AI 中文总结
研究利用新模拟方法，通过弱引力透镜、热苏尼亚耶夫-泽尔多维奇效应和X射线场，量化对原初非高斯性的约束能力，发现tSZ和X射线场能提供重要信息，打破参数简并，相比仅用引力透镜，PNG约束有两倍改善。
AI 中文摘要
原初非高斯性（PNGs）是初始密度场中的一类广泛特征，与早期宇宙的粒子物理学相关。直接测量这些特征的幅度可约束早期的基础物理学，并深入了解地面实验无法探测的能量尺度。我们使用一类新的模拟方法，将这些特征传播到它们对非线性结构形成的影响，并量化弱引力透镜、热苏尼亚耶夫-泽尔多维奇效应（tSZ）和X射线勘测的非高斯汇总统计中的约束能力。我们使用能始终包含所有这些可观测物天体物理效应的半解析重子模型，并使用明确纳入各成分间相关性的前景建模方法。我们发现tSZ和X射线场包含有关PNGs的重要信息，还能通过打破参数简并帮助自校准一系列讨厌参数/模型。利用引力透镜、tSZ和X射线场的二阶和三阶矩，相对于仅使用引力透镜，我们发现PNG约束有两倍的改善。纳入更多尺度和其他互补汇总统计时，有望有更大改善。我们的多波长地图制作工具可在该网址找到。本分析中使用的模拟和软件管道是公开可用的。
英文摘要
Primordial non-Gaussianities (PNGs) are a broad class of features in the initial density field that are connected to the particle physics of the early Universe. Measuring the amplitude of these features directly constrains fundamental physics from these earliest epochs and lends insight into energy scales that cannot be probed with terrestrial experiments. Using a new class of simulation methods, we propagate these signatures to their impact on the formation of non-linear structure and quantify the constraining power in non-Gaussian summary statistics of weak lensing, thermal Sunyaev Zeldovich (tSZ), and X-ray surveys. We use semi-analytic baryon models that consistently include astrophysical effects across all these observables, and use foreground modeling approaches that explicitly fold in correlations between the various components. We find that the tSZ and X-ray fields have significant information about PNGs, and additionally can help self-calibrate a broad set of nuisance parameters/models by breaking parameter degeneracies. Using the second and third moments of the lensing, tSZ, and X-ray fields, we find a factor of 2 improvement in PNG constraints relative to using lensing alone. Larger improvements are expected when including more scales and other complementary summary statistics. Our multi-wavelength map maker can be found at https://github.com/DhayaaAnbajagane/Vaanam. The simulations and software pipelines used in this analysis are publicly available.
Comments[v1]: Original submission, [v2]: Updated federal acknowledgements
