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Author (up) Villanueva-Domingo, P.; Villaescusa-Navarro, F.; Angles-Alcazar, D.; Genel, S.; Marinacci, F.; Spergel, D.N.; Hernquist, L.; Vogelsberger, M.; Dave, R.; Narayanan, D. url  doi
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  Title Inferring Halo Masses with Graph Neural Networks Type Journal Article
  Year 2022 Publication Astrophysical Journal Abbreviated Journal Astrophys. J.  
  Volume 935 Issue 1 Pages 30 - 15pp  
  Keywords  
  Abstract Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet).  
  Address [Villanueva-Domingo, Pablo] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, E-46980 Paterna, Spain, Email: pablo.villanueva.domingo@gmail.com;  
  Corporate Author Thesis  
  Publisher IOP Publishing Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0004-637x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000838320900001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5325  
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