Certified Uncertainty for Surrogate Models of Neutron Star Equations of State via Mondrian Conformal Prediction
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917320792735744 |
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| author | Mendes, Marlon M. S. Pereira, Roberta Duarte Louren, Mariana Dutra da Rosa Lenzi, César H. |
| author_facet | Mendes, Marlon M. S. Pereira, Roberta Duarte Louren, Mariana Dutra da Rosa Lenzi, César H. |
| contents | We present a multitask surrogate for neutron-star equations of state (EoSs) that delivers \emph{distribution-free}, certified uncertainty via split conformal prediction (CP) and its Mondrian variant. The surrogate ingests a six-parameter piecewise-polytropic representation $(\log_{10}p_1,Γ_1,Γ_2,Γ_3,ρ_1,ρ_2)$ -- with fixed transition densities $ρ_1$ and $ρ_2$ -- and jointly performs (i) validity classification under physical/observational constraints and (ii) regression of $M_{\max}$, $R(M_{\max})$, $R_{1.4}$, and $Λ_{1.4}$. Trained on a balanced set of $40{,}000$ EoSs, the model attains near-perfect discrimination (AUC $\approx 0.997$) and sub-percent relative errors for masses and radii, with few-percent error for tidal deformability. Across $α\in[0.05,0.25]$, empirical coverages closely track $1-α$ for both Standard and Mondrian CP; in conservative regimes, Mondrian yields narrower average physical widths at comparable coverage. To our knowledge, this is the first application of class-conditioned (Mondrian) conformal calibration to neutron-star EoS surrogates, enabling efficient, reproducible, and uncertainty-aware inference; the framework is readily extensible to functional targets (e.g., full $R(M)$ curves). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19363 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Certified Uncertainty for Surrogate Models of Neutron Star Equations of State via Mondrian Conformal Prediction Mendes, Marlon M. S. Pereira, Roberta Duarte Louren, Mariana Dutra da Rosa Lenzi, César H. High Energy Astrophysical Phenomena Computational Physics We present a multitask surrogate for neutron-star equations of state (EoSs) that delivers \emph{distribution-free}, certified uncertainty via split conformal prediction (CP) and its Mondrian variant. The surrogate ingests a six-parameter piecewise-polytropic representation $(\log_{10}p_1,Γ_1,Γ_2,Γ_3,ρ_1,ρ_2)$ -- with fixed transition densities $ρ_1$ and $ρ_2$ -- and jointly performs (i) validity classification under physical/observational constraints and (ii) regression of $M_{\max}$, $R(M_{\max})$, $R_{1.4}$, and $Λ_{1.4}$. Trained on a balanced set of $40{,}000$ EoSs, the model attains near-perfect discrimination (AUC $\approx 0.997$) and sub-percent relative errors for masses and radii, with few-percent error for tidal deformability. Across $α\in[0.05,0.25]$, empirical coverages closely track $1-α$ for both Standard and Mondrian CP; in conservative regimes, Mondrian yields narrower average physical widths at comparable coverage. To our knowledge, this is the first application of class-conditioned (Mondrian) conformal calibration to neutron-star EoS surrogates, enabling efficient, reproducible, and uncertainty-aware inference; the framework is readily extensible to functional targets (e.g., full $R(M)$ curves). |
| title | Certified Uncertainty for Surrogate Models of Neutron Star Equations of State via Mondrian Conformal Prediction |
| topic | High Energy Astrophysical Phenomena Computational Physics |
| url | https://arxiv.org/abs/2602.19363 |