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

Improving astrophysical scaling relations with machine learning

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 Online Posters


Digvijay Wadekar (Institute for Advanced Study (IAS))


Finding low-scatter relationships in properties of astrophysical systems is important to estimate their masses/distances. I will show how interpretable ML tools like symbolic regression can be used to expeditiously search for these low-scatter relations in abstract high-dimensional astrophysical datasets. I will present new scaling relations between properties of galaxy clusters that we obtained using ML. I will also highlight advantages of using interpretable ML tools instead of deep neural networks for particular astrophysical problems.

Primary authors

Digvijay Wadekar (Institute for Advanced Study (IAS)) Francisco Villaescusa-Navarro (Flatiron Institute) Leander Thiele (Princeton University) Miles Cranmer (Cambridge University) Shirley Ho


Presentation materials