In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

An array of large observational programs using ground-based and space-borne telescopes
is planned in the next decade. The forthcoming wide-field sky surveys are expected to …

We present a comparison of simulation-based inference to full, field-based analytical
inference in cosmological data analysis. To do so, we explore parameter inference for two …

We present F or SE (Foreground Scale Extender), a novel Python package that aims to
overcome the current limitations in the simulation of diffuse Galactic radiation, in the context …

Scattering transforms have been successfully used to describe dust polarisation for flat-sky
images. This paper expands this framework to noisy observations on the sphere with the aim …

Dust emission is the main foreground for cosmic microwave background polarization. Its
statistical characterization must be derived from the analysis of observational data because …

Context. Component separation is the process with which emission sources in astrophysical
maps are generally extracted by taking multi-frequency information into account. It is crucial …

The cosmic microwave background (CMB), carrying the inhomogeneous information of the
very early universe, is of great significance for understanding the origin and evolution of our …

Emission from the interstellar medium can be a significant contaminant of measurements of
the intensity and polarization of the cosmic microwave background (CMB). For planning …

Methods based on machine learning have recently made substantial inroads in many
corners of cosmology. Through this process, new computational tools, new perspectives on …