ANTARES Collaboration(Albert, A. et al), Barrios-Marti, J., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., Tönnis, C., et al. (2020). Model-independent search for neutrino sources with the ANTARES neutrino telescope. Astropart Phys., 114, 35–47.
Abstract: A novel method to analyse the spatial distribution of neutrino candidates recorded with the ANTARES neutrino telescope is introduced, searching for an excess of neutrinos in a region of arbitrary size and shape from any direction in the sky. Techniques originating from the domains of machine learning, pattern recognition and image processing are used to purify the sample of neutrino candidates and for the analysis of the obtained skymap. In contrast to a dedicated search for a specific neutrino emission model, this approach is sensitive to a wide range of possible morphologies of potential sources of high-energy neutrino emission. The application of these methods to ANTARES data yields a large-scale excess with a post-trial significance of 2.5 sigma. Applied to public data from IceCube in its IC40 configuration, an excess consistent with the results from ANTARES is observed with a post-trial significance of 2.1 sigma.
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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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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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ANTARES Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Hernandez-Rey, J. J., Sanchez-Losa, A., Tönnis, C., Zornoza, J. D., et al. (2016). The first combined search for neutrino point-sources in the Southern Hemisphere with the ANTARES and IceCube neutrino telescopes. Astrophys. J., 823(1), 65–12pp.
Abstract: We present the results of searches for point-like sources of neutrinos based on the first combined analysis of data from both the ANTARES and IceCube neutrino telescopes. The combination of both detectors, which differ in size and location, forms a window in the southern sky where the sensitivity to point sources improves by up to a factor of 2 compared with individual analyses. Using data recorded by ANTARES from 2007 to 2012, and by IceCube from 2008 to 2011, we search for sources of neutrino emission both across the southern sky and from a preselected list of candidate objects. No significant excess over background has been found in these searches, and flux upper limits for the candidate sources are presented for E-2.5 and E-2 power-law spectra with different energy cut-offs.
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Trotta, R., Johannesson, G., Moskalenko, I. V., Porter, T. A., Ruiz de Austri, R., & Strong, A. W. (2011). Constraints on Cosmic-Ray Propagation Models from a Global Bayesian Analysis. Astrophys. J., 729(2), 106–16pp.
Abstract: Research in many areas of modern physics such as, e. g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, gamma-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP.
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