Abstract: Coherent production of J/psi mesons is studied in ultraperipheral lead-lead collisions at a nucleon-nucleon centre-of-mass energy of 5 TeV, using a data sample collected by the LHCb experiment corresponding to an integrated luminosity of about 10 μb(-1). The J/psi mesons are reconstructed in the dimuon final state and are required to have transverse momentum below 1 GeV. The cross-section within the rapidity range of 2.0 < y < 4.5 is measured to be 4.45 +/- 0.24 +/- 0.18 +/- 0.58 mb, where the first uncertainty is statistical, the second systematic and the third originates from the luminosity determination. The cross-section is also measured in J/psi rapidity intervals. The results are compared to predictions from phenomenological models.

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: Cosmic rays have been studied, since they were discovered one century ago, with a very broad spectrum of detectors and techniques. However, never the properties of the extended air showers (EAS) induced by high energy primary cosmic rays had been analysed at the Earth surface with a high granularity detector and a time resolution at the 0.1 ns scale. The commissioning of the timing RPC (Resistive Plate Chambers) time of flight wall of the HADES spectrometer with cosmic rays, at the GSI (Darmstadt, Germany), opened up that opportunity. During the last months of 2009, more than 500 millions of cosmic ray events were recorded by a stack of two RPC modules, of about 1.25 m(2) each, able to measure swarms of up to similar to 100 particles with a time resolution better than 100 ps. In this document it is demonstrated how such a relative small two-plane, high-granularity timing RPC setup may provide significant information about the properties of the shower and hence about the primary cosmic ray properties.