Adaptive proposals for nonparametric equation-of-state inference with RIFT: iterative Gaussian-process refinement in a compressed sequence representation
Abstract
Nonparametric equation-of-state (EOS) inference evaluates the likelihood on a finite ensemble drawn from a prescribed functional prior. Reweighting that ensemble suffices for the binary neutron stars detected so far, and loses effective sample size only once the likelihood concentrates --- which needs third-generation catalogues, or extra electromagnetic and nuclear-theory information folded into the same target. We develop and validate an adaptive-proposal procedure that leaves the base prior fixed: data-informed generations are combined by multiple importance sampling and, where that degenerates at large catalogue size, by a calibrated Gaussian projection. We implement it in two representations --- a compressed principal-component parametrization of the neutron-star sequence, whose draws need no stellar-structure solve, and a sound-speed parametrization with a differentiable Tolman--Oppenheimer--Volkoff solver --- both on a constrained low-density-to-pQCD bridge. Because that anchor, and not the bridge, fixes the low-density prior, we rebuild the ensemble with a crust-plus-chiral-EFT anchor in place of the wide nuclear metamodel: the maximum-mass prior is unmoved, the radius gains a hard prior ceiling at the measured NICER value. GW170817 with the specified NICER and radio factors gives the calibration-conditional R(1.4 M⊙)=11.7±0.4km; in a synthetic $1000$-event catalogue it reproduces the mean fixed-prior Λ(1.4 M⊙) to $0.4$%, $1.5$ times wider, as the fixed archive's effective sample size falls from ∼ 470 to 22.
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BibTeX
@misc{oshaughnessy2026adaptive,
title = {{Adaptive proposals for nonparametric equation-of-state inference with RIFT: iterative Gaussian-process refinement in a compressed sequence representation}},
author = {R. O'Shaughnessy and Z. Zhu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.07424},
eprint = {2610.07424},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.07424},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Adaptive proposals for nonparametric equation-of-state inference with RIFT: iterative Gaussian-process refinement in a compressed sequence representation
AU - R. O'Shaughnessy
AU - Z. Zhu
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.07424
UR - https://arxiv.org/abs/2610.07424
AB - Nonparametric equation-of-state (EOS) inference evaluates the likelihood on a finite ensemble drawn from a prescribed functional prior. Reweighting that ensemble suffices for the binary neutron stars detected so far, and loses effective sample size only once the likelihood concentrates --- which needs third-generation catalogues, or extra electromagnetic and nuclear-theory information folded into the same target. We develop and validate an adaptive-proposal procedure that leaves the base prior fixed: data-informed generations are combined by multiple importance sampling and, where that degenerates at large catalogue size, by a calibrated Gaussian projection. We implement it in two representations --- a compressed principal-component parametrization of the neutron-star sequence, whose draws need no stellar-structure solve, and a sound-speed parametrization with a differentiable Tolman--Oppenheimer--Volkoff solver --- both on a constrained low-density-to-pQCD bridge. Because that anchor, and not the bridge, fixes the low-density prior, we rebuild the ensemble with a crust-plus-chiral-EFT anchor in place of the wide nuclear metamodel: the maximum-mass prior is unmoved, the radius gains a hard prior ceiling at the measured NICER value. GW170817 with the specified NICER and radio factors gives the calibration-conditional R(1.4 M_⊙)=11.7±0.4km; in a synthetic $1000$-event catalogue it reproduces the mean fixed-prior Λ(1.4 M_⊙) to $0.4$%, $1.5$ times wider, as the fixed archive's effective sample size falls from ∼ 470 to 22.
ER -