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The properties of galaxies in low-density regions of the universe suggest an interplay between galaxy formation and environment. However, the specific reason why this particular large-scale environment influences the evolution of galaxies remains unclear. This paper examines the properties and evolutionary paths of galaxies within cosmic voids using the Illustris TNG300 simulation. The population of void galaxies at z = 0 has a higher star formation rate, a smaller stellar-to-halo-mass ratio, higher gas metallicity, and lower stellar metallicity in comparison with non-void galaxies at fixed stellar mass. Our analysis shows that these differences are mainly due to the characteristics of galaxies classified as satellites, for which the largest differences between void and non-void samples are found. Although the mean number of mergers is similar between void and non-void samples at a fixed stellar mass, void galaxies tend to experience mergers at later times, resulting in a more recent accumulation of accreted stellar mass. While the mean net accreted mass is comparable for high-mass galaxies, low-mass void galaxies tend to exhibit higher fractions of accreted stars than non-void galaxies. This finding challenges the common notion that void galaxies predominantly experience growth with infrequent mergers or interactions. With this paper, we also publicly release our void catalogue as part of the IllustrisTNG project. [ABSTRACT FROM AUTHOR]

Cosmological galaxy formation simulations are powerful tools to understand the complex processes that govern the formation and evolution of galaxies. However, evaluating the realism of these simulations remains a challenge. The two common approaches for evaluating galaxy simulations is either through scaling relations based on a few key physical galaxy properties, or through a set of pre-defined morphological parameters based on galaxy images. This paper proposes a novel image-based method for evaluating the quality of galaxy simulations using unsupervised deep learning anomaly detection techniques. By comparing full galaxy images, our approach can identify and quantify discrepancies between simulated and observed galaxies. As a demonstration, we apply this method to SDSS imaging and NIHAO simulations with different physics models, parameters, and resolution. We further compare the metric of our method to scaling relations as well as morphological parameters. We show that anomaly detection is able to capture similarities and differences between real and simulated objects that scaling relations and morphological parameters are unable to cover, thus indeed providing a new point of view to validate and calibrate cosmological simulations against observed data. [ABSTRACT FROM AUTHOR]

Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to the real one is difficult. In this paper of the Extracting Reality from Galaxy Observables with Machine Learning series, we utilize contrastive learning to directly compare a large sample of simulated and observed galaxies based on their stellar-light images. This eliminates the need to specify summary statistics and allows to exploit the whole information content of the observations. We produce survey-realistic galaxy mock data sets resembling real Hyper Suprime-Cam (HSC) observations using the cosmological simulations TNG50 and TNG100. Our focus is on galaxies with stellar masses between 109 and 1012 M⊙ at z  = 0.1–0.4. This allows us to evaluate the realism of the simulated TNG galaxies in comparison to actual HSC observations. We apply the self-supervised contrastive learning method Nearest Neighbour Contrastive Learning to the images from both simulated and observed data sets (g -, r -, i -bands). This results in a 256-dimensional representation space, encoding all relevant observable galaxy properties. First, this allows us to identify simulated galaxies that closely resemble real ones by seeking similar images in this multidimensional space. Even more powerful, we quantify the alignment between the representations of these two image sets, finding that the majority (≳ 70 per cent) of the TNG galaxies align well with observed HSC images. However, a subset of simulated galaxies with larger sizes, steeper Sérsic profiles, smaller Sérsic ellipticities, and larger asymmetries appears unrealistic. We also demonstrate the utility of our derived image representations by inferring properties of real HSC galaxies using simulated TNG galaxies as the ground truth. [ABSTRACT FROM AUTHOR]

Multiphase galactic outflows, generated by supernova (SN) feedback, are likely to be more metal rich than the interstellar media from which they are driven due to incomplete mixing between SN ejecta and the ambient interstellar medium. This enrichment is important for shaping galactic metallicities and metallicity gradients, but measuring it quantitatively from simulations requires resolution high enough to resolve mass, momentum and energy exchanges between the different phases of the outflows. In this context, we present QED, which are simulations of outflows, driven by SN feedback, conducted using Quokka , a new GPU-optimized adaptive mesh refinement radiation-hydrodynamics code. This code allows us to reach combinations of resolution, simulation volume, and simulation duration larger than those that have previously been possible, and to resolve all gas phases from cold neutral medium, T ∼ 100 K, to hot ionized gas, T ≳ 107 K. In this, a first of a series of papers exploring generation and evolution of multiphase outflows from a wide range of galactic environments and star formation rates, we quantify the extent of selective metal loading in solar neighbourhood-like environments. We explain the selective metal loading, we find as a result of the transport of metals within and between phases, a phenomenon we can study owing to the parsec-scale resolution that our simulations achieve. We also quantify the sensitivity of metal loading studies to numerical resolution, and present convergence criteria for future studies. [ABSTRACT FROM AUTHOR]

Arkenstone is a new model for multiphase, stellar feedback-driven galactic winds designed for inclusion in coarse resolution cosmological simulations. In this first paper of a series, we describe the features that allow Arkenstone to properly treat high specific energy wind components and demonstrate them using idealized non-cosmological simulations of a galaxy with a realistic circumgalactic medium (CGM), using the arepo code. Hot, fast gas phases with low mass loadings are predicted to dominate the energy content of multiphase outflows. In order to treat the huge dynamic range of spatial scales involved in cosmological galaxy formation at feasible computational expense, cosmological volume simulations typically employ a Lagrangian code or else use adaptive mesh refinement with a quasi-Lagrangian refinement strategy. However, it is difficult to inject a high specific energy wind in a Lagrangian scheme without incurring artificial burstiness. Additionally, the low densities inherent to this type of flow result in poor spatial resolution. Arkenstone addresses these issues with a novel scheme for coupling energy into the transition region between the interstellar medium (ISM) and the CGM, while also providing refinement at the base of the wind. Without our improvements, we show that poor spatial resolution near the sonic point of a hot, fast outflow leads to an underestimation of gas acceleration as the wind propagates. We explore the different mechanisms by which low and high specific energy winds can regulate the star formation rate of galaxies. In future work, we will demonstrate other aspects of the Arkenstone model. [ABSTRACT FROM AUTHOR]