基于群体级校准神经比率估计的强引力透镜宇宙学
Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation
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中文总结 AI 辅助
该研究提出群体级校准神经比率估计方法，利用引力透镜图像和光谱信息，可从ΛCDM宇宙的100个透镜中实现暗能量参数w和物质密度Ω_m的高精度推断，为大规模宇宙学参数推断提供了可扩展方案。
中文摘要 AI 辅助
强引力透镜包含宇宙加速的关键信息。现代及下一代星系成像巡天预计将提供约10^5个星系-星系引力透镜系统的高质量数据，这类海量且复杂的数据可能会给拟合高维似然的参数推断方法带来计算挑战，而这类似然通常难以解析处理。神经比率估计（Neural Ratio Estimation, NRE）可高效计算单个似然比，这些似然比可组合为群体级后验分布。我们利用模拟数据研究NRE从透镜图像及配套光谱信息中联合预测暗能量状态方程参数w和总物质密度Ω_m的能力，还引入一种事后后验覆盖校准程序，以缓解神经密度估计应用中常见的模型过度自信问题。实验表明，两个参数的误差随推断群体规模增大而减小；特别地，在标准ΛCDM宇宙中，针对100个透镜，我们的校准NRE模型在w上实现22.8%的中位数分数不确定度，在Ω_m上实现2.9%的中位数分数不确定度。这一概念验证为未来巡天观测的大量星系尺度透镜提供了一种潜在可扩展的高效宇宙学参数推断方法。
英文摘要
Strong gravitational lensing contains key information about cosmic acceleration. Modern and next-generation galaxy imaging surveys are expected to provide high-quality data on $\mathcal{O}(10^5)$ galaxy-galaxy lensing systems. The plethora and complexity of the data are likely to present computational challenges for parameter inference methods for fitting high-dimensional likelihoods, which are often analytically intractable. Neural Ratio Estimation (NRE) efficiently computes individual likelihood ratios that can be combined into population-level posteriors. We use simulations to study the capacity of NRE to jointly predict the dark energy equation-of-state parameter $w$ and the total matter density $Ω_{m}$ from lensing images and companion spectroscopic information. We also introduce a post hoc posterior coverage calibration procedure that mitigates the model overconfidence that is typically found in neural density estimation applications. Our experiments show that the errors on both parameters decrease with increasing inference population sizes. In particular, for 100 lenses in a standard $Λ$CDM Universe, our calibrated NRE model achieves median fractional uncertainty of $22.8\%$ in $w$ and $2.9\%$ in $Ω_{m}$. This proof of concept demonstrates a potentially scalable approach for efficient cosmological parameter inference with large populations of galaxy-scale lenses observed in future surveys.
