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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(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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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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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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AMON Team, H. A. W. C. and I. C. C.(A. S., H.A. et al), & Salesa Greus, F. (2021). Multimessenger Gamma-Ray and Neutrino Coincidence Alerts Using HAWC and IceCube Subthreshold Data. Astrophys. J., 906(1), 63–10pp.
Abstract: The High Altitude Water Cerenkov (HAWC) and IceCube observatories, through the Astrophysical Multimessenger Observatory Network (AMON) framework, have developed a multimessenger joint search for extragalactic astrophysical sources. This analysis looks for sources that emit both cosmic neutrinos and gamma rays that are produced in photohadronic or hadronic interactions. The AMON system is running continuously, receiving subthreshold data (i.e., data that are not suited on their own to do astrophysical searches) from HAWC and IceCube, and combining them in real time. Here we present the analysis algorithm, as well as results from archival data collected between 2015 June and 2018 August, with a total live time of 3.0 yr. During this period we found two coincident events that have a false-alarm rate (FAR) of <1 coincidence yr(-1), consistent with the background expectations. The real-time implementation of the analysis in the AMON system began on 2019 November 20 and issues alerts to the community through the Gamma-ray Coordinates Network with an FAR threshold of <4 coincidences yr(-1).
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