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Author Villaescusa-Navarro, F. et al; Villanueva-Domingo, P.
Title The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence Type Journal Article
Year 2022 Publication Astrophysical Journal Supplement Series Abbreviated Journal Astrophys. J. Suppl. Ser.
Volume 259 Issue 2 Pages 61 - 14pp
Keywords
Abstract (down) We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span similar to 100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.
Address [Villaescusa-Navarro, Francisco; Nicola, Andrina; Spergel, David N.; Matilla, Jose Manuel Zorrilla; Shao, Helen] Princeton Univ, Dept Astrophys Sci, Peyton Hall, Princeton, NJ 08544 USA, Email: villaescusa.francisco@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 0067-0049 ISBN Medium
Area Expedition Conference
Notes WOS:000780035300001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5194
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Author Villanueva-Domingo, P.; Villaescusa-Navarro, F.; Genel, S.; Angles-Alcazar, D.; Hernquist, L.; Marinacci, F.; Spergel, D.N.; Vogelsberger, M.; Narayanan, D.
Title Weighing the Milky Way and Andromeda galaxies with artificial intelligence Type Journal Article
Year 2023 Publication Physical Review D Abbreviated Journal Phys. Rev. D
Volume 107 Issue 10 Pages 103003 - 8pp
Keywords
Abstract (down) We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on 2,000 state-of-the-art hydrodynamic simulations of the CAMELS project, only make use of the positions, velocities and stellar masses of the galaxies belonging to the halos, and are able to perform likelihood-free inference on halo masses while accounting for both cosmological and astrophysical uncertainties. Our constraints are in agreement with estimates from other traditional methods, within our derived posterior standard deviation.
Address [Villanueva-Domingo, Pablo; Narayanan, Desika] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, E-46980 Paterna, Spain, Email: pablo.villanueva.domingo@gmail.com;
Corporate Author Thesis
Publisher Amer Physical Soc Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2470-0010 ISBN Medium
Area Expedition Conference
Notes WOS:000988340900001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5539
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Author Witte, S.; Villanueva-Domingo, P.; Gariazzo, S.; Mena, O.; Palomares-Ruiz, S.
Title EDGES result versus CMB and low-redshift constraints on ionization histories Type Journal Article
Year 2018 Publication Physical Review D Abbreviated Journal Phys. Rev. D
Volume 97 Issue 10 Pages 103533 - 8pp
Keywords
Abstract (down) We examine the results from the Experiment to Detect the Global Epoch of Reionization Signature (EDGES), which has recently claimed the detection of a strong absorption in the 21 cm hyperfine transition line of neutral hydrogen, at redshifts demarcating the early stages of star formation. More concretely, we study the compatibility of the shape of the EDGES absorption profile, centered at a redshift of z similar to 17.2, with measurements of the reionization optical depth, the Gunn-Peterson optical depth, and Lyman-alpha emission from star-forming galaxies, for a variety of possible reionization models within the standard ACDM framework (that is, a Universe with a cosmological constant. and cold dark matter CDM). When, conservatively, we only try to accommodate the location of the absorption dip, we identify a region in the parameter space of the astrophysical parameters that successfully explains all of the aforementioned observations. However, one of the most abnormal features of the EDGES measurement is the absorption amplitude, which is roughly a factor of 2 larger than the maximum allowed value in the ACDM framework. We point out that the simple considered astrophysical models that produce the largest absorption amplitudes are unable to explain the depth of the dip and of reproducing the observed shape of the absorption profile.
Address [Witte, Samuel; Villanueva-Domingo, Pablo; Gariazzo, Stefano; Mena, Olga; Palomares-Ruiz, Sergio] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Apartado Correos 22085, E-46071 Valencia, Spain
Corporate Author Thesis
Publisher Amer Physical Soc Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2470-0010 ISBN Medium
Area Expedition Conference
Notes WOS:000433291600010 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 3606
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Author Villanueva-Domingo, P.; Mena, O.; Miralda-Escude, J.
Title Maximum amplitude of the high-redshift 21-cm absorption feature Type Journal Article
Year 2020 Publication Physical Review D Abbreviated Journal Phys. Rev. D
Volume 101 Issue 8 Pages 083502 - 8pp
Keywords
Abstract (down) We examine the maximum possible strength of the global 21-cm absorption dip on the cosmic background radiation at high-redshift caused by the atomic intergalactic medium, when the Lyman-alpha coupling is maximum, assuming no exotic cooling mechanisms from interactions with dark matter. This maximum absorption is limited by three inevitable factors that need to be accounted for: (a) heating by energy transferred from the cosmic background radiation to the hydrogen atoms via 21-cm transitions, dubbed as 21-cm heating; (b) Ly alpha heating by scatterings of Ly alpha photons from the first stars; (c) the impact of the expected density fluctuations in the intergalactic gas in standard cold dark matter theory, which reduces the mean 21-cm absorption signal. Inclusion of this third novel effect reduces the maximum global 21-cm absorption by similar to 10%. Overall, the three effects studied here reduce the 21-cm global absorption by similar to 20% at z similar or equal to 17.
Address [Villanueva-Domingo, Pablo; Mena, Olga] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Apartado Correos 22085, E-46071 Paterna, Spain, Email: pablo.villanueva@ific.uv.es;
Corporate Author Thesis
Publisher Amer Physical Soc Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2470-0010 ISBN Medium
Area Expedition Conference
Notes WOS:000523343100006 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 4360
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Author Villanueva-Domingo, P.; Villaescusa-Navarro, F.; Angles-Alcazar, D.; Genel, S.; Marinacci, F.; Spergel, D.N.; Hernquist, L.; Vogelsberger, M.; Dave, R.; Narayanan, D.
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 (down) 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
Permanent link to this record