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Balibrea-Correa, J., Lerendegui-Marco, J., Babiano-Suarez, V., Caballero, L., Calvo, D., Ladarescu, I., et al. (2021). Machine Learning aided 3D-position reconstruction in large LaCl3 crystals. Nucl. Instrum. Methods Phys. Res. A, 1001, 165249–17pp.
Abstract: We investigate five different models to reconstruct the 3D gamma-ray hit coordinates in five large LaCl3(Ce) monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 x 50 mm(2) and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2 mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5 mm fwhm are obtained. Depth of interaction resolutions between 1 mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70%-80% of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements.
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Barrientos, D., Bellato, M., Bazzacco, D., Bortolato, D., Cocconi, P., Gadea, A., et al. (2015). Performance of the Fully Digital FPGA-Based Front-End Electronics for the GALILEO Array. IEEE Trans. Nucl. Sci., 62(6), 3134–3139.
Abstract: In this work we present the architecture and results of a fully digital Front End Electronics (FEE) read out system developed for the GALILEO array. The FEE system, developed in collaboration with the Advanced Gamma Tracking Array (AGATA) collaboration, is composed of three main blocks: preamplifiers, digitizers and preprocessing electronics. The slow control system contains a custom Linux driver, a dynamic library and a server implementing network services. This work presents the first results of the digital FEE system coupled with a GALILEO germanium detector, which has demonstrated the capability to achieve an energy resolution of 1.53% at an energy of 1.33 MeV, similar to the one obtained with a conventional analog system. While keeping a good performance in terms of energy resolution, digital electronics will allow to instrument the full GALILEO array with a versatile system with high integration and low power consumption and costs.
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Domingo-Pardo, C. (2012). A new technique for 3D gamma-ray imaging: Conceptual study of a 3D camera. Nucl. Instrum. Methods Phys. Res. A, 675, 123–132.
Abstract: A novel technique for 3D gamma-ray imaging is presented. This method combines the positron annihilation Compton scattering imaging technique with a supplementary position sensitive detector, which registers gamma-rays scattered in the object at angles of about 90 degrees. The 3D coordinates of the scattering location can be determined rather accurately by applying the Compton principle. This method requires access to the object from two orthogonal sides and allows one to achieve a position resolution of few mm in all three space coordinates. A feasibility study for a 3D camera is presented based on Monte Carlo calculations.
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Gammaldi, V., Zaldivar, B., Sanchez-Conde, M. A., & Coronado-Blazquez, J. (2023). A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning. Mon. Not. Roy. Astron. Soc., 520(1), 1348–1361.
Abstract: Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93 . 3 per cent +/- 0 . 7 per cent performance. Other ML evaluation parameters, such as the True Ne gativ e and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the de generac y between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.
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Goasduff, A. et al, & Gadea, A. (2021). The GALILEO gamma-ray array at the Legnaro National Laboratories. Nucl. Instrum. Methods Phys. Res. A, 1015, 165753–15pp.
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.
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