Abstract: Detection of the IceCube-170922A neutrino coincident with the flaring blazar TXS 0506+056, the first and only similar to 3 sigma high-energy neutrino source association to date, offers a potential breakthrough in our understanding of high-energy cosmic particles and blazar physics. We present a comprehensive analysis of TXS. 0506+056 during its flaring state, using newly collected Swift, NuSTAR, and X-shooter data with Fermi observations and numerical models to constrain the blazar's particle acceleration processes and multimessenger (electromagnetic (EM) and high-energy neutrino) emissions. Accounting properly for EM cascades in the emission region, we find a physically consistent picture only within a hybrid leptonic scenario, with gamma-rays produced by external inverse-Compton processes and high-energy neutrinos via a radiatively subdominant hadronic component. We derive robust constraints on the blazar's neutrino and cosmic-ray emissions and demonstrate that, because of cascade effects, the 0.1-100 keV emissions of TXS. 0506+056 serve as a better probe of its hadronic acceleration and highenergy neutrino production processes than its GeV-TeV emissions. If the IceCube neutrino association holds, physical conditions in the TXS. 0506+056 jet must be close to optimal for high-energy neutrino production, and are not favorable for ultrahigh-energy cosmic-ray acceleration. Alternatively, the challenges we identify in generating a significant rate of IceCube neutrino detections from TXS. 0506+056 may disfavor single-zone models, in which.-rays and high-energy neutrinos are produced in a single emission region. In concert with continued operations of the high-energy neutrino observatories, we advocate regular X-ray monitoring of TXS. 0506+056 and other blazars in order to test single-zone blazar emission models, clarify the nature and extent of their hadronic acceleration processes, and carry out the most sensitive possible search for additional multimessenger sources.

Abstract: The extragalactic gamma-ray and neutrino emission may have a contribution from dark matter (DM) annihilations. In the case of discrepancies between observations and standard predictions, one could infer the DM pair annihilation cross section into cosmic rays by studying the shape of the energy spectrum. So far all analyses of the extragalactic DM signal have assumed the standard cosmological model (ACDM) as the underlying theory. However, there are alternative DM scenarios where the number of low-mass objects is significantly suppressed. Therefore the characteristics of the gamma-ray and neutrino emission in these models may differ from ACDM as a result. Here we show that the extragalactic isotropic signal in these alternative models has a similar energy dependence to that in ACDM, but the overall normalisation is reduced. The similarities between the energy spectra combined with the flux suppression could lead one to misinterpret possible evidence for models beyond ACDM as being due to CDM particles annihilating with a much weaker cross section than expected.

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.