标题： Quijote-PNG：优化总结统计量以测量原初非高斯性 显示英文标题

标题： Quijote-PNG: Optimizing the summary statistics to measure Primordial non-Gaussianity

摘要： 我们应用一系列不同的估计器到 Quijote-PNG 星团目录中，以寻找在非线性宇宙尺度上约束原初非高斯性（PNG）的最佳方法，直到$k_{\rm max} = 0.5 \, h\,{\rm Mpc}^{-1}$。 我们在分析中考虑的总结统计集包括功率谱、三功率谱、星团质量函数、标记功率谱和标记模态三功率谱。 标记统计在这里首次用于 PNG 研究的背景下。 我们进行了一项 Fisher 分析来估算它们的宇宙信息内容，在分析中加入标记可观测量时显示出显著改进。 从这些总结统计开始，我们训练深度神经网络（NN）来进行无偏似然推断的宇宙学和 PNG 参数。 我们评估了不同子集总结统计的表现；对于$f_\mathrm{NL}^\mathrm{equil}$情况下，我们发现功率谱与适当的标记功率谱组合的表现优于功率谱与三功率谱的组合，后者通常是 PNG 分析中使用的基准统计量。 一个最小的管道可以使用我们的机器学习算法来分析我们确定的统计量，或者通过更传统的估计器来实现，如果这些估计器被认为更可靠的话。 显示英文摘要

摘要： We apply a suite of different estimators to the Quijote-PNG halo catalogues to find the best approach to constrain Primordial non-Gaussianity (PNG) at non-linear cosmological scales, up to $k_{\rm max} = 0.5 \, h\,{\rm Mpc}^{-1}$. The set of summary statistics considered in our analysis includes the power spectrum, bispectrum, halo mass function, marked power spectrum, and marked modal bispectrum. Marked statistics are used here for the first time in the context of PNG study. We perform a Fisher analysis to estimate their cosmological information content, showing substantial improvements when marked observables are added to the analysis. Starting from these summaries, we train deep neural networks (NN) to perform likelihood-free inference of cosmological and PNG parameters. We assess the performance of different subsets of summary statistics; in the case of $f_\mathrm{NL}^\mathrm{equil}$, we find that a combination of the power spectrum and a suitable marked power spectrum outperforms the combination of power spectrum and bispectrum, the baseline statistics usually employed in PNG analysis. A minimal pipeline to analyse the statistics we identified can be implemented either with our ML algorithm or via more traditional estimators, if these are deemed more reliable.