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Amoroso, S., Caron, S., Jueid, A., Ruiz de Austri, R., & Skands, P. (2019). Estimating QCD uncertainties in Monte Carlo event generators for gamma-ray dark matter searches. J. Cosmol. Astropart. Phys., 05(5), 007–44pp.
Abstract: Motivated by the recent galactic center gamma-ray excess identified in the Fermi-LAT data, we perform a detailed study of QCD fragmentation uncertainties in the modeling of the energy spectra of gamma-rays from Dark-Matter (DM) annihilation. When Dark-Matter particles annihilate to coloured final states, either directly or via decays such as W(*) -> qq-', photons are produced from a complex sequence of shower, hadronisation and hadron decays. In phenomenological studies their energy spectra are typically computed using Monte Carlo event generators. These results have however intrinsic uncertainties due to the specific model used and the choice of model parameters, which are difficult to asses and which are typically neglected. We derive a new set of hadronisation parameters (tunes) for the PYTHIA 8.2 Monte Carlo generator from a fit to LEP and SLD data at the Z peak. For the first time we also derive a conservative set of uncertainties on the shower and hadronisation model parameters. Their impact on the gamma-ray energy spectra is evaluated and discussed for a range of DM masses and annihilation channels. The spectra and their uncertainties are also provided in tabulated form for future use. The fragmentation-parameter uncertainties may be useful for collider studies as well.
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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2019). Modelling radiation damage to pixel sensors in the ATLAS detector. J. Instrum., 14, P06012–52pp.
Abstract: Silicon pixel detectors are at the core of the current and planned upgrade of the ATLAS experiment at the LHC. Given their close proximity to the interaction point, these detectors will be exposed to an unprecedented amount of radiation over their lifetime. The current pixel detector will receive damage from non-ionizing radiation in excess of 10(15) 1 MeV n(eq)/cm(2), while the pixel detector designed for the high-luminosity LHC must cope with an order of magnitude larger fluence. This paper presents a digitization model incorporating effects of radiation damage to the pixel sensors. The model is described in detail and predictions for the charge collection efficiency and Lorentz angle are compared with collision data collected between 2015 and 2017 (<= 10(15) 1 MeV n(eq)/cm(2)).
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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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Etxebeste, A., Dauvergne, D., Fontana, M., Letang, J. M., Llosa, G., Muñoz, E., et al. (2020). CCMod: a GATE module for Compton camera imaging simulation. Phys. Med. Biol., 65(5), 055004–17pp.
Abstract: Compton cameras are gamma-ray imaging systems which have been proposed for a wide variety of applications such as medical imaging, nuclear decommissioning or homeland security. In the design and optimization of such a system Monte Carlo simulations play an essential role. In this work, we propose a generic module to perform Monte Carlo simulations and analyses of Compton Camera imaging which is included in the open-source GATE/Geant4 platform. Several digitization stages have been implemented within the module to mimic the performance of the most commonly employed detectors (e.g. monolithic blocks, pixelated scintillator crystals, strip detectors...). Time coincidence sorter and sequence coincidence reconstruction are also available in order to aim at providing modules to facilitate the comparison and reproduction of the data taken with different prototypes. All processing steps may be performed during the simulation (on-the-fly mode) or as a post-process of the output files (offline mode). The predictions of the module have been compared with experimental data in terms of energy spectra, angular resolution, efficiency and back-projection image reconstruction. Consistent results within a 3-sigma interval were obtained for the energy spectra except for low energies where small differences arise. The angular resolution measure for incident photons of 1275 keV was also in good agreement between both data sets with a value close to 13 degrees. Moreover, with the aim of demonstrating the versatility of such a tool the performance of two different Compton camera designs was evaluated and compared.
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Mertsch, P., Parimbelli, G., de Salas, P. F., Gariazzo, S., Lesgourgues, J., & Pastor, S. (2020). Neutrino clustering in the Milky Way and beyond. J. Cosmol. Astropart. Phys., 01(1), 015–23pp.
Abstract: The standard cosmological model predicts the existence of a Cosmic Neutrino Background, which has not yet been observed directly. Some experiments aiming at its detection are currently under development, despite the tiny kinetic energy of the cosmological relic neutrinos, which makes this task incredibly challenging. Since massive neutrinos are attracted by the gravitational potential of our Galaxy, they can cluster locally. Neutrinos should be more abundant at the Earth position than at an average point in the Universe. This fact may enhance the expected event rate in any future experiment. Past calculations of the local neutrino clustering factor only considered a spherical distribution of matter in the Milky Way and neglected the influence of other nearby objects like the Virgo cluster, although recent N-body simulations suggest that the latter may actually be important. In this paper, we adopt a back-tracking technique, well established in the calculation of cosmic rays fluxes, to perform the first three-dimensional calculation of the number density of relic neutrinos at the Solar System, taking into account not only the matter composition of the Milky Way, but also the contribution of the Andromeda galaxy and the Virgo cluster. The effect of Virgo is indeed found to be relevant and to depend non-trivially on the value of the neutrino mass. Our results show that the local neutrino density is enhanced by 0.53% for a neutrino mass of 10 meV, 12% for 50 meV, 50% for 100 meV or 500% for 300 meV.
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