Deep Neural Networks for Heavy Lepton-Flavor-Violating Higgs Searches at the LHC
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914585413419008 |
|---|---|
| author | Ferdiyan, Akmal Primulando, Reinard Akbar, Fiki Taufik Gunara, Bobby Eka |
| author_facet | Ferdiyan, Akmal Primulando, Reinard Akbar, Fiki Taufik Gunara, Bobby Eka |
| contents | We study lepton-flavor-violating (LFV) decays of a heavy Higgs boson, $H \to μτ$, in the Type-III two-Higgs-doublet model by recasting the CMS search at $\sqrt{s} = 13$ TeV with 35.9 fb$^{-1}$ using fast detector simulation in the mass range 200-450 GeV. We develop a deep neural network (DNN) classifier trained on final-state kinematic variables that, with mass-dependent threshold optimization, reduces the expected 95% CL upper limits on the signal cross section by 42-46% in the 0-jet channel and 36-40% in the 1-jet channel relative to the standard collinear mass ($M_\mathrm{col}$) baseline. We apply SHAP interpretability analysis to identify the visible mass $m_\mathrm{vis}$ as one of the dominant discriminating feature, reflecting the characteristic neutrino momentum fraction of the $τ$ decay. We show that supplementing the $M_\mathrm{col}$ analysis with a simplified mass-dependent pre-selection, $m_\mathrm{vis} < f \cdot m_H$ with $f = 0.7$ (0-jet) and $f = 0.8$ (1-jet), consistently improves the sensitivity over the $M_\mathrm{col}$-only baseline without requiring multivariate infrastructure. In addition, a DNN regression model trained to predict the ratio $m_H/M_\mathrm{col}$ corrects the systematic prediction bias inherent in the collinear approximation, maintaining an absolute mass prediction error below 1 GeV for signals up to 400 GeV and improving the mass resolution by 12% (0-jet) and 21% (1-jet) at $m_H = 450$ GeV. These results demonstrate a clear path toward significantly enhanced sensitivity in LFV Higgs searches at the LHC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21870 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Neural Networks for Heavy Lepton-Flavor-Violating Higgs Searches at the LHC Ferdiyan, Akmal Primulando, Reinard Akbar, Fiki Taufik Gunara, Bobby Eka High Energy Physics - Phenomenology We study lepton-flavor-violating (LFV) decays of a heavy Higgs boson, $H \to μτ$, in the Type-III two-Higgs-doublet model by recasting the CMS search at $\sqrt{s} = 13$ TeV with 35.9 fb$^{-1}$ using fast detector simulation in the mass range 200-450 GeV. We develop a deep neural network (DNN) classifier trained on final-state kinematic variables that, with mass-dependent threshold optimization, reduces the expected 95% CL upper limits on the signal cross section by 42-46% in the 0-jet channel and 36-40% in the 1-jet channel relative to the standard collinear mass ($M_\mathrm{col}$) baseline. We apply SHAP interpretability analysis to identify the visible mass $m_\mathrm{vis}$ as one of the dominant discriminating feature, reflecting the characteristic neutrino momentum fraction of the $τ$ decay. We show that supplementing the $M_\mathrm{col}$ analysis with a simplified mass-dependent pre-selection, $m_\mathrm{vis} < f \cdot m_H$ with $f = 0.7$ (0-jet) and $f = 0.8$ (1-jet), consistently improves the sensitivity over the $M_\mathrm{col}$-only baseline without requiring multivariate infrastructure. In addition, a DNN regression model trained to predict the ratio $m_H/M_\mathrm{col}$ corrects the systematic prediction bias inherent in the collinear approximation, maintaining an absolute mass prediction error below 1 GeV for signals up to 400 GeV and improving the mass resolution by 12% (0-jet) and 21% (1-jet) at $m_H = 450$ GeV. These results demonstrate a clear path toward significantly enhanced sensitivity in LFV Higgs searches at the LHC. |
| title | Deep Neural Networks for Heavy Lepton-Flavor-Violating Higgs Searches at the LHC |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2605.21870 |