Bayesian model-data comparison incorporating theoretical uncertainties
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915574496362496 |
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| author | Jaiswal, Sunil Shen, Chun Furnstahl, Richard J. Heinz, Ulrich Pratola, Matthew T. |
| author_facet | Jaiswal, Sunil Shen, Chun Furnstahl, Richard J. Heinz, Ulrich Pratola, Matthew T. |
| contents | Accurate comparisons between theoretical models and experimental data are critical for scientific progress. However, inferred physical model parameters can vary significantly with the chosen physics model, highlighting the importance of properly accounting for theoretical uncertainties. In this Letter, we present a Bayesian framework that explicitly quantifies these uncertainties by statistically modeling theory errors, guided by qualitative knowledge of a theory's varying reliability across the input domain. We demonstrate the effectiveness of this approach using two systems: a simple ball drop experiment and multi-stage heavy-ion simulations. In both cases incorporating model discrepancy leads to improved parameter estimates, with systematic improvements observed as additional experimental observables are integrated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian model-data comparison incorporating theoretical uncertainties Jaiswal, Sunil Shen, Chun Furnstahl, Richard J. Heinz, Ulrich Pratola, Matthew T. High Energy Physics - Phenomenology Nuclear Theory Data Analysis, Statistics and Probability Accurate comparisons between theoretical models and experimental data are critical for scientific progress. However, inferred physical model parameters can vary significantly with the chosen physics model, highlighting the importance of properly accounting for theoretical uncertainties. In this Letter, we present a Bayesian framework that explicitly quantifies these uncertainties by statistically modeling theory errors, guided by qualitative knowledge of a theory's varying reliability across the input domain. We demonstrate the effectiveness of this approach using two systems: a simple ball drop experiment and multi-stage heavy-ion simulations. In both cases incorporating model discrepancy leads to improved parameter estimates, with systematic improvements observed as additional experimental observables are integrated. |
| title | Bayesian model-data comparison incorporating theoretical uncertainties |
| topic | High Energy Physics - Phenomenology Nuclear Theory Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2504.13144 |