Galaxy formation and evolution

Abstract:

We study the stellar mass-iron metallicity relation of dwarf galaxies in the new high-resolution megatron cosmological radiation-hydrodynamics simulations. These simulations model galaxy formation up to z ≈ 8 in a region that will collapse into a Milky-Way-like galaxy at z = 0, while self-consistently tracking Population III and II (Pop. III, Pop. II) star formation, feedback and chemical enrichment. megatron dwarf galaxies are in excellent agreement with the observed stellar mass-metallicity relation at z = 0, including an over-abundance of dwarfs along a flat plateau in metallicity (⟨[Fe/H]⟩ ≈ −2.5) at low stellar masses (M⋆ ≤ 10⁵ M⊙). We tie this feature to the chemical enrichment of dwarf galaxies by Pop. III pair-instability supernova (PISN) explosions. The strong Lyman-Werner background (LW) from the protogalaxy ensures that PISNe occur in haloes massive enough (≈ 10⁷ M⊙) to retain their ejecta. We also predict a tail of ≈ 20% of iron-deficient (⟨[Fe/H]⟩ ≤ −3) dwarf galaxies. We show that both plateau and tail (i) are robust to large variations in Pop. II feedback assumptions, and (ii) survive in bound satellites surrounding the central galaxy at z = 0.

Abstract:

We present the MEGATRON suite of cosmological radiation hydrodynamics simulations following the formation of Milky Way-mass galaxies from the earliest cosmic epochs when Population III stars form to Cosmic Noon. The suite represents the first set of cosmological simulations that couples a vast nonequilibrium thermochemistry network of primordial species, metals, and molecules to multifrequency, on-the-fly radiation transport, allowing us to directly predict the spectral properties of early galaxies. By initializing the simulations at zero metallicity, resolving haloes well below the atomic cooling threshold, reaching parsec-scale resolution, and modeling a Milky Way-mass environment, we aim to address four key science themes: 1) Star formation at cosmic dawn, 2) Galaxy formation and the interstellar medium in the epoch of reionization, 3) The circumgalactic medium towards cosmic noon, and 4) Reionization in a local volume environment and near-field cosmology. In this introductory work, we present an overview of the physical characteristics of high-redshift MEGATRON galaxies and their environment at z > 8. We present a library of > 175, 000 simulated galaxy spectra and demonstrate how much of the diversity of galaxy spectra seen by JWST is naturally reproduced in the context of a ΛCDM cosmology. Caveats are discussed, such as the lack of AGN in our simulations and the limitations of our adopted stellar population and chemical yield models. This project represents a step towards making more direct comparisons between simulations and observations and is particularly applicable for optimizing methods to infer galaxy properties from existing high-redshift JWST spectra and imaging data.

Abstract:

The advent of integral field data has revolutionised the study of galaxy evolution. A key component of this is dynamical modelling methods which have allowed for crucial insights to be made from kinematic data. Despite this importance, most dynamical models make a number of key assumptions which do not hold for real galaxies. These include assumptions about the geometry (axisymmetry or triaxiality), the shape of the velocity ellipsoid, and the shape of the underlying stellar distribution. At the same time, machine learning methods are becoming increasingly powerful, with many applications appearing in astronomy. As a first step towards building new dynamical modelling methods with machine learning, it is important to understand the types of machine learning architectures that are best fit for dynamical modelling. To investigate this, we construct a training set of dynamical models of early-type galaxies using Jeans Anisotropic Modelling (JAM). We then train a neural network on this data using the parameters of JAM and mock photometry as the input. We are able to accurately model JAM galaxies with relatively simple machine learning architectures, leading to a significant speed increase over traditional JAM modelling.

Abstract:

Abstract We present morphological classifications of over 41 000 galaxies out to zphot ∼ 2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of mHSC − i = 21.5. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45 000 subjects, including more than 41 000 galaxies (median zphot of 0.42 ± 0.23), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) collaboration and the newly operational Vera C. Rubin Observatory.