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Wavelet Moments for Cosmological Parameter Estimation

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arxiv 2204.07646 v1 pith:HH35EH2G submitted 2022-04-15 astro-ph.CO stat.AP

Wavelet Moments for Cosmological Parameter Estimation

classification astro-ph.CO stat.AP
keywords cosmologicalstatisticswaveletconstraintsdatafieldsmattermoments
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Learning Cosmology from Nearest Neighbour Statistics

    astro-ph.CO 2025-11 conditional novelty 6.0

    Nearest-neighbour distance maps, combined with kNN-CDFs in a hybrid neural network, constrain Ωm and σ8 from Quijote halos with R2=0.80 and 0.93, matching or beating point-cloud methods at a fraction of the compute.

  2. Quantifying Weighted Morphological Content of Large-Scale Structures via Simulation-Based Inference

    astro-ph.CO 2025-11 unverdicted novelty 6.0

    Simulation-based inference on Big Sobol Sequence halos at z=0.5 shows CMD+MFs improves σ8 and Ωm precision by ~27% over MFs alone and outperforms PS by ~45% in mass-selected samples at matched scales.