Cascade training technique for particle identification

2006/11/27 by Yong Liu, I. Stancu, Ion Stancu
Physics and Astronomy · #Neutrino Physics Research #Particle Detector Development and Performance #Radiation Detection and Scintillator Technologies #hep-ex #physics.data-an

paper · pdf · doi:10.1016/j.nima.2007.05.173

published as Nucl.Instrum.Meth.A578:315-321,2007 · 12 pages and 4 EPS figures

arxiv created 2006/11/27 · openalex publication_date 2007/05/19 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The cascade training technique which was developed during our work on the MiniBooNE particle identification has been found to be a very efficient way to improve the selection performance, especially when very low background contamination levels are desired. The detailed description of this technique is presented here based on the MiniBooNE detector Monte Carlo simulations, using both artifical neural networks and boosted decision trees as examples.

Citations

• Event Selection Using an Extended Fisher Discriminant Method
• Evidence for Production of Single Top Quarks and First Direct Measurement of|Vtb|
• Boosted decision trees as an alternative to artificial neural networks for particle identification
• Evidence for neutrino oscillations from the observation ofν¯eappearance in aν¯μbeam