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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.11224 |
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| _version_ | 1866918383744712704 |
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| author | Conde, Daniel Folgado, Miguel G. Sanz, Veronica |
| author_facet | Conde, Daniel Folgado, Miguel G. Sanz, Veronica |
| contents | The Simplified Template Cross Section (STXS) program has become the standard interface between Higgs measurements and global fits, but its fixed one-dimensional boundaries are not guaranteed to align with the phase-space directions to which the Standard Model Effective Field Theory (SMEFT) is most sensitive. We propose a machine-learning-inspired extension of STXS in which supervised classifiers are used only at the design stage to identify simple, publishable phase-space boundaries. Using associated Higgs production, $pp \to ZH$, as a case study and a benchmark momentum-dependent bosonic SMEFT deformation, we show that the relevant signal-background separation is well captured by a linear boundary in the $(p_T^Z,mZH)$ plane. We construct such boundaries with a linear support vector machine and with a deep-neural-network-assisted distillation procedure, and compare them directly with the standard STXS $p_T^Z$ bins through a common single-region Asimov-significance analysis. In this proof-of-concept setup, the ML-inspired regions systematically outperform the corresponding STXS regions, with the largest gains appearing in the boosted regime where SMEFT effects are concentrated. The final observable remains a simple linear cut, preserving the transparency and experimental portability that make STXS useful. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11224 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine-Learning-Inspired SMEFT Simplified Template Cross Sections: A Case Study in ZH Production Conde, Daniel Folgado, Miguel G. Sanz, Veronica High Energy Physics - Phenomenology The Simplified Template Cross Section (STXS) program has become the standard interface between Higgs measurements and global fits, but its fixed one-dimensional boundaries are not guaranteed to align with the phase-space directions to which the Standard Model Effective Field Theory (SMEFT) is most sensitive. We propose a machine-learning-inspired extension of STXS in which supervised classifiers are used only at the design stage to identify simple, publishable phase-space boundaries. Using associated Higgs production, $pp \to ZH$, as a case study and a benchmark momentum-dependent bosonic SMEFT deformation, we show that the relevant signal-background separation is well captured by a linear boundary in the $(p_T^Z,mZH)$ plane. We construct such boundaries with a linear support vector machine and with a deep-neural-network-assisted distillation procedure, and compare them directly with the standard STXS $p_T^Z$ bins through a common single-region Asimov-significance analysis. In this proof-of-concept setup, the ML-inspired regions systematically outperform the corresponding STXS regions, with the largest gains appearing in the boosted regime where SMEFT effects are concentrated. The final observable remains a simple linear cut, preserving the transparency and experimental portability that make STXS useful. |
| title | Machine-Learning-Inspired SMEFT Simplified Template Cross Sections: A Case Study in ZH Production |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2603.11224 |