发表机构
Stockholm University; Flatiron Institute; Sorbonne Université（斯德哥尔摩大学; 平顿研究所; 索邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ROBIN-PIP框架利用物理信息先验，将星系偏袒模型的参数推断引导至物理合理区域，在模拟宇宙中联合推断初始条件与偏袒参数，降低后验标准差并提升采样效率，且不损害数据似然。
AI 中文摘要
从星系巡天中准确提取信息，特别是在非线性尺度上，需要对观测到的星系分布建立日益复杂且灵活的前向模型。其中一个关键组成部分是星系偏袒关系，它将底层物质分布与观测到的星系联系起来，并且需要比当前已纳入贝叶斯场级推断中的表示更具表达力的表示形式。针对这种映射的某些参数化模型可能过度参数化，其中各个参数没有直接的物理意义，这使得它们难以被约束。我们的目标是通过物理信息先验，将灵活模型参数的推断引导至参数空间中物理上合理的区域。这些先验源自高保真模拟，从而能够间接利用那些无法直接整合到基于梯度的场级推断中的模拟。为此，我们引入了ROBIN-PIP（具有物理信息先验的鲁棒贝叶斯推断）框架。作为概念验证，我们在一个模拟宇宙中联合推断宇宙初始条件和截断幂律星系偏袒模型的参数。我们将ROBIN-PIP集成到星系贝叶斯起源重建算法中，并与不使用基于模拟先验的推断进行基准比较。在额外先验的情况下，偏袒模型参数的后验标准差降低了高达20%，自相关长度从730-870降至440-550，从而增加了有效样本量。我们发现在恢复的初始条件中没有偏差，这表明ROBIN-PIP在不损害数据似然的情况下将推断引导至物理上一致的解，并突显了其在场级推断中纳入过度参数化模型的潜力。
英文摘要
Accurately extracting information from galaxy surveys, particularly at non-linear scales, requires increasingly complex and flexible forward models of the observed galaxy distribution. A key component is the galaxy bias relation, which connects the underlying matter distribution to observed galaxies and requires more expressive representations than those currently incorporated into Bayesian field-level inference. Certain parameterized models for this mapping can be overparameterized, with individual parameters carrying no direct physical interpretation, making them challenging to constrain. Our objective is to guide the inference of flexible model parameters towards physically plausible regions of parameter space through physics-informed priors. These priors are derived from high-fidelity simulations, thereby enabling the indirect use of simulations that cannot be integrated directly into gradient-based field-level inference. To this end, we introduce the ROBIN-PIP (RObust Bayesian INference with Physics-Informed Priors) framework. As a proof of concept, we jointly infer the cosmic initial conditions and the parameters of a truncated power-law galaxy bias model for a simulated universe. We integrate ROBIN-PIP into the Bayesian Origin Reconstruction from Galaxies algorithm and benchmark against inference without the simulation-based prior. With the additional prior, posterior standard deviations of the bias model parameters are reduced by up to $20\%$ and autocorrelation lengths are reduced from $730$-$870$ to $440$-$550$, increasing the effective sample sizes. We find no bias in the recovered initial conditions, demonstrating that ROBIN-PIP guides inference towards physically consistent solutions without compromising the data likelihood and highlighting its potential to incorporate overparameterized models in field-level inference.
Comments14 pages, 4 figures
