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L. Uboldi

Publications and source records attributed to L. Uboldi.

2 recordsLinked to original sources
arXiv2021

Coded masks for imaging of neutrino events

The capture of scintillation light emitted by liquid Argon and Xenon under molecular excitations by charged particles is still a challenging task. Here we present a first attempt to design a device able to grab sufficiently high luminosity in order to reconstruct the path of ionizing particles. This preliminary study is based on the use of masks to encode the light signal combined with single-photon detectors. In this respect, the proposed system is able to detect tracks over focal distances of about tens of centimeters. From numerical simulations it emerges that it is possible to successfully decode and recognize signals, even complex, with a relatively limited number of acquisition channels. Such innovative technique can be very fruitful in a new generation of detectors devoted to neutrino physics and dark matter search. Indeed the introduction of coded masks combined with SiPM detectors is proposed for a liquid-Argon target in the Near Detector of the DUNE experiment.

physics.ins-det↗
arXiv2021

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae and to look for exotic physics at very low energies. In order to achieve those physics goals, it is crucial to identify and reconstruct signals in the waveforms recorded on each TPC wire. In this paper, we report on a novel algorithm based on a one-dimensional convolutional neural network (CNN) to look for the region-of-interest (ROI) in raw waveforms. We test this algorithm using data from the ArgoNeuT experiment in conjunction with an improved noise mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of $\sim$0.03-0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.

physics.ins-det↗