LHCb Topological Trigger Reoptimization
The main b-physics trigger algorithm used by the LHCb experiment is the so- called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the decay of a b-hadron. In the LHC Run 1, this trigger,...
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| Format: | Article |
| Language: | en_US |
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IOP Publishing
2017
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| Online Access: | http://hdl.handle.net/1721.1/108457 https://orcid.org/0000-0001-5534-1732 https://orcid.org/0000-0001-8285-3346 |
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| author | Likhomanenko, Tatiana Khairullin, Egor Rogozhnikov, Alex Ustyuzhanin, Andrey Ilten, Philip J Williams, Michael |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Likhomanenko, Tatiana Khairullin, Egor Rogozhnikov, Alex Ustyuzhanin, Andrey Ilten, Philip J Williams, Michael |
| author_sort | Likhomanenko, Tatiana |
| collection | MIT |
| description | The main b-physics trigger algorithm used by the LHCb experiment is the so- called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the decay of a b-hadron. In the LHC Run 1, this trigger, which utilized a custom boosted decision tree algorithm, selected a nearly 100% pure sample of b-hadrons with a typical efficiency of 60-70%; its output was used in about 60% of LHCb papers. This talk presents studies carried out to optimize the topological trigger for LHC Run 2. In particular, we have carried out a detailed comparison of various machine learning classifier algorithms, e.g., AdaBoost, MatrixNet and neural networks. The topological trigger algorithm is designed to select all 'interesting" decays of b-hadrons, but cannot be trained on every such decay. Studies have therefore been performed to determine how to optimize the performance of the classification algorithm on decays not used in the training. Methods studied include cascading, ensembling and blending techniques. Furthermore, novel boosting techniques have been implemented that will help reduce systematic uncertainties in Run 2 measurements. We demonstrate that the reoptimized topological trigger is expected to significantly improve on the Run 1 performance for a wide range of b-hadron decays. |
| first_indexed | 2024-09-23T10:30:22Z |
| format | Article |
| id | mit-1721.1/108457 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:30:22Z |
| publishDate | 2017 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1084572022-09-27T09:51:30Z LHCb Topological Trigger Reoptimization Likhomanenko, Tatiana Khairullin, Egor Rogozhnikov, Alex Ustyuzhanin, Andrey Ilten, Philip J Williams, Michael Massachusetts Institute of Technology. Department of Physics Ilten, Philip J Williams, Michael The main b-physics trigger algorithm used by the LHCb experiment is the so- called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the decay of a b-hadron. In the LHC Run 1, this trigger, which utilized a custom boosted decision tree algorithm, selected a nearly 100% pure sample of b-hadrons with a typical efficiency of 60-70%; its output was used in about 60% of LHCb papers. This talk presents studies carried out to optimize the topological trigger for LHC Run 2. In particular, we have carried out a detailed comparison of various machine learning classifier algorithms, e.g., AdaBoost, MatrixNet and neural networks. The topological trigger algorithm is designed to select all 'interesting" decays of b-hadrons, but cannot be trained on every such decay. Studies have therefore been performed to determine how to optimize the performance of the classification algorithm on decays not used in the training. Methods studied include cascading, ensembling and blending techniques. Furthermore, novel boosting techniques have been implemented that will help reduce systematic uncertainties in Run 2 measurements. We demonstrate that the reoptimized topological trigger is expected to significantly improve on the Run 1 performance for a wide range of b-hadron decays. 2017-04-27T17:37:53Z 2017-04-27T17:37:53Z 2015-12 2015-11 Article http://purl.org/eprint/type/JournalArticle 1742-6588 1742-6596 http://hdl.handle.net/1721.1/108457 Likhomanenko, Tatiana; Ilten, Philip; Khairullin, Egor; Rogozhnikov, Alex; Ustyuzhanin, Andrey and Williams, Michael. “LHCb Topological Trigger Reoptimization.” Journal of Physics: Conference Series 664, no. 8 (December 2015): 082025. © 2015 IOP Publishing https://orcid.org/0000-0001-5534-1732 https://orcid.org/0000-0001-8285-3346 en_US http://dx.doi.org/10.1088/1742-6596/664/8/082025 Journal of Physics: Conference Series Creative Commons Attribution 3.0 Unported license http://creativecommons.org/licenses/by/3.0/ application/pdf IOP Publishing IOP Publishing |
| spellingShingle | Likhomanenko, Tatiana Khairullin, Egor Rogozhnikov, Alex Ustyuzhanin, Andrey Ilten, Philip J Williams, Michael LHCb Topological Trigger Reoptimization |
| title | LHCb Topological Trigger Reoptimization |
| title_full | LHCb Topological Trigger Reoptimization |
| title_fullStr | LHCb Topological Trigger Reoptimization |
| title_full_unstemmed | LHCb Topological Trigger Reoptimization |
| title_short | LHCb Topological Trigger Reoptimization |
| title_sort | lhcb topological trigger reoptimization |
| url | http://hdl.handle.net/1721.1/108457 https://orcid.org/0000-0001-5534-1732 https://orcid.org/0000-0001-8285-3346 |
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