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Updated: Jul 2, 2025

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
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Multiscale Flow for robust and optimal cosmological analysis.

Biwei Dai1,2, Uroš Seljak1,2,3

  • 1Berkeley Center for Cosmological Physics and Department of Physics, University of California, Berkeley, CA 94720.

Proceedings of the National Academy of Sciences of the United States of America
|February 21, 2024
PubMed
Summary

Multiscale Flow, a new generative Normalizing Flow, models cosmological data field-level likelihoods. This method outperforms traditional statistics and identifies unknown systematics in weak lensing data.

Keywords:
deep learningfield-level inferencelarge-scale structurenormalizing flowweak lensing

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Area of Science:

  • Cosmology
  • Astrophysics
  • Machine Learning

Background:

  • Cosmological data analysis often relies on summary statistics, which can lose information.
  • Generative models are increasingly used for likelihood estimation in cosmology.
  • Normalizing Flows offer a powerful framework for density estimation.

Purpose of the Study:

  • To introduce Multiscale Flow, a novel generative Normalizing Flow for modeling field-level cosmological data.
  • To enable accurate likelihood analysis and data generation for cosmological surveys.
  • To identify and mitigate scale-dependent systematics in observational data.

Main Methods:

  • Utilizing a hierarchical decomposition of cosmological fields via a wavelet basis.
  • Modeling different wavelet components separately using Normalizing Flows.
  • Summing individual wavelet log-likelihoods to recover the full field log-likelihood.

Main Results:

  • Multiscale Flow significantly outperforms traditional and machine learning-based summary statistics in weak lensing inference.
  • The method successfully identifies unknown distribution shifts, including baryonic effects not present in training data.
  • Demonstrated capability to generate realistic weak lensing data samples.

Conclusions:

  • Multiscale Flow provides an optimal, field-level likelihood analysis without dimensionality reduction.
  • This approach enhances cosmological inference by accurately modeling complex data distributions.
  • The generative capabilities open new avenues for realistic mock data creation.