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The new generation of upcoming deep photometric and spectroscopic surveys will allow us to measure the astrophysical properties of faint galaxies in massive clusters. This would demand to produce simulations of galaxy clusters with better mass resolution than the ones available today if we want to make comparisons between the upcoming observations and predictions of cosmological models. But producing full-physics hydrodynamical simulations of the most massive clusters is not an easy task. This would involve billions of computational elements to reliably resolve low mass galaxies similar to those measured in observations. On the other hand, dark matter only simulations of cluster size halos can be done with much larger mass resolution but at the cost of having to apply a model that populate galaxies within each of the subhalos in these simulations. In this paper we present the results of a new set of dark matter only simulations with different mass resolutions within the THE THREE HUNDRED project. We have generated catalogs of galaxies with stellar and luminosity properties by applying the SAGE Semi-Analytical Model of galaxy formation. To obtain the catalogs consistent with the results from hydrodynamical simulations, the internal physical parameters of SAGE were calibrated with the Particle Swarm Optimization method using a subset of full-physics runs with the same mass resolution than the dark matter only ones., Comment: To appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

A key ingredient for semi-analytic models (SAMs) of galaxy formation is the mass assembly history of haloes, encoded in a tree structure. The most commonly used method to construct halo merger histories is based on the outcomes of high-resolution, computationally intensive N-body simulations. We show that machine learning (ML) techniques, in particular Generative Adversarial Networks (GANs), are a promising new tool to tackle this problem with a modest computational cost and retaining the best features of merger trees from simulations. We train our GAN model with a limited sample of merger trees from the Evolution and Assembly of GaLaxies and their Environments (EAGLE) simulation suite, constructed using two halo finders-tree builder algorithms: SUBFIND-D-TREES and ROCKSTAR-ConsistentTrees. Our GAN model successfully learns to generate well-constructed merger tree structures with high temporal resolution, and to reproduce the statistical features of the sample of merger trees used for training, when considering up to three variables in the training process. These inputs, whose representations are also learned by our GAN model, are mass of the halo progenitors and the final descendant, progenitor type (main halo or satellite) and distance of a progenitor to that in the main branch. The inclusion of the latter two inputs greatly improves the final learned representation of the halo mass growth history, especially for SUBFIND-like ML trees. When comparing equally sized samples of ML merger trees with those of the EAGLE simulation, we find better agreement for SUBFIND-like ML trees. Finally, our GAN-based framework can be utilised to construct merger histories of low- and intermediate-mass haloes, the most abundant in cosmological simulations., Comment: 17 pages, 12 figures, 3 tables, 2 appendices. Minor editorial improvements, matches published version

We examine the effect of using different halo finders and merger tree building algorithms on galaxy properties predicted using the GALFORM semi-analytical model run on a high resolution, large volume dark matter simulation. The halo finders/tree builders HBT, ROCKSTAR, SUBFIND and VELOCIRAPTOR differ in their definitions of halo mass, on whether only spatial or phase-space information is used, and in how they distinguish satellite and main haloes; all of these features have some impact on the model galaxies, even after the trees are post-processed and homogenised by GALFORM. The stellar mass function is insensitive to the halo and merger tree finder adopted. However, we find that the number of central and satellite galaxies in GALFORM does depend slightly on the halo finder/tree builder. The number of galaxies without resolved subhaloes depends strongly on the tree builder, with VELOCIRAPTOR, a phase-space finder, showing the largest population of such galaxies. The distributions of stellar masses, cold and hot gas masses, and star formation rates agree well between different halo finders/tree builders. However, because VELOCIRAPTOR has more early progenitor haloes, with these trees GALFORM produces slightly higher star formation rate densities at high redshift, smaller galaxy sizes, and larger stellar masses for the spheroid component. Since in all cases these differences are small we conclude that, when all of the trees are processed so that the main progenitor mass increases monotonically, the predicted GALFORM galaxy populations are stable and consistent for these four halo finders/tree builders., Comment: 20 pages, 16 figures

Semi-analytic models are best suited to compare galaxy formation and evolution theories with observations. These models rely heavily on halo merger trees, and their realistic features (i.e., no drastic changes on halo mass or jumps on physical locations). Our aim is to provide a new framework for halo merger tree generation that takes advantage of the results of large volume simulations, with a modest computational cost. We treat halo merger tree construction as a matrix generation problem, and propose a Generative Adversarial Network that learns to generate realistic halo merger trees. We evaluate our proposal on merger trees from the EAGLE simulation suite, and show the quality of the generated trees., Comment: 11 pages, 7 figures, 2 tables, 3 appendices. Presented at the ICML 2019 Workshop on Theoretical Physics for Deep Learning

Hyperluminous infrared galaxies (HyLIRGs) provide crucial "long lever arm" constraints on galaxy evolution. H-ATLAS $J084933.4+021443$, a $z=2.41$ binary HyLIRG with at least two additional luminous companion galaxies, is thus an optimal test-ground for studies of star formation and galaxy evolution during "cosmic noon". We have used ALMA to obtain resolved imaging and kinematics of atomic and molecular emission lines, and rest-frame $340$ to $1160$GHz continuum emission, for the known luminous component galaxies in H-ATLAS $J084933.4+021443$: W, T, M, C. All four component galaxies are spatially ($\sim 0 .\!\!^{''} 3$ or $2.5$kpc) resolved in CO J:7-6, [C$_\mathrm{I}$] 2-1, H$_2$O and the millimetre (mm) to sub-mm continuum. Rotation-dominated gas kinematics is confirmed in W and T. The significant extension to component T, in gas and continuum, along its kinematic minor axis, is attributable to its lensing magnification. Spatially resolved sub-mm spectral energy distributions reveal that component W is well fit with greybody emission from dust at a single temperature over the full extent of the galaxy, despite it containing a powerful AGN, while component T requires an additional component of hotter nuclear dust and additional sources of emission in the mm. We confirm that [C$_\mathrm{I}$] 2-1 can be used as a rough tracer of warm/dense molecular gas in extreme systems, though the [C$_\mathrm{I}$] 2-1/CO luminosity ratio increases sub-linearly. We obtain an exquisite and unprecedented resolved ($2.5$-kpc-scale) "warm/dense molecular gas" Schmidt-Kennicutt (SK) relationship for components W and T. Gas exhaustion times for all apertures in W (T) are $1-4$Gyr ($0.5-2$Gyr). Both W and T follow a resolved "warm/dense gas" SK relationship with power law $n\sim1.7$, significantly steeper than the $n\sim1$ found previously via "cold" molecular gas in nearby "normal" star-forming galaxies.