| Records |
| Author |
Villanueva-Domingo, P.; Ichiki, K. |
| Title |
21 cm forest constraints on primordial black holes |
Type |
Journal Article |
| Year |
2023 |
Publication |
Publications of the Astronomical Society of Japan |
Abbreviated Journal |
Publ. Astron. Soc. Jpn. |
| Volume |
75 |
Issue |
SP1 |
Pages |
S33-S49 |
| Keywords |
dark matter; radio lines: ISM |
| Abstract |
Primordial black holes (PBHs) as part of the dark matter (DM) would modify the evolution of large-scale structures and the thermal history of the universe. Future 21 cm forest observations, sensitive to small scales and the thermal state of the intergalactic medium (IGM), could probe the existence of such PBHs. In this article, we show that the shot noise isocurvature mode on small scales induced by the presence of PBHs can enhance the amount of low-mass halos, or minihalos, and thus, the number of 21 cm absorption lines. However, if the mass of PBHs is as large as M-PBH greater than or similar to 10 M-circle dot, with an abundant enough fraction of PBHs as DM, f(PBH), the IGM heating due to accretion on to the PBHs counteracts the enhancement due to the isocurvature mode, reducing the number of absorption lines instead. The concurrence of both effects imprints distinctive signatures on the number of absorbers, allowing the abundance of PBHs to be bound. We compute the prospects for constraining PBHs with future 21 cm forest observations, finding achievable competitive upper limits on the abundance as low as f(PBH) similar to 10(-3) at M-PBH = 100 M-circle dot, or even lower at larger masses, in regions of the parameter space unexplored by current probes. The impact of astrophysical X-ray sources on the IGM temperature is also studied, which could potentially weaken the bounds. |
| Address |
[Villanueva-Domingo, Pablo] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, Apartado Correos 22085, E-46071 Valencia, Spain, Email: ichiki@a.phys.nagoya-u.ac.jp |
| Corporate Author |
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Thesis |
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| Publisher |
Oxford Univ Press |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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ISSN  |
0004-6264 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000768441900001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5168 |
| Permanent link to this record |
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| Author |
Villanueva-Domingo, P.; Gnedin, N.Y.; Mena, O. |
| Title |
Warm Dark Matter and Cosmic Reionization |
Type |
Journal Article |
| Year |
2018 |
Publication |
Astrophysical Journal |
Abbreviated Journal |
Astrophys. J. |
| Volume |
852 |
Issue |
2 |
Pages |
139 - 7pp |
| Keywords |
cosmology: theory; galaxies: formation; intergalactic medium; large-scale structure of universe; methods: numerical |
| Abstract |
In models with dark matter made of particles with keV masses, such as a sterile neutrino, small-scale density perturbations are suppressed, delaying the period at which the lowest mass galaxies are formed and therefore shifting the reionization processes to later epochs. In this study, focusing on Warm Dark Matter (WDM) with masses close to its present lower bound, i.e., around the 3. keV region, we derive constraints from galaxy luminosity functions, the ionization history and the Gunn-Peterson effect. We show that even if star formation efficiency in the simulations is adjusted to match the observed UV galaxy luminosity functions in both CDM and WDM models, the full distribution of Gunn-Peterson optical depth retains the strong signature of delayed reionization in the WDM model. However, until the star formation and stellar feedback model used in modern galaxy formation simulations is constrained better, any conclusions on the nature of dark matter derived from reionization observables remain model-dependent. |
| Address |
[Villanueva-Domingo, Pablo; Mena, Olga] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Apartado Correos 22085, E-46071 Valencia, Spain, Email: gnedin@fnal.gov |
| Corporate Author |
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Thesis |
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| Publisher |
Iop Publishing Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0004-637x |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000422865600009 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
3455 |
| Permanent link to this record |
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| 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 |
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Thesis |
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| Publisher |
Iop Publishing Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0004-637x |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000612333400001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4698 |
| Permanent link to this record |
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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 |
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| 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 |
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Thesis |
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| Publisher |
IOP Publishing Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0004-637x |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000838320900001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5325 |
| Permanent link to this record |
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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 |
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| 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 |
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Thesis |
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| Publisher |
IOP Publishing Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0067-0049 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000780035300001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5194 |
| Permanent link to this record |
