发表机构
Rochester Institute of Technology（罗切斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对非参数状态方程推断，提出自适应提议程序，通过多重重要性采样和校准高斯投影组合数据知情生成，在压缩表示中实现，GW170817校准给出半径11.7±0.4km，合成目录中有效样本量从470降至22。
AI 中文摘要
非参数状态方程（EOS）推断在由预设函数先验抽取的有限系综上评估似然。对迄今探测到的双中子星，对该系综进行重加权已足够，但仅在似然集中时损失有效样本量——这需要第三代目录，或额外电磁和核理论信息折叠进同一目标。我们开发并验证了一种自适应提议程序，保持基础先验固定：通过多重重要性采样组合数据知情生成，并在大目录规模下退化时通过校准高斯投影组合。我们在两种表示中实现它——中子星序列的压缩主成分参数化（其抽样无需恒星结构求解）和具有可微Tolman-Oppenheimer-Volkoff求解器的声速参数化——两者均受限于从低密度到pQCD的约束桥。因为该锚点（而非桥）固定低密度先验，我们用壳层加手征EFT锚点重建系综，取代宽核元模型：最大质量先验不变，半径获得在实测NICER值处的硬先验上限。GW170817结合指定NICER和射电因子给出校准条件$R(1.4\\,M_\odot)=11.7\pm0.4$km；在合成的1000事件目录中，它重现固定先验平均$\Lambda(1.4\\,M_\odot)$至0.4%，宽度为1.5倍，而固定档案的有效样本量从约470降至22。
英文摘要
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 $R(1.4\,M_\odot)=11.7\pm0.4$km; in a synthetic $1000$-event catalogue it reproduces the mean fixed-prior $Λ(1.4\,M_\odot)$ to $0.4$%, $1.5$ times wider, as the fixed archive's effective sample size falls from $\sim\!470$ to 22.
Comments28 pages, 13 figures
