Extracting non-Gaussian information from the non-linear regime of structure formation is key to fully exploiting the rich data from upcoming cosmological surveys probing the large-scale structure of the universe. However, due to theoretical and computational complexities, this remains one of the main challenges in analyzing observational data. We present a set of summary statistics for cosmological matter fields based on 3D wavelets to tackle this challenge. These statistics are computed as the spatial average of the complex modulus of the 3D wavelet transform raised to a power $q$ and are therefore known as invariant wavelet moments. The 3D wavelets are constructed to be radially band-limited and separable on a spherical polar grid and come in three types: isotropic, oriented, and harmonic. In the Fisher forecast framework, we evaluate the performance of these summary statistics on matter fields from the Quijote suite, where they are shown to reach state-of-the-art parameter constraints on the base $\Lambda$CDM parameters, as well as the sum of neutrino masses. We show that we can improve constraints by a factor 5 to 10 in all parameters with respect to the power spectrum baseline.
翻译:从非线状结构形成体系中提取非加西文的非加西文信息,是充分利用即将到来的宇宙调查所收集的丰富数据以探索宇宙的大规模结构的关键。然而,由于理论和计算的复杂性,这仍然是分析观测数据的主要挑战之一。我们根据3D波子提出了一套基于3D波子的宇宙物质域汇总统计数据,以应对这一挑战。这些统计数据是作为3D波子变换到1美元电源的复杂模数的空间平均数计算的,因此被称为变异波点。3D波点的构建是为了在球状极电网上进行辐射带限制和分离,并分为三种类型:异质、定向和和谐。在Fishercher预测框架中,我们评估Quijote套件中这些物质域汇总统计数据的性能,显示它们能够达到基数 $\Lambda美元参数的状态参数限制,以及中微子质量的总和。我们用5个基准参数来改进所有频谱参数的制约。我们用所有5个基准参数来改进所有频度参数。