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AMON and ANTARES Collaborations(Ayala Solares, H. A. et al), Barrios-Marti, J., Coleiro, A., Colomer, M., Gozzini, R., Hernandez-Rey, J. J., et al. (2019). A Search for Cosmic Neutrino and Gamma-Ray Emitting Transients in 7.3 yr of ANTARES and Fermi LAT Data. Astrophys. J., 886(2), 98–8pp.
Abstract: We analyze 7.3 yr of ANTARES high-energy neutrino and Fermi Large Area Telescope (LAT) gamma-ray data in search of cosmic neutrino + gamma-ray (nu + gamma) transient sources or source populations. Our analysis has the potential to detect either individual nu + gamma transient sources (durations delta t less than or similar to 1000 s), if they exhibit sufficient gamma-ray or neutrino multiplicity, or a statistical excess of nu + gamma transients of individually lower multiplicities. Individual high gamma-ray multiplicity events could be produced, for example, by a single ANTARES neutrino in coincidence with a LAT-detected gamma-ray burst. Treating ANTARES track and cascade event types separately, we establish detection thresholds by Monte Carlo scrambling of the neutrino data, and determine our analysis sensitivity by signal injection against these scrambled data sets. We find our analysis is sensitive to nu + gamma transient populations responsible for >5% of the observed gamma-coincident neutrinos in the track data at 90% confidence. Applying our analysis to the unscrambled data reveals no individual nu + gamma events of high significance; two ANTARES track + Fermi gamma-ray events are identified that exceed a once per decade false alarm rate threshold (p = 17%). No evidence for subthreshold nu + gamma source populations is found among the track (p = 39%) or cascade (p = 60%) events. Exploring a possible correlation of high-energy neutrino directions with Fermi gamma-ray sky brightness identified in previous work yields no added support for this correlation. While TXS.0506+056, a blazar and variable (nontransient) Fermi gamma-ray source, has recently been identified as the first source of high-energy neutrinos, the challenges in reconciling observations of the Fermi gamma-ray sky, the IceCube high-energy cosmic neutrinos, and ultrahigh-energy cosmic rays using only blazars suggest a significant contribution by other source populations. Searches for transient sources of high-energy neutrinos thus remain interesting, with the potential for either neutrino clustering or multimessenger coincidence searches to lead to discovery of the first nu + gamma transients.
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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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