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

Multiview Symbolic Regression in astronomy

Nov 27, 2023, 5:18 PM
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
Flash talk Online Contributed talks


Etienne Russeil (Laboratoire de Physique de Clermont (LPC))


Symbolic Regression is a data-driven method that searches the space of mathematical equations with the goal of finding the best analytical representation of a given dataset. It is a very powerful tool, which enables the emergence of underlying behavior governing the data generation process. Furthermore, in the case of physical equations, obtaining an analytical form adds a layer of interpretability to the answer which might highlight interesting physical properties.
However equations built with traditional symbolic regression approaches are limited to describing one particular event at a time. That is, if a given parametric equation was at the origin of two datasets produced using two sets of parameters, the method would output two particular solutions, with specific parameter values for each event, instead of finding a common parametric equation. In fact there are many real world applications – in particular astrophysics -- where we want to propose a formula for a family of events which may share the same functional shape, but with different numerical parameters
In this work we propose an adaptation of the Symbolic Regression method that is capable of recovering a common parametric equation hidden behind multiple examples generated using different parameter values. We call this approach Multiview Symbolic Regression and we demonstrate how it can reconstruct well known physical equations. Additionally we explore possible applications in the domain of astronomy for light curves modeling. Building equations to describe astrophysical object behaviors can lead to better flux prediction as well as new feature extraction for future machine learning applications.

Primary author

Etienne Russeil (Laboratoire de Physique de Clermont (LPC))


Dr Emille Ishida (Laboratoire de Physique de Clermont (LPC)) Dr Emmanuel Gangler (Laboratoire de Physique de Clermont (LPC)) Dr Fabricio Olivetti de França (Heuristics, Analysis and Learning Laboratory (HAL), Federal University of ABC) Dr Konstantin Malanchev (Department of Astronomy, University of Illinois)

Presentation materials