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Villaescusa-Navarro, F. et al, & Villanueva-Domingo, P. (2022). The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence. Astrophys. J. Suppl. Ser., 259(2), 61–14pp.
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.
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Villanueva-Domingo, P., Villaescusa-Navarro, F., Angles-Alcazar, D., Genel, S., Marinacci, F., Spergel, D. N., et al. (2022). Inferring Halo Masses with Graph Neural Networks. Astrophys. J., 935(1), 30–15pp.
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).
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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2022). HAWC Study of the Ultra-high-energy Spectrum of MGRO J1908+06. Astrophys. J., 928(2), 116–13pp.
Abstract: We report TeV gamma-ray observations of the ultra-high-energy source MGRO J1908+06 using data from the High Altitude Water Cherenkov Observatory. This source is one of the highest-energy known gamma-ray sources, with emission extending past 200 TeV. Modeling suggests that the bulk of the TeV gamma-ray emission is leptonic in nature, driven by the energetic radio-faint pulsar PSR J1907+0602. Depending on what assumptions are included in the model, a hadronic component may also be allowed. Using the results of the modeling, we discuss implications for detection prospects by multi-messenger campaigns.
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HAWC Collaboration(Alfaro, R. et al), & Salesa Greus, F. (2022). Study of the Very High Energy Emission of M87 through its Broadband Spectral Energy Distribution. Astrophys. J., 934(2), 158–9pp.
Abstract: The radio galaxy M87 is the central dominant galaxy of the Virgo Cluster. Very high-energy (VHE, greater than or similar to 0.1 TeV) emission from M87 has been detected by imaging air Cherenkov telescopes. Recently, marginal evidence for VHE long-term emission has also been observed by the High Altitude Water Cherenkov Observatory, a gamma-ray and cosmic-ray detector array located in Puebla, Mexico. The mechanism that produces VHE emission in M87 remains unclear. This emission originates in its prominent jet, which has been spatially resolved from radio to X-rays. In this paper, we construct a spectral energy distribution from radio to gamma rays that is representative of the nonflaring activity of the source, and in order to explain the observed emission, we fit it with a lepto-hadronic emission model. We found that this model is able to explain nonflaring VHE emission of M87 as well as an orphan flare reported in 2005.
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Villaescusa-Navarro, F. et al, & Villanueva-Domingo, P. (2023). The CAMELS Project: Public Data Release. Astrophys. J. Suppl. Ser., 265(2), 54–14pp.
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 .
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