mu m}$ luminosity function, along with good agreement in the redshift distribution of bright SMGs. The agreement is driven primarily by SIMBA's good match to infrared measures of the star formation rate (SFR) function between $z = 2-4$ at high SFRs. Also important is the self-consistent on-the-fly dust model in SIMBA, which predicts, on average, higher dust masses (by up to a factor of 2.5) compared to using a fixed dust-to-metals ratio of 0.3. We construct a lightcone to investigate the effect of far-field blending, and find that 52% of sources are blends of multiple components, which makes a small contribution to the normalisation of the bright-end of the number counts. We provide new fits to the 850$\mathrm{\mu m}$ luminosity as a function of SFR and dust mass. Our results demonstrate that exotic solutions to the discrepancy between sub-mm counts in simulations and observations, such as a top-heavy IMF, are unnecessary, and that sub-millimetre-bright phases are a natural consequence of massive galaxy evolution.Peer reviewedFinal Accepted Versio

Characterising and identifying galaxy protoclusters

We study the characteristics of galaxy protoclusters using the latest L-GALAXIES semi-analytic model. Searching for protoclusters on a scale of ∼10 cMpc gives an excellent compromise between the completeness and purity of their galaxy populations, leads to high distinction from the field in overdensity space, and allows accurate determination of the descendant cluster mass. This scale is valid over a range of redshifts and selection criteria. We present a procedure for estimating, given a measured galaxy overdensity, the protocluster probability and its descendant cluster mass for a range of modelling assumptions, particularly taking into account the shape of the measurement aperture. This procedure produces lower protocluster probabilities compared to previous estimates using fixed size apertures. The relationship between active galactic nucleus (AGN) and protoclusters is also investigated and shows significant evolution with redshift; at z ∼ 2, the fraction of protoclusters traced by AGN is high, but the fraction of all AGNs in protoclusters is low, whereas atz ≥ 5 the fraction of protoclusters containing AGN is low, but most AGNs are in protoclusters. We also find indirect evidence for the emergence of a passive sequence in protoclusters at z ∼ 2, and note that a significant fraction of all galaxies reside in protoclusters at z ≥ 2, particularly the most massive

Dust-obscured star-forming galaxies in the early universe

Motivated by recent observational constraints on dust reprocessed emission in star-forming galaxies at z ∼ 6 and above, we use the very large cosmological hydrodynamical simulation BLUETIDES to explore predictions for the amount of dust-obscured star formation in the early Universe (z > 8). BLUETIDES matches current observational constraints on both the UV luminosity function and galaxy stellar mass function and predicts that approximately 90 per cent of the star formation in high-mass (M* > 1010 M⊙) galaxies at z = 8 is already obscured by dust. The relationship between dust attenuation and stellar mass predicted by BLUETIDES is consistent with that observed at lower redshift. However, observations of several individual objects at z > 6 are discrepant with the predictions, though it is possible that their uncertainties may have been underestimated. We find that the predicted surface density of z ≥ 8 submm sources is below that accessible to current Herschel, SCUBA-2 and Atacama Large Millimetre Array (ALMA) submm surveys. However, as ALMA continues to accrue an additional surface area the population of z > 8 dust-obscured galaxies may become accessible in the near future

Learning the relationship between galaxies spectra and their star formation histories using convolutional neural etworks and cosmological simulations

We present a new method for inferring galaxy star formation histories (SFH) using machine learning methods coupled with two cosmological hydrodynamic simulations. We train Convolutional Neural Networks to learn the relationship between synthetic galaxy spectra and high resolution SFHs from the EAGLE and Illustris models. To evaluate our SFH reconstruction we use Symmetric Mean Absolute Percentage Error (SMAPE), which acts as a true percentage error in the low-error regime. On dust-attenuated spectra we achieve high test accuracy (median SMAPE = 10.5%). Including the effects of simulated observational noise increases the error (12.5%), however this is alleviated by including multiple realisations of the noise, which increases the training set size and reduces overfitting (10.9%). We also make estimates for the observational and modelling errors. To further evaluate the generalisation properties we apply models trained on one simulation to spectra from the other, which leads to only a small increase in the error (median SMAPE ∼15%⁠). We apply each trained model to SDSS DR7 spectra, and find smoother histories than in the VESPA catalogue. This new approach complements the results of existing SED fitting techniques, providing star formation histories directly motivated by the results of the latest cosmological simulations