Bayesian inference of W-boson mass
Abstract We use a Bayesian regression technique (similar to a recent analysis by Rinaldi et al.) to obtain a central estimate for the W-boson mass using four different combinations of datasets compiled by the PDG including the 2022 CDF result. We use three different priors on the unknown intrinsic s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-07-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-023-11754-x |
| _version_ | 1797689144388878336 |
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| author | Aaseesh Rallapalli Shantanu Desai |
| author_facet | Aaseesh Rallapalli Shantanu Desai |
| author_sort | Aaseesh Rallapalli |
| collection | DOAJ |
| description | Abstract We use a Bayesian regression technique (similar to a recent analysis by Rinaldi et al.) to obtain a central estimate for the W-boson mass using four different combinations of datasets compiled by the PDG including the 2022 CDF result. We use three different priors on the unknown intrinsic scatter and also a non-parametric hierarchical Dirichlet Process Gaussian Mixture model to obtain a world average for W-boson mass. We also evaluate the statistical significance of the discrepancy with respect to the Standard model for each of the datasets. We find that for all the combination of datasets and the aformentioned prior choices, the discrepancy with respect to the Standard Model value for the W-mass is less than 3 $$\sigma $$ σ . We also checked that if we use a narrow prior on the intrinsic scatter, we get a discrepancy of about 3.8 $$\sigma $$ σ compared to the Standard model value. |
| first_indexed | 2024-03-12T01:41:43Z |
| format | Article |
| id | doaj.art-b2a9f0d87c984de9b87ebe13a6cd9c02 |
| institution | Directory Open Access Journal |
| issn | 1434-6052 |
| language | English |
| last_indexed | 2024-03-12T01:41:43Z |
| publishDate | 2023-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj.art-b2a9f0d87c984de9b87ebe13a6cd9c022023-09-10T11:24:06ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522023-07-018371710.1140/epjc/s10052-023-11754-xBayesian inference of W-boson massAaseesh Rallapalli0Shantanu Desai1Department of Electrical Engineering, Indian Institute of TechnologyDepartment of Physics, Indian Institute of TechnologyAbstract We use a Bayesian regression technique (similar to a recent analysis by Rinaldi et al.) to obtain a central estimate for the W-boson mass using four different combinations of datasets compiled by the PDG including the 2022 CDF result. We use three different priors on the unknown intrinsic scatter and also a non-parametric hierarchical Dirichlet Process Gaussian Mixture model to obtain a world average for W-boson mass. We also evaluate the statistical significance of the discrepancy with respect to the Standard model for each of the datasets. We find that for all the combination of datasets and the aformentioned prior choices, the discrepancy with respect to the Standard Model value for the W-mass is less than 3 $$\sigma $$ σ . We also checked that if we use a narrow prior on the intrinsic scatter, we get a discrepancy of about 3.8 $$\sigma $$ σ compared to the Standard model value.https://doi.org/10.1140/epjc/s10052-023-11754-x |
| spellingShingle | Aaseesh Rallapalli Shantanu Desai Bayesian inference of W-boson mass European Physical Journal C: Particles and Fields |
| title | Bayesian inference of W-boson mass |
| title_full | Bayesian inference of W-boson mass |
| title_fullStr | Bayesian inference of W-boson mass |
| title_full_unstemmed | Bayesian inference of W-boson mass |
| title_short | Bayesian inference of W-boson mass |
| title_sort | bayesian inference of w boson mass |
| url | https://doi.org/10.1140/epjc/s10052-023-11754-x |
| work_keys_str_mv | AT aaseeshrallapalli bayesianinferenceofwbosonmass AT shantanudesai bayesianinferenceofwbosonmass |