Unfolding using deep learning and its application on pulse height analysis and pile-up management
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/59219DOI: 10.1016/j.nima.2021.165403
ISSN: 0168-9002
Publisher
Elsevier
Date
2021-07-21Bibliographic citation
Regadío Carretero, A., Esteban, L. & Sánchez Prieto, S. 2021, “Unfolding using deep learning and its application on pulse height analysis and pile-up management”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 1005, art. no. 165403.
Keywords
Digital pulse processing
Instrumentation
Unfolding
Pile-up
Deep learning
Neural networks
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
https://doi.org/10.1016/j.nima.2021.165403Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2021 Elsevier
Access rights
info:eu-repo/semantics/openAccess
Abstract
Traditionally, electronics for pulse processing can be modeled as linear transfer functions. In contrast, due to the fact that artificial Neural Networks (NNs) are generally non-linear systems, their behavior against noise is significantly different as in linear systems. We take advantage of this non-linearity to achieve acceptable Signal-to-Noise Ratios (SNR) with a extremely short shaping time. This article shows an approach to a concrete NN named U-net as pulse shaper. It filters the pulses and return them unfolded solving the pile-up problem, and even estimates the height of the pulses when there has been saturation in the detector. In this article, the NN architecture and results using simulated pulses and real pulses from scintillators are shown. The results clearly show the effectiveness of the approach.
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Unfolding_Regadio_NIMPRA_2021.pdf | 589.8Kb |
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Unfolding_Regadio_NIMPRA_2021.pdf | 589.8Kb |
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