Abstract: The concordance of the ACDM cosmological model in light of current observations has been the subject of an intense debate in recent months. The 2018 Planck Cosmic Microwave Background (CMB) temperature anisotropy power spectrum measurements appear at face value to favour a spatially closed Universe with curvature parameter Omega(K) < 0. This preference disappears if Baryon Acoustic Oscillation (BAO) measurements are combined with Planck data to break the geometrical degeneracy, although the reliability of this combination has been questioned due to the strong tension present between the two datasets when assuming a curved Universe. Here, we approach this issue from yet another point of view, using measurements of the full-shape (FS) galaxy power spectrum, P(k), from the Baryon Oscillation Spectroscopic Survey DR12 CMASS sample. By combining Planck data with FS measurements, we break the geometrical degeneracy and find Omega(K) = 0.0023 +/- 0.0028. This constrains the Universe to be spatially flat to sub-percent precision, in excellent agreement with results obtained using BAO measurements. However, as with BAO, the overall increase in the best-fit chi(2) suggests a similar level of tension between Planck and P(k) under the assumption of a curved Universe. While the debate on spatial curvature and the concordance between cosmological datasets remains open, our results provide new perspectives on the issue, highlighting the crucial role of FS measurements in the era of precision cosmology.

Abstract: The Scotogenic model is an economical scenario that generates neutrino masses at the 1-loop level and includes a dark matter candidate. This is achieved by means of an ad hoc Z(2) symmetry, which forbids the tree-level generation of neutrino masses and stabilizes the lightest Z(2)-odd state. Neutrino masses are also suppressed by a quartic coupling, usually denoted by lambda(5). While the smallness of this parameter is natural, it is not explained in the context of the Scotogenic model. We construct an ultraviolet completion of the Scotogenic model that provides a natural explanation for the smallness of the lambda(5) parameter and induces the Z(2) parity as the low-energy remnant of a global U(1) symmetry at high energies. The low-energy spectrum contains, besides the usual Scotogenic states, a massive scalar and a massless Goldstone boson, hence leading to novel phenomenological predictions in flavor observables, dark matter physics and colliders.

Abstract: GALILEO, a new 4 pi high-resolution gamma-detection array, based on HPGe detectors, has been developed and installed at the Legnaro National Laboratories. The GALILEO array greatly benefits from a fully-digital readout chain, customized DAQ, and a variety of complementary detectors to improve the resolving power by the detection of particles, ions or high-energy gamma-ray transitions. In this work, a full description of the array, including electronics and DAQ, is presented together with its complementary instrumentation.

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: 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.