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| Formato: | Preprint |
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2025
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| Acceso en línea: | https://arxiv.org/abs/2508.21541 |
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| _version_ | 1866918141450256384 |
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| author | Uniyal, Akhil Kalita, Surajit Mizuno, Yosuke Chakrabarti, Sayan Lu, Yan |
| author_facet | Uniyal, Akhil Kalita, Surajit Mizuno, Yosuke Chakrabarti, Sayan Lu, Yan |
| contents | We explore the potential variation of two fundamental constants, the fine-structure constant $α$ and the proton-to-electron mass ratio $μ$, within the framework of modified gravity theories and finite-temperature effects. Utilising high-precision white dwarf observations from the Gaia-DR3 survey, we construct a robust mass--radius relation using a Bayesian-inspired machine learning framework. This empirical relation is rigorously compared with theoretical predictions derived from scalar-tensor gravity models and temperature-dependent equations of state. Our results demonstrate that both underlying gravitational theory and temperature substantially influence the inferred constraints on $α$ and $μ$. We obtain the strongest constraints as $|Δα/α|=2.10^{+32.56}_{-39.26}\times10^{-7}$ and $|Δμ/μ|=1.61^{+37.16}_{-34.67}\times10^{-7}$ for modified gravity parameter $γ\simeq -3.69\times10^{13}\,\mathrm{cm}^2$, while for the finite temperature case, these are $|Δα/α|=1.60^{+37.31}_{-35.42}\times10^{-7}$ and $|Δμ/μ|=1.23^{+37.02}_{-35.71}\times10^{-7}$ for $T \simeq 1.1 \times 10^7\rm\, K$. These findings yield tighter constraints than those reported in earlier studies and underscore the critical roles of gravitational and thermal physics in testing the constancy of fundamental parameters. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_21541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Refining fundamental constants with white dwarfs: machine learning informed constraints on fine-structure constant and proton-to-electron mass ratio Uniyal, Akhil Kalita, Surajit Mizuno, Yosuke Chakrabarti, Sayan Lu, Yan Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology We explore the potential variation of two fundamental constants, the fine-structure constant $α$ and the proton-to-electron mass ratio $μ$, within the framework of modified gravity theories and finite-temperature effects. Utilising high-precision white dwarf observations from the Gaia-DR3 survey, we construct a robust mass--radius relation using a Bayesian-inspired machine learning framework. This empirical relation is rigorously compared with theoretical predictions derived from scalar-tensor gravity models and temperature-dependent equations of state. Our results demonstrate that both underlying gravitational theory and temperature substantially influence the inferred constraints on $α$ and $μ$. We obtain the strongest constraints as $|Δα/α|=2.10^{+32.56}_{-39.26}\times10^{-7}$ and $|Δμ/μ|=1.61^{+37.16}_{-34.67}\times10^{-7}$ for modified gravity parameter $γ\simeq -3.69\times10^{13}\,\mathrm{cm}^2$, while for the finite temperature case, these are $|Δα/α|=1.60^{+37.31}_{-35.42}\times10^{-7}$ and $|Δμ/μ|=1.23^{+37.02}_{-35.71}\times10^{-7}$ for $T \simeq 1.1 \times 10^7\rm\, K$. These findings yield tighter constraints than those reported in earlier studies and underscore the critical roles of gravitational and thermal physics in testing the constancy of fundamental parameters. |
| title | Refining fundamental constants with white dwarfs: machine learning informed constraints on fine-structure constant and proton-to-electron mass ratio |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2508.21541 |