Journal article
A deep learning approach to halo merger tree construction
S Robles, JS Gómez, A Ramírez Rivera, ND Padilla, D Dujovne
Monthly Notices of the Royal Astronomical Society | OXFORD UNIV PRESS | Published : 2022
Abstract
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..
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Awarded by Horizon 2020 Framework Programme
Funding Acknowledgements
SR was partially supported by MINECO/FEDER (Spain) under grant PGC2018-094975-C2, by the UK STFC grant ST/T000759/1, and by the Australian Research Council through the ARC Centre of Excellence for Dark Matter Particle Physics, CE200100008. SR also acknowledges funding from the European Union's Horizon 2020 - Research and Innovation Framework Programme under the Marie Sklodowska-Curie grant agreement no. 734374 (LACEGAL-RISE) for a secondment at the Pontificia Universidad Catolica de Chile. JSG acknowledges support from CONICYT project Basal AFB-170002, funding from the CONICYT PFCHA/DOCTORADO BECAS CHILE/2019 21191147, and the Predoctoral contract 'Formacion de Personal Investigador' from the Universidad Autonoma de Madrid (FPI-UAM, 2021). ARR acknowledges support from the Brazilian National Council for Scientific and Technological Development (CNPq) under grant no. 307425/2017-7. ARR was also with University of Campinas and University of Reykjavik while working on this research. This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200. This work also used computer facilities at the Universidad Autonoma de Madrid and the Geryon computer at the Center for Astro-Engineering UC, part of the BASAL PFB-06, which received additional funding from QUIMAL 130008 and Fondequip AIC-57 for upgrades. The authors would also like to thank the CYTED AgIoT Project (520rt0011), CORFO CoTH2O Consortium, and Proyecto Asociativo UDP 'Plataformas Digitales como modelo organizacional', for their support.