A comparison between a neural network and the likelihood method to evaluate the performance of a transition radiation detector
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Cited by (10)
Sweep-Net: An Artificial Neural Network for radiation transport solves
2021, Journal of Computational PhysicsCitation Excerpt :In the radiation transport community, ANNs have been seldomly used in comparison to other engineering disciplines. Prior work using Neural Networks can be found for solar irradiation spectrum reconstruction [17], to improve the radiative scalar flux approximation in space radiation sensors [18], and to reproduce one-group transport solutions in slab geometry [19] [20]. However, ANNs have not yet been applied to accelerate radiation transport problems.
Electron/pion identification with ALICE TRD prototypes using a neural network algorithm
2005, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated EquipmentCitation Excerpt :Neural networks are used for a variety of tasks in modern particle detectors [7]. A first NN analysis for electron/pion identification with a TRD [8] showed that the performance can be significantly improved. We report results for pion rejection using a NN, which increases the pion rejection factor up to about 500 for a momentum of 2 GeV/c.
Neural networks in high energy physics: A ten year perspective
1999, Computer Physics CommunicationsIdentification of cosmic ray electrons and positrons by neural networks
1996, Astroparticle PhysicsA large area transition radiation detector to measure the energy of muons in the Gran Sasso underground laboratory
1995, Nuclear Inst. and Methods in Physics Research, A