Georgi Vasilev, Galina Vankova-Kirilova, Galina Bozhkova
{"title":"Optimization of singly-charged particles identification with the AMS02 RICH detector by a machine learning method","authors":"Georgi Vasilev, Galina Vankova-Kirilova, Galina Bozhkova","doi":"10.1016/j.astropartphys.2025.103134","DOIUrl":null,"url":null,"abstract":"<div><div>AMS-02 is a detector currently in operation onboard the International Space Station (ISS). One of the main scientific goals of the spectrometer is the measurement of charged particle fluxes. The detector design makes possible the identification of particles and antiparticles by precise measurement of particle momentum in the AMS-02 Silicon Tracker, and velocity in the Cherenkov (RICH) detector. The RICH is able to measure the isotopic composition of the light elements (up to charge <em>Z</em> = 5) in the kinetic energy range from a few GeV/n to about 10 GeV/n. However, the velocity reconstruction for charge 1 particles is particularly challenging due to the low number of photons they produce in the RICH detector which can lead to wrong event reconstruction. In this paper, we show the high potential of the Multilayer Perceptron deep learning model (MLP-BFGS) for identification of signal and the background due to interactions inside the AMS-02 detector, and to significantly improve particle identification by its mass.</div></div>","PeriodicalId":55439,"journal":{"name":"Astroparticle Physics","volume":"171 ","pages":"Article 103134"},"PeriodicalIF":4.2000,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Astroparticle Physics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S092765052500057X","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
AMS-02 is a detector currently in operation onboard the International Space Station (ISS). One of the main scientific goals of the spectrometer is the measurement of charged particle fluxes. The detector design makes possible the identification of particles and antiparticles by precise measurement of particle momentum in the AMS-02 Silicon Tracker, and velocity in the Cherenkov (RICH) detector. The RICH is able to measure the isotopic composition of the light elements (up to charge Z = 5) in the kinetic energy range from a few GeV/n to about 10 GeV/n. However, the velocity reconstruction for charge 1 particles is particularly challenging due to the low number of photons they produce in the RICH detector which can lead to wrong event reconstruction. In this paper, we show the high potential of the Multilayer Perceptron deep learning model (MLP-BFGS) for identification of signal and the background due to interactions inside the AMS-02 detector, and to significantly improve particle identification by its mass.
期刊介绍:
Astroparticle Physics publishes experimental and theoretical research papers in the interacting fields of Cosmic Ray Physics, Astronomy and Astrophysics, Cosmology and Particle Physics focusing on new developments in the following areas: High-energy cosmic-ray physics and astrophysics; Particle cosmology; Particle astrophysics; Related astrophysics: supernova, AGN, cosmic abundances, dark matter etc.; Gravitational waves; High-energy, VHE and UHE gamma-ray astronomy; High- and low-energy neutrino astronomy; Instrumentation and detector developments related to the above-mentioned fields.