A new approach to observational cosmology using the scattering transform

Sihao Cheng, Yuan Sen Ting, Brice Menard, Joan Bruna

Research output: Contribution to journalArticlepeer-review


Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with convolutional neural networks (CNNs) but requiring neither training nor tuning. It generates a compact set of coefficients, which can be used as robust summary statistics for non-Gaussian information. It is especially suited for fields presenting localized structures and hierarchical clustering, such as the cosmological density field. To demonstrate its power, we apply this estimator to a cosmological parameter inference problem in the context of weak lensing. On simulated convergence maps with realistic noise, the scattering transform outperforms classic estimators and is on a par with the state-of-the-art CNN. It retains advantages of traditional statistical descriptors, has provable stability properties, allows to check for systematics, and importantly, the scattering coefficients are interpretable. It is a powerful and attractive estimator for observational cosmology and the study of physical fields in general.

Original languageEnglish (US)
Pages (from-to)5902-5914
Number of pages13
JournalMonthly Notices of the Royal Astronomical Society
Issue number4
StatePublished - 2020


  • Cosmological parameters
  • Gravitational lensing: Weak
  • Large-scale structure of Universe
  • Methods: Statistical

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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