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 change of the shell structure in atomic nuclei, so-called “nuclear shell evolution”, occurs due to changes of major configurations through particle-hole excitations inside one nucleus, as well as due to variation of the number of constituent protons or neutrons. We have investigated how the shell evolution affects Gamow-Teller (GT) transitions that dominate the beta decay in the region below Sn-132 using the newly obtained experimental data on a long-lived isomer in Ag-127. The T-1/2 = 67.5(9) ms isomer has been identified with a spin and parity of (27/2(+)) at an excitation energy of 1942(-20)(+14) keV, and found to decay via an internal transition of an E3 character, which competes with the dominant beta-decay branches towards the high-spin states in Cd-127. The underlying mechanism of a strong GT transition from the Ag-127 isomer is discussed in terms of configuration-dependent optimization of the effective single-particle energies in the framework of a shell-model approach.

Abstract: Up-to-date cosmological data analyses have shown that (sigma) a closed universe is preferred by the Planck data at more than 99% CL, and (b) interacting scenarios offer a very compelling solution to the Hubble constant tension. In light of these two recent appealing scenarios, we consider here an interacting dark matter-dark energy model with a non-zero spatial curvature component and a freely varying dark energy equation of state in both the quintessential and phantom regimes. When considering Cosmic Microwave Background data only, a phantom and closed universe can perfectly alleviate the Hubble tension, without the necessity of a coupling among the dark sectors. Accounting for other possible cosmological observations compromises the viability of this very attractive scenario as a global solution to current cosmological tensions, either by spoiling its effectiveness concerning the H-0 problem, as in the case of Supernovae Ia data, or by introducing a strong disagreement in the preferred value of the spatial curvature, as in the case of Baryon Acoustic Oscillations.