A Bayesian algorithm for model selection applied to caustic-crossing binary-lens microlensing events

TL;DR

This paper introduces a Bayesian algorithm for automated parameter space exploration in caustic-crossing binary-lens microlensing events, improving model selection by integrating priors and Bayesian criteria.

Contribution

The work develops a Bayesian framework with Monte Carlo Markov Chains for efficient, automated microlensing model selection, incorporating priors from Galactic models and geometrical considerations.

Findings

Successfully recovers input parameters from synthetic data.

Favours a binary star model over a planetary model for OGLE-2007-BLG-472.

Demonstrates improved model plausibility using Bayesian information criteria.

Abstract

We present a full Bayesian algorithm designed to perform automated searches of the parameter space of caustic-crossing binary-lens microlensing events. This builds on previous work implementing priors derived from Galactic models and geometrical considerations. The geometrical structure of the priors divides the parameter space into well-defined boxes that we explore with multiple Monte Carlo Markov Chains. We outline our Bayesian framework and test our automated search scheme using two data sets: a synthetic lightcurve, and the observations of OGLE-2007-BLG-472 that we analysed in previous work. For the synthetic data, we recover the input parameters. For OGLE-2007-BLG-472 we find that while \chi^2 is minimised for a planetary mass-ratio model with extremely long timescale, the introduction of priors and minimisation of BIC, rather than \chi^2, favours a more plausible lens model, a binary star with components of 0.78 and 0.11 M_Sun at a distance of 6.3 kpc, compared to our previous result of 1.50 and 0.12 M_Sun at a distance of 1 kpc.