November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

How to create powerful machine learning projects in astronomy

Not scheduled
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Poster Paris Posters


Johannes Buchner (Max Planck Institute for extraterrestrial Physics)


Large, freely available, well-maintained data sets have made astronomy a popular playground for machine learning projects. Nevertheless, robust insights gained into both machine learning and physics could be improved by clarity in problem definition and establishing workflows that critically verify, characterize and calibrate machine learning models. We provide a collection of guidelines for setting up machine learning projects to make them likely useful for science, less frustrating and time-intensive for the scientist and their computers, and more likely to lead to robust insights. We draw examples and experience from astronomy, but the advice is potentially applicable to other areas of science. The recommendations have been influenced by projects with students, and discussions at conferences including ML-IAP2021 in Paris.

Primary authors

Johannes Buchner (Max Planck Institute for extraterrestrial Physics) Dr Sotiria Fotopoulou (University of Bristol)

Presentation materials