Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01153 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909795656663040 |
|---|---|
| author | Villarreal, Joshua Woodward, Julia Hardin, John Conrad, Janet |
| author_facet | Villarreal, Joshua Woodward, Julia Hardin, John Conrad, Janet |
| contents | A critical challenge in particle physics is combining results from diverse experimental setups that measure the same physical quantity to enhance precision and statistical power, a process known as a global fit. Global fits of sterile neutrino searches, hunts for additional neutrino oscillation frequencies and amplitudes, present an intriguing case study. In such a scenario, the key assumptions underlying Wilks' theorem, a cornerstone of most classic frequentist analyses, do not hold. The method of Feldman and Cousins, a trials-based approach which does not assume Wilks' theorem, becomes computationally prohibitive for complex or intractable likelihoods. To bypass this limitation, we borrow a technique from simulation-based inference (SBI) to estimate likelihood ratios for use in building trials-based confidence intervals, speeding up test statistic evaluations by a factor $>10^4$ per grid point, resulting in a faster, but approximate, frequentist fitting framework. Applied to a subset of sterile neutrino search data involving the disappearance of muon-flavor (anti)neutrinos, our method leverages machine learning to compute frequentist confidence intervals while significantly reducing computational expense. In addition, the SBI-based approach holds additional value by recognizing underlying systematic uncertainties that the Wilks approach does not. Thus, our method allows for more robust machine learning-based analyses critical to performing accurate but computationally feasible global fits. This allows, for the first time, a global fit to sterile neutrino data without assuming Wilks' theorem. While we demonstrate the utility of such a technique studying sterile neutrino searches, it is applicable to both single-experiment and global fits of all kinds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Frequentist Simulation-Based Inference Treatment of Sterile Neutrino Global Fits Villarreal, Joshua Woodward, Julia Hardin, John Conrad, Janet High Energy Physics - Phenomenology High Energy Physics - Experiment A critical challenge in particle physics is combining results from diverse experimental setups that measure the same physical quantity to enhance precision and statistical power, a process known as a global fit. Global fits of sterile neutrino searches, hunts for additional neutrino oscillation frequencies and amplitudes, present an intriguing case study. In such a scenario, the key assumptions underlying Wilks' theorem, a cornerstone of most classic frequentist analyses, do not hold. The method of Feldman and Cousins, a trials-based approach which does not assume Wilks' theorem, becomes computationally prohibitive for complex or intractable likelihoods. To bypass this limitation, we borrow a technique from simulation-based inference (SBI) to estimate likelihood ratios for use in building trials-based confidence intervals, speeding up test statistic evaluations by a factor $>10^4$ per grid point, resulting in a faster, but approximate, frequentist fitting framework. Applied to a subset of sterile neutrino search data involving the disappearance of muon-flavor (anti)neutrinos, our method leverages machine learning to compute frequentist confidence intervals while significantly reducing computational expense. In addition, the SBI-based approach holds additional value by recognizing underlying systematic uncertainties that the Wilks approach does not. Thus, our method allows for more robust machine learning-based analyses critical to performing accurate but computationally feasible global fits. This allows, for the first time, a global fit to sterile neutrino data without assuming Wilks' theorem. While we demonstrate the utility of such a technique studying sterile neutrino searches, it is applicable to both single-experiment and global fits of all kinds. |
| title | A Frequentist Simulation-Based Inference Treatment of Sterile Neutrino Global Fits |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2507.01153 |