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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.25407 |
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| _version_ | 1866918471160299520 |
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| author | Moreno, Diego A. Baron Englert, Christoph Peters, Yvonne |
| author_facet | Moreno, Diego A. Baron Englert, Christoph Peters, Yvonne |
| contents | Destructive interference between signal and background processes poses a fundamental challenge in searches for top-philic scalar resonances, significantly reducing experimental sensitivity to well-motivated extensions of the Higgs sector. Traditional bump-hunting strategies fail in this instance because interference effects invalidate the narrow-width approximation across large regions of the BSM parameter space. As a result, experimental analyses typically rely on detailed simulations to accurately model these effects throughout the full analysis chain. In this work, we consider the inverse problem in a proof-of-principle study: given an observed pattern in a discriminating distribution, what is the likelihood that it originates from a BSM scalar? To address this, we employ parametric neural networks to learn the likelihood ratio as a function of both background and key BSM parameters, based on a ratio-of-signed-mixtures framework. We perform inference by testing the compatibility of observed data with a scan over the parameter space of a minimal scalar extension of the Standard Model. While BSM parameter extraction remains inherently model-dependent, our approach provides a robust diagnostic in perturbative regimes and motivates a complementary strategy of `dip-hunting'. This strategy extends traditional bump-hunts and could point the way as we navigate towards future discoveries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25407 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Big Dipper, Help Me Find A Way -- Dip-hunting at hadron colliders Moreno, Diego A. Baron Englert, Christoph Peters, Yvonne High Energy Physics - Phenomenology Destructive interference between signal and background processes poses a fundamental challenge in searches for top-philic scalar resonances, significantly reducing experimental sensitivity to well-motivated extensions of the Higgs sector. Traditional bump-hunting strategies fail in this instance because interference effects invalidate the narrow-width approximation across large regions of the BSM parameter space. As a result, experimental analyses typically rely on detailed simulations to accurately model these effects throughout the full analysis chain. In this work, we consider the inverse problem in a proof-of-principle study: given an observed pattern in a discriminating distribution, what is the likelihood that it originates from a BSM scalar? To address this, we employ parametric neural networks to learn the likelihood ratio as a function of both background and key BSM parameters, based on a ratio-of-signed-mixtures framework. We perform inference by testing the compatibility of observed data with a scan over the parameter space of a minimal scalar extension of the Standard Model. While BSM parameter extraction remains inherently model-dependent, our approach provides a robust diagnostic in perturbative regimes and motivates a complementary strategy of `dip-hunting'. This strategy extends traditional bump-hunts and could point the way as we navigate towards future discoveries. |
| title | Big Dipper, Help Me Find A Way -- Dip-hunting at hadron colliders |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2604.25407 |