{"title":"用于液氩探测器位置重建的神经网络","authors":"Miguel Cárdenas-Montes, Roberto Santorelli","doi":"10.1088/1748-0221/19/05/c05047","DOIUrl":null,"url":null,"abstract":"\n This article explores the integration of Deep Learning and Explainable Artificial Intelligence in Particle Physics, focusing on their application in position reconstruction within dual-phase liquid argon detectors for Dark Matter search. Facing challenges like pile-up scenarios, Neural Networks prove crucial for refining algorithms. This article emphasizes Deep Learning's role in addressing regression and classification problems, such as position reconstruction and particle identification, particularly in Time Projection Chambers. Explainable Artificial Intelligence emerges as pivotal in unraveling Deep Learning's complex decisions, exposing biases, and facilitating improvement cycles. Innovations like Evolutionary Neural Networks and topology-driven dataset reduction offer potential efficiency gains. The conclusion highlights challenges in analyzing massive data volumes, emphasizing the need for adaptability and ethical considerations in the pursuit of understanding Dark Matter.","PeriodicalId":16184,"journal":{"name":"Journal of Instrumentation","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural Networks for position reconstruction in liquid argon detectors\",\"authors\":\"Miguel Cárdenas-Montes, Roberto Santorelli\",\"doi\":\"10.1088/1748-0221/19/05/c05047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n This article explores the integration of Deep Learning and Explainable Artificial Intelligence in Particle Physics, focusing on their application in position reconstruction within dual-phase liquid argon detectors for Dark Matter search. Facing challenges like pile-up scenarios, Neural Networks prove crucial for refining algorithms. This article emphasizes Deep Learning's role in addressing regression and classification problems, such as position reconstruction and particle identification, particularly in Time Projection Chambers. Explainable Artificial Intelligence emerges as pivotal in unraveling Deep Learning's complex decisions, exposing biases, and facilitating improvement cycles. Innovations like Evolutionary Neural Networks and topology-driven dataset reduction offer potential efficiency gains. The conclusion highlights challenges in analyzing massive data volumes, emphasizing the need for adaptability and ethical considerations in the pursuit of understanding Dark Matter.\",\"PeriodicalId\":16184,\"journal\":{\"name\":\"Journal of Instrumentation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Instrumentation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1748-0221/19/05/c05047\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Instrumentation","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1748-0221/19/05/c05047","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Neural Networks for position reconstruction in liquid argon detectors
This article explores the integration of Deep Learning and Explainable Artificial Intelligence in Particle Physics, focusing on their application in position reconstruction within dual-phase liquid argon detectors for Dark Matter search. Facing challenges like pile-up scenarios, Neural Networks prove crucial for refining algorithms. This article emphasizes Deep Learning's role in addressing regression and classification problems, such as position reconstruction and particle identification, particularly in Time Projection Chambers. Explainable Artificial Intelligence emerges as pivotal in unraveling Deep Learning's complex decisions, exposing biases, and facilitating improvement cycles. Innovations like Evolutionary Neural Networks and topology-driven dataset reduction offer potential efficiency gains. The conclusion highlights challenges in analyzing massive data volumes, emphasizing the need for adaptability and ethical considerations in the pursuit of understanding Dark Matter.
期刊介绍:
Journal of Instrumentation (JINST) covers major areas related to concepts and instrumentation in detector physics, accelerator science and associated experimental methods and techniques, theory, modelling and simulations. The main subject areas include.
-Accelerators: concepts, modelling, simulations and sources-
Instrumentation and hardware for accelerators: particles, synchrotron radiation, neutrons-
Detector physics: concepts, processes, methods, modelling and simulations-
Detectors, apparatus and methods for particle, astroparticle, nuclear, atomic, and molecular physics-
Instrumentation and methods for plasma research-
Methods and apparatus for astronomy and astrophysics-
Detectors, methods and apparatus for biomedical applications, life sciences and material research-
Instrumentation and techniques for medical imaging, diagnostics and therapy-
Instrumentation and techniques for dosimetry, monitoring and radiation damage-
Detectors, instrumentation and methods for non-destructive tests (NDT)-
Detector readout concepts, electronics and data acquisition methods-
Algorithms, software and data reduction methods-
Materials and associated technologies, etc.-
Engineering and technical issues.
JINST also includes a section dedicated to technical reports and instrumentation theses.