Browsing by Author "Gomez, Jonathan S."

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    A deep learning approach to halo merger tree construction
    (2022) Robles, Sandra; Gomez, Jonathan S.; Ramirez Rivera, Adin; Padilla, Nelson D.; Dujovne, Diego
    A key ingredient for semi-analytic models 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 utilized to construct merger histories of low- and intermediate-mass haloes, the most abundant in cosmological simulations.
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    Halo merger tree comparison: impact on galaxy formation models
    (2022) Gomez, Jonathan S.; Padilla, N. D.; Helly, J. C.; Lacey, C. G.; Baugh, C. M.; Lagos, C. D. P.
    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 VELOCI raptor 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 homogenized 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.