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Deep Learning techniques for signal processing and event reconstruction in DUNE

Authors: Laura Accorto;

Deep Learning techniques for signal processing and event reconstruction in DUNE

Abstract

DUNE - Deep Underground Neutrino Experiment - will be an experiment based in the USA whose main goal will be to study long-baseline neutrino oscillations from an accelerator beam. At CERN, a smaller prototype of the DUNE far detectors has been created, ProtoDUNE, with the objective to test and validate the technology required for DUNE far detectors. The signal captured by ProtoDUNE needs to be processed to clean it from noise. To do so, a Deep Learning tool has been developed at CERN. However, the costs of this tool imply the necessity to evaluate its performance comparing to more classical techniques, which involve lower costs. For this reason, a statistical algorithm has been considered, which is the one currently used: the results of the two solutions are compared in this project.

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Keywords

summer-student programme, CERN openlab

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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