比较用于弱引力透镜宇宙切变的显式似然和基于无似然模拟的推断
Comparing explicit likelihood and likelihood-free simulation-based inference for weak lensing cosmic shear
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中文总结 AI 辅助
该研究比较弱引力透镜宇宙切变中显式似然推断（ELI）和无似然推断（LFI）两种范式，用高斯随机场模拟分析相关函数及CNN统计量，通过后验校准诊断发现ELI易误校准，LFI更稳健，强调非高斯似然建模和后验校准对ELI分析的重要性。
中文摘要 AI 辅助
基于模拟的推断（SBI）已成为从弱引力透镜（WL）调查中提取宇宙学信息的主要工具，特别是从非高斯可观测量中提取信息。我们比较了它的两种主要范式：显式似然推断（ELI），基于由模拟器和协方差矩阵构建的高斯似然；以及无似然推断（LFI），它使用神经密度估计器直接从模拟中学习似然。使用代表非层析最终欧几里得数据发布的高斯随机场模拟，我们分析了用线性或非线性方法压缩的切变两点相关函数（shear-2PCFs），以及一种根本不同的地图级卷积神经网络（CNN）统计量，重点关注$\Omega_{\rm m}$和$S_8$。我们部署了为LFI开发的后验校准诊断，包括随机点准确性测试（TARP），表明在模拟不准确或似然非高斯性的情况下，ELI会严重误校准，而LFI仍能良好校准。这些效应导致ELI和LFI之间存在很大分歧，一旦解决，这种分歧在很大程度上就会消失。我们进一步表明，压缩方案会显著降低ELI，而对LFI影响不大。尽管shear-2PCFs应捕获高斯场中的所有信息，但有限压缩和非高斯似然会导致ELI约束与用CNN推断的约束相差高达两倍，而LFI的差异降至约30%，突出了深度学习探测器的稳健性。总体而言，我们的结果表明，在我们忽略系统偏差的简单设置中，LFI提供了一个更稳健和校准更好的框架，同时强调准确的非高斯似然建模和后验校准诊断对未来ELI分析至关重要。
英文摘要
Simulation-based inference (SBI) has become a major tool for extracting cosmological information from weak-lensing (WL) surveys, particularly from non-Gaussian observables. We compare its two main paradigms: explicit likelihood inference (ELI), based on a Gaussian likelihood built from an emulator and covariance matrix, and likelihood-free inference (LFI), which learns the likelihood directly from simulations using neural density estimators. Using Gaussian random field mocks representative of the non-tomographic final Euclid data release, we analyse shear two-point correlation functions (shear-2PCFs), compressed with linear or non-linear methods, together with a fundamentally different map-level convolutional neural network (CNN) statistic, focusing on $Ω_{\rm m}$ and $S_8$. We deploy posterior calibration diagnostics developed for LFI, including the test of accuracy with random points (TARP), showing that ELI becomes strongly miscalibrated under emulation inaccuracies or likelihood non-Gaussianity, whereas LFI remains well calibrated. These effects drive substantial disagreement between ELI and LFI, which largely vanishes once addressed. We further show that the compression scheme can significantly degrade ELI while leaving LFI largely unaffected. Although shear-2PCFs should capture all the information in Gaussian fields, finite compression and non-Gaussian likelihoods cause ELI constraints to differ by up to a factor of two from those inferred with the CNN, while the discrepancy drops to $\approx 30\%$ for LFI, underscoring the robustness of the deep-learning probe. Overall, our results indicate that in our simple setup, which neglects systematic biases, LFI provides a more robust and better-calibrated framework, while highlighting accurate non-Gaussian likelihood modelling and posterior calibration diagnostics as essential for future ELI analyses.
