1995/05/06 by H. Abramowicz, A. Caldwell, Ralph Sinkus +1 · 6 citations
Chemistry · Materials Science · Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Botany #Calorimeter (particle physics) #Computer science #Deep inelastic scattering #Electron #Identification (biology) #Inelastic scattering #Machine Learning in Materials Science #Mass Spectrometry Techniques and Applications #Nuclear Physics and Applications #Nuclear physics #Optics #Physics #Probabilistic neural network #Time delay neural network #ZEUS (particle detector) #hep-ex
paper · pdf · doi:10.1016/0168-9002(95)00612-5
published as Nucl.Instrum.Meth.A365:508-517,1995 · 20 pages, latex, 16 figures appended as uuencoded file
arxiv created 1995/05/06 · openalex publication_date 1995/11/01 · arxiv updated 2010/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach.