Episode 39 – data-driven stellar population studies, multi-scale AGN feedback, and solar flare ribbons

In this episode, Payel and Nicole discuss machine learning methods to to transform composite galaxy spectra and determine key features of galaxy evolution. They also examine new observations of the Centarus cluster with JWST, closing the loop on AGN feedback across multiple radial scales. Finally, they discuss new solar observations that shed new light on the formation of solar flare ribbons.

A novel data-driven approach to extract stellar population properties from galaxy spectra using absorption indices – Zahra Sharbaf et al.

JWST reveals how black holes are fed: kiloparsec-scale multiphase filaments feed sub-kiloparsec circumnuclear disks - Julie Hlavacek-Larrondo et al.

pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches - Benedict Van den Bussche et al.

Solar flare ribbons structured by uncombed chromospheric loops - Lakshmi Chitta et al.