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Faccini, R.

Publications and source records attributed to Faccini, R..

CUORE opens the door to tonne-scale cryogenics experiments

The past few decades have seen major developments in the design and operation of cryogenic particle detectors. This technology offers an extremely good energy resolution – comparable to semiconductor detectors – and a wide choice of target materials, making low temperature calorimetric detectors ideal for a variety of particle physics applications. Rare event searches have continued to require ever greater exposures, which has driven them to ever larger cryogenic detectors, with the CUORE experiment being the first to reach a tonne-scale, mK-cooled, experimental mass. CUORE, designed to search for neutrinoless double beta decay, has been operational since 2017 at a temperature of about 10 mK. This result has been attained by the use of an unprecedentedly large cryogenic infrastructure called the CUORE cryostat: conceived, designed and commissioned for this purpose. In this article the main characteristics and features of the cryogenic facility developed for the CUORE experiment are highlighted. In this work, a brief introduction of the evolution of the field and of the past cryogenic facilities are given. The motivation behind the design and development of the CUORE cryogenic facility is detailed as are the steps taken toward realization, commissioning, and operation of the CUORE cryostat. The major challenges overcome by the collaboration and the solutions implemented throughout the building of the cryogenic facility will be discussed along with the potential improvements for future facilities. The success of CUORE has opened the door to a new generation of large-scale cryogenic facilities in numerous fields of science. Broader implications of the incredible feat achieved by the CUORE collaboration on the future cryogenic facilities in various fields ranging from neutrino and dark matter experiments to quantum computing will be examined.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model

Purpose: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB (“Boltzmann-Langevin One Body”), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also 12 C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. Methods: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of 12 C with 12 C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter (b) training a classifier of b jointly with the VAE. Results: In this work, the distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. Conclusions: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗