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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/2501.08988 |
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| _version_ | 1866909457130192896 |
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| author | Villarreal, Joshua Hardin, John M. Conrad, Janet M. |
| author_facet | Villarreal, Joshua Hardin, John M. Conrad, Janet M. |
| contents | For many small-signal particle physics analyses, Wilks' theorem, a simplifying assumption that presumes log-likelihood asymptotic normality, does not hold. The most common alternative approach applied in particle physics is a highly computationally expensive procedure put forward by Feldman and Cousins. When many experiments are combined for a global fit to data, deviations from Wilks' theorem are exacerbated, and Feldman-Cousins becomes computationally intractable. We present a novel, machine learning-based procedure that can approximate a full-fledged Bayesian analysis 200 times faster than the Feldman-Cousins method. We demonstrate the utility of this novel method by performing a joint analysis of electron neutrino/antineutrino disappearance data within a single sterile neutrino oscillation framework. Although we present a prototypical simulation-based inference method for a sterile neutrino global fit, we anticipate that similar procedures will be useful for global fits of all kinds, especially those in which Feldman-Cousins is too computationally expensive to use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08988 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feldman-Cousins' ML Cousin: Sterile Neutrino Global Fits using Simulation-Based Inference Villarreal, Joshua Hardin, John M. Conrad, Janet M. High Energy Physics - Experiment For many small-signal particle physics analyses, Wilks' theorem, a simplifying assumption that presumes log-likelihood asymptotic normality, does not hold. The most common alternative approach applied in particle physics is a highly computationally expensive procedure put forward by Feldman and Cousins. When many experiments are combined for a global fit to data, deviations from Wilks' theorem are exacerbated, and Feldman-Cousins becomes computationally intractable. We present a novel, machine learning-based procedure that can approximate a full-fledged Bayesian analysis 200 times faster than the Feldman-Cousins method. We demonstrate the utility of this novel method by performing a joint analysis of electron neutrino/antineutrino disappearance data within a single sterile neutrino oscillation framework. Although we present a prototypical simulation-based inference method for a sterile neutrino global fit, we anticipate that similar procedures will be useful for global fits of all kinds, especially those in which Feldman-Cousins is too computationally expensive to use. |
| title | Feldman-Cousins' ML Cousin: Sterile Neutrino Global Fits using Simulation-Based Inference |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2501.08988 |