Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911973465128960 |
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| author | Englert, Philipp |
| author_facet | Englert, Philipp |
| contents | Extracting bounds on BSM operators at hadron colliders can be a highly non-trivial task. It can be useful or, depending on the complexity of the event structure, even essential to employ modern analysis techniques in order to measure New-Physics effects. A particular class of such modern methods are Machine-Learning algorithms, which are becoming more and more popular in particle physics. We attempt to gauge their potential in the study of $Vh(\rightarrow b\bar b)$ production processes, focusing on the leptonic decay channels of the vector bosons. Specifically, we employ boosted decision trees using the kinematical information of a given event to discriminate between signal and background. Based on this analysis strategy, we derive bounds on four dimension-6 SMEFT operators and subsequently compare them with the ones obtained from a conventional cut-and-count analysis. We find a mild improvement of $\mathcal{O}(\mathrm{few}\, \%)$ across the different operators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21239 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees Englert, Philipp High Energy Physics - Phenomenology Extracting bounds on BSM operators at hadron colliders can be a highly non-trivial task. It can be useful or, depending on the complexity of the event structure, even essential to employ modern analysis techniques in order to measure New-Physics effects. A particular class of such modern methods are Machine-Learning algorithms, which are becoming more and more popular in particle physics. We attempt to gauge their potential in the study of $Vh(\rightarrow b\bar b)$ production processes, focusing on the leptonic decay channels of the vector bosons. Specifically, we employ boosted decision trees using the kinematical information of a given event to discriminate between signal and background. Based on this analysis strategy, we derive bounds on four dimension-6 SMEFT operators and subsequently compare them with the ones obtained from a conventional cut-and-count analysis. We find a mild improvement of $\mathcal{O}(\mathrm{few}\, \%)$ across the different operators. |
| title | Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2407.21239 |