C. L. Liao, Z. Quan, Y. W. Dong, M. Xu., C. Zhang, J. J. Wang, X. G. Yang, Q. Wu, J. Y. Sun, X. Liu., Z. G. Wang., R. J. Wang.
{"title":"将机器学习方法应用于基于空间的高粒度热量计的能量重构","authors":"C. L. Liao, Z. Quan, Y. W. Dong, M. Xu., C. Zhang, J. J. Wang, X. G. Yang, Q. Wu, J. Y. Sun, X. Liu., Z. G. Wang., R. J. Wang.","doi":"10.1007/s10686-024-09957-5","DOIUrl":null,"url":null,"abstract":"<div><p>The High Energy Cosmic-Radiation Detection Facility (HERD) is dedicated to achieving several scientific objectives, including the search for dark matter, precise measurement of the cosmic ray spectrum, and gamma-ray sky survey observations. HERD’s innovative design incorporates a three-dimensional imaging calorimeter with five sensitive faces, significantly enhancing geometric acceptance. However, this design introduces a new challenge for reconstructing particles incident from all directions. This article aims to integrate rapidly advancing deep learning techniques into the reconstruction task. Utilizing simulation data, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and other deep learning networks are employed to reconstruct the energy of isotropic electrons. Model performance sees a significant boost through the application of end-layer visible energy correction and a “multi-class multi-prediction” approach, involving different models trained for distinct energy ranges. Moreover, recognizing differences between simulation and physical samples, the model is validated using the beam test data. The model predicts an energy resolution of better than 1% for simulation isotropic electrons ranging from 10 to 1000 GeV. In the case of beam data, the model achieves an energy resolution of 1.3% at 200 GeV, comparable to traditional methods. The results demonstrate the significant potential of deep learning in the reconstruction of three-dimensional calorimeters.</p></div>","PeriodicalId":551,"journal":{"name":"Experimental Astronomy","volume":"58 3","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of machine learning method for energy reconstruction on space based high granularity calorimeter\",\"authors\":\"C. L. Liao, Z. Quan, Y. W. Dong, M. Xu., C. Zhang, J. J. Wang, X. G. Yang, Q. Wu, J. Y. Sun, X. Liu., Z. G. Wang., R. J. Wang.\",\"doi\":\"10.1007/s10686-024-09957-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The High Energy Cosmic-Radiation Detection Facility (HERD) is dedicated to achieving several scientific objectives, including the search for dark matter, precise measurement of the cosmic ray spectrum, and gamma-ray sky survey observations. HERD’s innovative design incorporates a three-dimensional imaging calorimeter with five sensitive faces, significantly enhancing geometric acceptance. However, this design introduces a new challenge for reconstructing particles incident from all directions. This article aims to integrate rapidly advancing deep learning techniques into the reconstruction task. Utilizing simulation data, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and other deep learning networks are employed to reconstruct the energy of isotropic electrons. Model performance sees a significant boost through the application of end-layer visible energy correction and a “multi-class multi-prediction” approach, involving different models trained for distinct energy ranges. Moreover, recognizing differences between simulation and physical samples, the model is validated using the beam test data. The model predicts an energy resolution of better than 1% for simulation isotropic electrons ranging from 10 to 1000 GeV. In the case of beam data, the model achieves an energy resolution of 1.3% at 200 GeV, comparable to traditional methods. The results demonstrate the significant potential of deep learning in the reconstruction of three-dimensional calorimeters.</p></div>\",\"PeriodicalId\":551,\"journal\":{\"name\":\"Experimental Astronomy\",\"volume\":\"58 3\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Experimental Astronomy\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10686-024-09957-5\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Experimental Astronomy","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s10686-024-09957-5","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
Application of machine learning method for energy reconstruction on space based high granularity calorimeter
The High Energy Cosmic-Radiation Detection Facility (HERD) is dedicated to achieving several scientific objectives, including the search for dark matter, precise measurement of the cosmic ray spectrum, and gamma-ray sky survey observations. HERD’s innovative design incorporates a three-dimensional imaging calorimeter with five sensitive faces, significantly enhancing geometric acceptance. However, this design introduces a new challenge for reconstructing particles incident from all directions. This article aims to integrate rapidly advancing deep learning techniques into the reconstruction task. Utilizing simulation data, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and other deep learning networks are employed to reconstruct the energy of isotropic electrons. Model performance sees a significant boost through the application of end-layer visible energy correction and a “multi-class multi-prediction” approach, involving different models trained for distinct energy ranges. Moreover, recognizing differences between simulation and physical samples, the model is validated using the beam test data. The model predicts an energy resolution of better than 1% for simulation isotropic electrons ranging from 10 to 1000 GeV. In the case of beam data, the model achieves an energy resolution of 1.3% at 200 GeV, comparable to traditional methods. The results demonstrate the significant potential of deep learning in the reconstruction of three-dimensional calorimeters.
期刊介绍:
Many new instruments for observing astronomical objects at a variety of wavelengths have been and are continually being developed. Furthermore, a vast amount of effort is being put into the development of new techniques for data analysis in order to cope with great streams of data collected by these instruments.
Experimental Astronomy acts as a medium for the publication of papers of contemporary scientific interest on astrophysical instrumentation and methods necessary for the conduct of astronomy at all wavelength fields.
Experimental Astronomy publishes full-length articles, research letters and reviews on developments in detection techniques, instruments, and data analysis and image processing techniques. Occasional special issues are published, giving an in-depth presentation of the instrumentation and/or analysis connected with specific projects, such as satellite experiments or ground-based telescopes, or of specialized techniques.