Construction of consistent neural network empirical physical formulas for detector counts in neutron exit channel selection

• Published in: Measurement : journal of the International Measurement Confederation Vol. 46; no. 9; pp. 3192 - 3197
• Main Authors: Akkoyun, Serkan, Yildiz, Nihat
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2013
• Subjects: Channels; Construction; Counting; Detectors; Empirical analysis; Empirical physical formula; Functions (mathematics); Mathematical analysis; Neural network; Neural networks; Neutron exit channel; Neutron exit channel; Empirical physical formula; Neural network
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2013.05.024

•Neutron exit channel selection is important in nuclear physics.•Neural network empirical physical formulas (NN-EPFs) were formed for detector counts.•These NN-EPFs are useful physical functions in channel selection. Proper selection of neutron exit channels following heavy-ion reactions is important in nuclear structure physics. A knowledge of detector counts versus number of neutron interaction points per event can be useful in this selection. In this paper, we constructed layered feedforward neural networks (LFNNs) consistent empirical physical formulas (EPFs) to estimate the detector counts versus number of neutron interaction points per event. The LFNN-EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LFNN-EPFs can be used to derivate further physical functions which might be potentially relevant to neutron exit channel selection.