Pub Date : 2022-02-06DOI: 10.1142/9789811234033_0009
Philip W. Harris, Nhan Tran
{"title":"Machine Learning for Triggering and Data Acquisition","authors":"Philip W. Harris, Nhan Tran","doi":"10.1142/9789811234033_0009","DOIUrl":"https://doi.org/10.1142/9789811234033_0009","url":null,"abstract":"","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117173785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234026_0014
R. Sharankova, T. Wongjirad
{"title":"Particle Identification in Neutrino Detectors","authors":"R. Sharankova, T. Wongjirad","doi":"10.1142/9789811234026_0014","DOIUrl":"https://doi.org/10.1142/9789811234026_0014","url":null,"abstract":"","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134060504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234026_0003
P. Baldi, Peter Sadowski, D. Whiteson
An early example of the ability of deep networks to improve the statistical power of data collected in particle physics experiments was the demonstration that such networks operating on lists of particle momenta (four-vectors) could outperform shallow networks using features engineered with domain knowledge. A benchmark case is described, with extensions to parameterized networks. A discussion of data handling and architecture is presented, as well as a description of how to incorporate physics knowledge into the network architecture.
{"title":"Deep Learning from Four Vectors","authors":"P. Baldi, Peter Sadowski, D. Whiteson","doi":"10.1142/9789811234026_0003","DOIUrl":"https://doi.org/10.1142/9789811234026_0003","url":null,"abstract":"An early example of the ability of deep networks to improve the statistical power of data collected in particle physics experiments was the demonstration that such networks operating on lists of particle momenta (four-vectors) could outperform shallow networks using features engineered with domain knowledge. A benchmark case is described, with extensions to parameterized networks. A discussion of data handling and architecture is presented, as well as a description of how to incorporate physics knowledge into the network architecture.","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131227867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234026_fmatter
P. Calafiura, D. Rousseau, K. Terao
{"title":"FRONT MATTER","authors":"P. Calafiura, D. Rousseau, K. Terao","doi":"10.1142/9789811234026_fmatter","DOIUrl":"https://doi.org/10.1142/9789811234026_fmatter","url":null,"abstract":"","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115821691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234026_0011
K. Terao
{"title":"Clustering","authors":"K. Terao","doi":"10.1142/9789811234026_0011","DOIUrl":"https://doi.org/10.1142/9789811234026_0011","url":null,"abstract":"","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"228 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114098924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234026_0015
R. Teixeira de Lima
{"title":"Sequence-Based Learning","authors":"R. Teixeira de Lima","doi":"10.1142/9789811234026_0015","DOIUrl":"https://doi.org/10.1142/9789811234026_0015","url":null,"abstract":"","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125214841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234026_0017
T. Dorigo, P. De Castro Manzano
{"title":"Dealing with Nuisance Parameters","authors":"T. Dorigo, P. De Castro Manzano","doi":"10.1142/9789811234026_0017","DOIUrl":"https://doi.org/10.1142/9789811234026_0017","url":null,"abstract":"","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124651497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234033_0011
K. Terao
{"title":"Clustering","authors":"K. Terao","doi":"10.1142/9789811234033_0011","DOIUrl":"https://doi.org/10.1142/9789811234033_0011","url":null,"abstract":"","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130393567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234033_0010
Adam Aurisano, L. Whitehead
End-to-end analyses of data from high-energy physics experiments using machine and deep learning techniques have emerged in recent years. These analyses use deep learning algorithms to go directly from low-level detector information directly to high-level quantities that classify the interactions. The most popular class of algorithms for these analyses are convolutional neural networks that operate on experimental data formatted as images. End-to-end analyses skip stages of the traditional workflow that includes the reconstruction of particles produced in the interactions, and as such are not limited by efficiency losses and sources of inaccuracy throughout the event reconstruction process. In many cases, deep learning end-to-end analyses have been shown to have significantly increased performance compared to previous state-of-the-art methods.
{"title":"End-to-End Analyses Using Image Classification","authors":"Adam Aurisano, L. Whitehead","doi":"10.1142/9789811234033_0010","DOIUrl":"https://doi.org/10.1142/9789811234033_0010","url":null,"abstract":"End-to-end analyses of data from high-energy physics experiments using machine and deep learning techniques have emerged in recent years. These analyses use deep learning algorithms to go directly from low-level detector information directly to high-level quantities that classify the interactions. The most popular class of algorithms for these analyses are convolutional neural networks that operate on experimental data formatted as images. End-to-end analyses skip stages of the traditional workflow that includes the reconstruction of particles produced in the interactions, and as such are not limited by efficiency losses and sources of inaccuracy throughout the event reconstruction process. In many cases, deep learning end-to-end analyses have been shown to have significantly increased performance compared to previous state-of-the-art methods.","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122292146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-06DOI: 10.1142/9789811234033_0006
Michela Paganini, Luke de Oliveira, B. Nachman, D. Derkach, F. Ratnikov, Andrey Ustyuzhanin, A. Ghosh
{"title":"Generative Models for Fast Simulation","authors":"Michela Paganini, Luke de Oliveira, B. Nachman, D. Derkach, F. Ratnikov, Andrey Ustyuzhanin, A. Ghosh","doi":"10.1142/9789811234033_0006","DOIUrl":"https://doi.org/10.1142/9789811234033_0006","url":null,"abstract":"","PeriodicalId":416365,"journal":{"name":"Artificial Intelligence for High Energy Physics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117220474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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