Speaker
Digvijay Wadekar
(Institute for Advanced Study (IAS))
Description
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
