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ANTARES Collaboration(Albert, A. et al), Barrios-Marti, J., Coleiro, A., Colomer, M., Gozzini, R., Hernandez-Rey, J. J., et al. (2019). ANTARES Neutrino Search for Time and Space Correlations with IceCube High-energy Neutrino Events. Astrophys. J., 879(2), 108–8pp.
Abstract: In past years the IceCube Collaboration has reported the observation of astrophysical high-energy neutrino events in several analyses. Despite compelling evidence for the first identification of a neutrino source, TXS 0506+056, the origin of the majority of these events is still unknown. In this paper, we search for a possible transient origin of the IceCube astrophysical events using neutrino events detected by the ANTARES telescope. The arrival time and direction of 6894 track-like and 160 shower-like events detected over 2346 days of livetime are examined to search for coincidences with 54 IceCube high-energy track-like neutrino events, by means of a maximum likelihood method. No significant correlation is observed and upper limits on the one-flavor neutrino fluence from the direction of the IceCube candidates are derived. The nonobservation of time and space correlation within the time window of 0.1 days with the two most energetic IceCube events constrains the spectral index of a possible point-like transient neutrino source to be harder than -2.3 and -2.4 for each event, respectively.
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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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Salesa Greus, F., & Sanchez Losa, A. (2021). Multimessenger Astronomy with Neutrinos. Universe, 7(11), 397–11pp.
Abstract: Multimessenger astronomy is arguably the branch of the astroparticle physics field that has seen the most significant developments in recent years. In this manuscript, we will review the state-of-the-art, the recent observations, and the prospects and challenges for the near future. We will give special emphasis to the observation carried out with neutrino telescopes.
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