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Ortiz Arciniega, J. L., Carrio, F., & Valero, A. (2019). FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions. J. Instrum., 14, P09002–13pp.
Abstract: The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.
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Oset, E., Ramos, A., Garzon, E. J., Molina, R., Tolos, L., Xiao, C. W., et al. (2012). Interaction of vector mesons with baryons and nuclei. Int. J. Mod. Phys. E, 21(11), 1230011–18pp.
Abstract: After some short introductory remarks on particular issues on the vector mesons in nuclei, in this paper, we present a short review of recent developments concerning the interaction of vector mesons with baryons and with nuclei from a modern perspective using the local hidden gauge formalism for the interaction of vector mesons. We present results for the vector-baryon interaction and in particular for the resonances which appear as composite states, dynamically generated from the interaction of vector mesons with baryons, taking also the mixing of these states with pseudoscalars and baryons into account. We then venture into the charm sector, reporting on hidden charm baryon states around 4400 MeV, generated from the interaction of vector mesons and baryons with charm, which have a strong repercussion on the properties of the J/Psi N interaction. We also address the interaction of K* with nuclei and make suggestions to measure the predicted huge width in the medium by means of transparency ratio. The formalism is extended to study the phenomenon of J/psi suppression in nuclei via J/psi photo-production reactions.
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PANDA Collaboration(Singh, B. et al), & Diaz, J. (2016). Study of doubly strange systems using stored antiprotons. Nucl. Phys. A, 954, 323–340.
Abstract: Bound nuclear systems with two units of strangeness are still poorly known despite their importance for many strong interaction phenomena. Stored antiprotons beams in the GeV range represent an unparalleled factory for various hyperon-antihyperon pairs. Their outstanding large production probability in antiproton collisions will open the floodgates for a series of new studies of systems which contain two or even more units of strangeness at the PANDA experiment at FAIR. For the first time, high resolution gamma-spectroscopy of doubly strange Lambda Lambda-hypernuclei will be performed, thus complementing measurements of ground state decays of Lambda Lambda-hypernuclei at J-PARC or possible decays of particle unstable hypernuclei in heavy ion reactions. High resolution spectroscopy of multistrange Xi(-) -atoms will be feasible and even the production of Omega(-) -atoms will be within reach. The latter might open the door to the vertical bar S vertical bar = 3 world in strangeness nuclear physics, by the study of the hadronic Omega(-) -nucleus interaction. For the first time it will be possible to study the behavior of Xi(+) in nuclear systems under well controlled conditions.
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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
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.
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Sahin, E. et al, Gadea, A., & Algora, A. (2012). Structure of the N=50 As, Ge, Ga nuclei. Nucl. Phys. A, 893, 1–12.
Abstract: The level structures of the N = 50 As-83, Ge-82, and Ga-81 isotones have been investigated by means of multi-nucleon transfer reactions. A first experiment was performed with the CLARA PRISMA setup to identify these nuclei. A second experiment was carried out with the GASP array in order to deduce the gamma-ray coincidence information. The results obtained on the high-spin states of such nuclei are used to test the stability of the N = 50 shell closure in the region of Ni-78 (Z = 28). The comparison of the experimental level schemes with the shell-model calculations yields an N = 50 energy gap value of 4.7(3) MeV at Z = 28. This value, in a good agreement with the prediction of the finite-range liquid-drop model as well as with the recent large-scale shell model calculations, does not support a weakening of the N = 50 shell gap down to Z = 28.
Keywords: NUCLEAR REACTIONS U-238(Se-82, Ga-81), (Se-82, Ge-82), (Se-82, As-83), E=515 MeV; measured E-gamma, I-gamma (theta), gamma gamma-coin, reaction fragments, (fragment)gamma-coin using PRISMA magnetic spectrometer, gamma after deexcitation using Ge Compton-suppressed detectors of CLARA array, thin and thick target; deduced sigma(theta), levels, J, pi; calculated levels, J, pi using shell model
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