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.

Abstract: The n_TOF collaboration operates a Total Absorption Calorimeter (TAC) [1] for measuring neutron capture cross-sections of low-mass and/or radioactive samples. The results obtained with the TAC have led to a substantial improvement of the capture cross sections of (237)Np and (240)Pu [2]. The experience acquired during the first measurements has allowed us to optimize the performance of the TAC and to improve the capture signal to background ratio, thus opening the way to more complex and demanding measurements on rare radioactive materials. The new design has been reached by a series of detailed Monte Carlo simulations of complete experiments and dedicated test measurements. The new capture setup will be presented and the main achievements highlighted.

Abstract: Minimal Dark Matter (MDM) stands as one of the simplest dark matter scenarios. In MDM models, annihilation and co-annihilation processes among the members of the MDM multiplet are usually very efficient, pushing the dark matter mass above O(10) TeV in order to reproduce the observed dark matter relic density. Motivated by this little drawback, in this paper we consider an extension of the MDM scenario by three right-handed neutrinos. Two specific choices for the MDM multiplet are studied: a fermionic SU(2)(L) quintuplet and a scalar SU(2)(L) septuplet. The lightest right-handed neutrino, with tiny Yukawa couplings, never reaches thermal equilibrium in the early universe and is produced by freeze-in. This creates a link between dark matter and neutrino physics: dark matter can be non-thermally produced by the decay of the lightest right-handed neutrino after freeze-out, allowing to lower significantly the dark matter mass. We discuss the phenomenology of the non-thermally produced MDM and, taking into account significant Sommerfeld corrections, we find that the dark matter mass must have some specific values in order not to be in conflict with the current bounds from gamma-ray observations.