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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.20926 |
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| _version_ | 1866908777547038720 |
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| author | Aguilar-Saavedra, J. A. Rodríguez-Benítez, S. |
| author_facet | Aguilar-Saavedra, J. A. Rodríguez-Benítez, S. |
| contents | Searches for new particles often span a wide range of mass scales, where the shape of potential signals and the SM background varies significantly. We make use of a multivariate method that fully exploits the correlation between signal and background features and the explored mass scale, and is trained on a sample that is balanced across the entire mass range. The classifiers, either a neural network or a boosted decision tree, produce a continuous output across the full mass range and, at a given mass, achieve nearly the same performance as a classifier specifically trained for that mass. The performance of the classifiers is better than the one obtained with parameterised neural networks and similar methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20926 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mass-unspecific classifiers for mass-dependent searches Aguilar-Saavedra, J. A. Rodríguez-Benítez, S. High Energy Physics - Phenomenology High Energy Physics - Experiment Searches for new particles often span a wide range of mass scales, where the shape of potential signals and the SM background varies significantly. We make use of a multivariate method that fully exploits the correlation between signal and background features and the explored mass scale, and is trained on a sample that is balanced across the entire mass range. The classifiers, either a neural network or a boosted decision tree, produce a continuous output across the full mass range and, at a given mass, achieve nearly the same performance as a classifier specifically trained for that mass. The performance of the classifiers is better than the one obtained with parameterised neural networks and similar methods. |
| title | Mass-unspecific classifiers for mass-dependent searches |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2503.20926 |