Physics of Eclipsing Binaries. V. General Framework for Solving the Inverse Problem

TL;DR

This paper introduces a new framework in PHOEBE 2.3 for efficiently solving the inverse problem in eclipsing binary star systems by integrating multiple algorithms and models, enhancing parameter estimation and uncertainty analysis.

Contribution

The paper presents a general framework in PHOEBE 2.3 that supports multiple algorithms and models for inverse problem solving in eclipsing binaries.

Findings

Framework supports multiple algorithms for parameter estimation.

Enables robust and efficient inverse modeling of eclipsing binaries.

Increases flexibility and reliability of stellar system analysis.

Abstract

PHOEBE 2 is a Python package for modeling the observables of eclipsing star systems, but until now has focused entirely on the forward-model -- that is, generating a synthetic model given fixed values of a large number of parameters describing the system and the observations. The inverse problem, obtaining orbital and stellar parameters given observational data, is more complicated and computationally expensive as it requires generating a large set of forward-models to determine which set of parameters and uncertainties best represent the available observational data. The process of determining the best solution and also of obtaining reliable and robust uncertainties on those parameters often requires the use of multiple algorithms, including both optimizers and samplers. Furthermore, the forward-model of PHOEBE has been designed to be as physically robust as possible, but is computationally expensive compared to other codes. It is useful, therefore, to use whichever code is most efficient given the reasonable assumptions for a specific system, but learning the intricacies of multiple codes presents a barrier to doing this in practice. Here we present the 2.3 release of PHOEBE (publicly available from http://phoebe-project.org) which introduces a general framework for defining and handling distributions on parameters, and utilizing multiple different estimation, optimization, and sampling algorithms. The presented framework supports multiple forward-models, including the robust model built into PHOEBE itself.