A. Yasser, T. A. Nahool, M. Anwar, C. Bowerman, G. A. Yahya
{"title":"预测介子束缚态谱的一种新的机器学习方法","authors":"A. Yasser, T. A. Nahool, M. Anwar, C. Bowerman, G. A. Yahya","doi":"10.1142/s0218301320500925","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate the benefits of machine learning (ML) approaches in predicting the spectra of meson bound states. A linear model (LM) approach is used to predict the spectra of some heavy mesons. Our proposed method has been successfully reproduced in recent experiments, to validate known outcomes. Our results are compared favorably to those obtained using other techniques. This novel perspective opens up a new future in the use of ML in the field of particle physics.","PeriodicalId":14032,"journal":{"name":"International Journal of Modern Physics E-nuclear Physics","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A new machine learning approach for predicting the spectra of meson bound states\",\"authors\":\"A. Yasser, T. A. Nahool, M. Anwar, C. Bowerman, G. A. Yahya\",\"doi\":\"10.1142/s0218301320500925\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we investigate the benefits of machine learning (ML) approaches in predicting the spectra of meson bound states. A linear model (LM) approach is used to predict the spectra of some heavy mesons. Our proposed method has been successfully reproduced in recent experiments, to validate known outcomes. Our results are compared favorably to those obtained using other techniques. This novel perspective opens up a new future in the use of ML in the field of particle physics.\",\"PeriodicalId\":14032,\"journal\":{\"name\":\"International Journal of Modern Physics E-nuclear Physics\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Modern Physics E-nuclear Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/s0218301320500925\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Modern Physics E-nuclear Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s0218301320500925","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new machine learning approach for predicting the spectra of meson bound states
In this paper, we investigate the benefits of machine learning (ML) approaches in predicting the spectra of meson bound states. A linear model (LM) approach is used to predict the spectra of some heavy mesons. Our proposed method has been successfully reproduced in recent experiments, to validate known outcomes. Our results are compared favorably to those obtained using other techniques. This novel perspective opens up a new future in the use of ML in the field of particle physics.