| Records |
| Author |
Villaescusa-Navarro, F. et al; Villanueva-Domingo, P. |
Title  |
The CAMELS Project: Public Data Release |
Type |
Journal Article |
| Year |
2023 |
Publication |
Astrophysical Journal Supplement Series |
Abbreviated Journal |
Astrophys. J. Suppl. Ser. |
| Volume |
265 |
Issue |
2 |
Pages |
54 - 14pp |
| Keywords |
Cosmology; Hydrodynamical simulations; Astrostatistics; Galaxy formation |
| Abstract |
The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N-body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Lya spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at . |
| Address |
[Villaescusa-Navarro, Francisco; Genel, Shy; Angles-Alcazar, Daniel; Hassan, Sultan; Pisani, Alice; Wong, Kaze W. K.; Coulton, William R.; Steinwandel, Ulrich P.; Spergel, David N.; Burkhart, Blakesley; Wandelt, Benjamin; Somerville, Rachel S.; Bryan, Greg L.; Li, Yin] Flatiron Inst, Ctr Computat Astrophys, 162 5th Ave, New York, NY 10010 USA, Email: camel.simulations@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:000964876300001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5525 |
| Permanent link to this record |
| |
|
| |
| 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 |
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 |
| Permanent link to this record |
| |
|
| |
| Author |
Villanueva-Domingo, P.; Villaescusa-Navarro, F. |
| Title |
Removing Astrophysics in 21 cm Maps with Neural Networks |
Type |
Journal Article |
| Year |
2021 |
Publication |
Astrophysical Journal |
Abbreviated Journal |
Astrophys. J. |
| Volume |
907 |
Issue |
1 |
Pages |
44 - 14pp |
| Keywords |
Cosmology; Cold dark matter; Dark matter; Dark matter distribution; H I line emission; Intergalactic medium; Cosmological evolution; Convolutional neural networks; Large-scale structure of the universe |
| Abstract |
Measuring temperature fluctuations in the 21 cm signal from the epoch of reionization and the cosmic dawn is one of the most promising ways to study the universe at high redshifts. Unfortunately, the 21 cm signal is affected by both cosmology and astrophysics processes in a nontrivial manner. We run a suite of 1000 numerical simulations with different values of the main astrophysical parameters. From these simulations we produce tens of thousands of 21 cm maps at redshifts 10 <= z <= 20. We train a convolutional neural network to remove the effects of astrophysics from the 21 cm maps and output maps of the underlying matter field. We show that our model is able to generate 2D matter fields not only that resemble the true ones visually but whose statistical properties agree with the true ones within a few percent down to scales 2 Mpc(-1). We demonstrate that our neural network retains astrophysical information that can be used to constrain the value of the astrophysical parameters. Finally, we use saliency maps to try to understand which features of the 21 cm maps the network is using in order to determine the value of the astrophysical parameters. |
| Address |
[Villanueva-Domingo, Pablo] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, Apartado Correos 22085, E-46071 Valencia, Spain, Email: Pablo.Villanueva@ific.uv.es; |
| 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:000612333400001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4698 |
| Permanent link to this record |
| |
|
| |
| 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 |
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 |
| Permanent link to this record |
| |
|
| |
| 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 |
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 |
