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.

Galaxies in cosmic voids have been reported with properties related to a delayed evolution with respect to the Universe in general. These characteristics reflect the interaction of galaxies with the environment. However, it is not clear the degree of influence of the large-scale structure on the properties of void galaxies or, if these are only influenced by the low local density around them typical of these regions. In this article we identified cosmic voids in the SDSS-DR16 and studied various properties of galaxies, such as g-r colour, star formation rate, and concentration. To characterise the local environment, we have identified groups of galaxies and studied their properties as a function of their dark matter and stellar masses, analysing separately those found in voids and in the general sample. Our results show that galaxies that inhabit haloes of a given mass (below \sim 10^13.5 M_\dot ), are bluer, have a higher star formation rate and are less concentrated when the host halo is inside voids compared to other regions. For larger halo masses, the trend disappears. We also analyse whether the properties of galaxies are sensitive to the type of voids that inhabit. This is done by separating voids embedded in overdense regions (S-type) from those that asymptotically converge to the average density of the universe (R-type). We found that galaxies in R-type voids are bluer, with higher SFR and less concentration than in S-type voids. Our results indicate some degree of correlation of galaxy properties with the large-scale environment provided by voids, suggesting possible second-order mechanisms in galaxy evolution.