RT Journal Article
T1 Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors
A1 Flores Garrido, Juan Luis
A1 Martel Bravo, Ismael
A1 Jiménez Naharro, Raúl
A1 Salmerón Revuelta, Patricio
A1 Gómez Galán, Juan Antonio
AB The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported
PB Elsevier
SN 0168-9002
SN 1872-9576 (electrónico)
YR 2016
FD 2016-05
LK https://hdl.handle.net/10272/25656
UL https://hdl.handle.net/10272/25656
LA eng
NO Flores, J. L., Martel, I., Jiménez, R., Galán, J., & Salmerón, P. (2016). Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 830, 287–293. https://doi.org/10.1016/j.nima.2016.05.107
NO The authors are grateful to the FAZIA collaboration [27] for providing part of the experimental data used in the present study. This work has been supported by the Spanish Ministerio de Economía y Competitividad under the grant FPA2014-59954-C3-1-P, by the Andalusian Conserjería de Innovación, Ciencia y Empresa, under the grants FQM 4964 and P10-TIC-6311, and by the European Union Seventh Framework Programme FP/2007-2013 under Grant Agreement n. 262010-EN-SAR
DS Repositorio Institucional de la Universidad de Huelva
RD 30 may 2026