Classification without labels: learning from mixed samples in high energy physics
Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer International Publishing AG
2018
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| Online Access: | http://hdl.handle.net/1721.1/114621 https://orcid.org/0000-0002-2406-8160 |
| _version_ | 1826217954241413120 |
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| author | Nachman, Benjamin Metodiev, Eric Mario Thaler, Jesse |
| author2 | Massachusetts Institute of Technology. Center for Theoretical Physics |
| author_facet | Massachusetts Institute of Technology. Center for Theoretical Physics Nachman, Benjamin Metodiev, Eric Mario Thaler, Jesse |
| author_sort | Nachman, Benjamin |
| collection | MIT |
| description | Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available. Keyword: jets |
| first_indexed | 2024-09-23T17:11:41Z |
| format | Article |
| id | mit-1721.1/114621 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T17:11:41Z |
| publishDate | 2018 |
| publisher | Springer International Publishing AG |
| record_format | dspace |
| spelling | mit-1721.1/1146212022-10-03T11:05:04Z Classification without labels: learning from mixed samples in high energy physics Nachman, Benjamin Metodiev, Eric Mario Thaler, Jesse Massachusetts Institute of Technology. Center for Theoretical Physics Massachusetts Institute of Technology. Department of Physics Metodiev, Eric Mario Thaler, Jesse Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available. Keyword: jets United States. Department of Energy (Contract DE-SC-00012567) United States. Department of Energy (Contract DE-SC-0001547) 2018-04-09T15:29:08Z 2018-04-09T15:29:08Z 2017-10 2017-09 2017-11-10T05:57:26Z Article http://purl.org/eprint/type/JournalArticle 1126-6708 1029-8479 http://hdl.handle.net/1721.1/114621 Metodiev, Eric M. et al. "Classification without labels: learning from mixed samples in high energy physics." Journal of High Energy Physics 2017 (October 2017): 174 © 2017 The Author(s) https://orcid.org/0000-0002-2406-8160 en http://dx.doi.org/10.1007/JHEP10(2017)174 Journal of High Energy Physics Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer International Publishing AG Springer Berlin Heidelberg |
| spellingShingle | Nachman, Benjamin Metodiev, Eric Mario Thaler, Jesse Classification without labels: learning from mixed samples in high energy physics |
| title | Classification without labels: learning from mixed samples in high energy physics |
| title_full | Classification without labels: learning from mixed samples in high energy physics |
| title_fullStr | Classification without labels: learning from mixed samples in high energy physics |
| title_full_unstemmed | Classification without labels: learning from mixed samples in high energy physics |
| title_short | Classification without labels: learning from mixed samples in high energy physics |
| title_sort | classification without labels learning from mixed samples in high energy physics |
| url | http://hdl.handle.net/1721.1/114621 https://orcid.org/0000-0002-2406-8160 |
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