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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ANTARES Collaboration(Adrian-Martinez, S. et al), Bigongiari, C., Dornic, D., Emanuele, U., Gomez-Gonzalez, J. P., Hernandez-Rey, J. J., et al. (2012). Search for Cosmic Neutrino Point Sources with Four Years of Data from the Antares Telescope. Astrophys. J., 760(1), 53–10pp.
Abstract: In this paper, a time-integrated search for point sources of cosmic neutrinos is presented using the data collected from 2007 to 2010 by the ANTARES neutrino telescope. No statistically significant signal has been found and upper limits on the neutrino flux have been obtained. Assuming an E-nu(-2). spectrum, these flux limits are at 1-10x10(-8) GeV cm(-2) s(-1) for declinations ranging from -90 degrees to 40 degrees. Limits for specific models of RX J1713.7-3946 and Vela X, which include information on the source morphology and spectrum, are also given.
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ANTARES Collaboration(Albert, A. et al), Alves, S., Carretero, V., Colomer, M., Hernandez-Rey, J. J., Khan-Chowdhury, N. R., et al. (2021). Search for Neutrinos from the Tidal Disruption Events AT2019dsg and AT2019fdr with the ANTARES Telescope. Astrophys. J., 920(1), 50–6pp.
Abstract: On 2019 October 1, the IceCube Collaboration detected a muon track neutrino with a high probability of being of astrophysical origin, IC191001A. After a few hours, the tidal disruption event (TDE) AT2019dsg, observed by the Zwicky Transient Facility (ZTF), was indicated as the most likely counterpart of the IceCube track. More recently, the follow-up campaign of the IceCube alerts by ZTF suggested a second TDE, AT2019fdr, as a promising counterpart of another IceCube muon track candidate, IC200530A, detected on 2020 May 30. Here, these intriguing associations are followed-up by searching for neutrinos in the ANTARES detector from the directions of AT2019dsg and AT2019fdr using a time-integrated approach. As no significant evidence for space clustering is found in the ANTARES data, upper limits on the one-flavor neutrino flux and fluence are set.
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ANTARES Collaboration(Albert, A. et al), Colomer, M., Gozzini, R., Hernandez-Rey, J. J., Illuminati, G., Khan-Chowdhury, N. R., et al. (2021). ANTARES Search for Point Sources of Neutrinos Using Astrophysical Catalogs: A Likelihood Analysis. Astrophys. J., 911(1), 48–11pp.
Abstract: A search for astrophysical pointlike neutrino sources using the data collected by the ANTARES detector between 2007 January 29 and 2017 December 31 is presented. A likelihood method is used to assess the significance of an excess of muon neutrinos inducing track-like events in correlation with the location of a list of possible sources. Different sets of objects are tested in the analysis: (a) a subsample of the Fermi 3LAC catalog of blazars, (b) a jet-obscured population of active galactic nuclei, (c) a sample of hard X-ray selected radio galaxies, (d) a star-forming galaxy catalog, and (e) a public sample of 56 very-high-energy track events from the IceCube experiment. None of the tested sources shows a significant association with the sample of neutrinos detected by ANTARES. The smallest p-value is obtained for the catalog of radio galaxies with an equal-weights hypothesis, with a pre-trial p-value equivalent to a 2.8 sigma excess, which is equivalent to 1.6 sigma post-trial. In addition, the results of a dedicated analysis for the blazar MG3 J225517+2409 are also reported: this source is found to be the most significant within the Fermi 3LAC sample, with five ANTARES events located less than one degree from the source. This blazar showed evidence of flaring activity in Fermi data, in spacetime coincidence with a high-energy track detected by IceCube. An a posteriori significance of 2.6 sigma for the combination of ANTARES and IceCube data is reported.
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Villanueva-Domingo, P., & Villaescusa-Navarro, F. (2021). Removing Astrophysics in 21 cm Maps with Neural Networks. Astrophys. J., 907(1), 44–14pp.
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.
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