Bayesian analysis of caustic-crossing microlensing events

TL;DR

This paper enhances Bayesian modeling techniques for caustic-crossing microlensing events, improving the efficiency and physical plausibility of binary-lens event analysis, which is crucial for exoplanet detection.

Contribution

It introduces a method to incorporate Bayesian priors into Cassan's parameter framework, enabling faster and more physically consistent microlensing model fitting.

Findings

Bayesian priors reduce fitting time by excluding implausible models.

The method improves discrimination between competing models.

Implementation in MCMC algorithms enhances robustness of analysis.

Abstract

Aims: Caustic-crossing binary-lens microlensing events are important anomalous events because they are capable of detecting an extrasolar planet companion orbiting the lens star. Fast and robust modelling methods are thus of prime interest in helping to decide whether a planet is detected by an event. Cassan (2008) introduced a new set of parameters to model binary-lens events, which are closely related to properties of the light curve. In this work, we explain how Bayesian priors can be added to this framework, and investigate on interesting options. Methods: We develop a mathematical formulation that allows us to compute analytically the priors on the new parameters, given some previous knowledge about other physical quantities. We explicitly compute the priors for a number of interesting cases, and show how this can be implemented in a fully Bayesian, Markov chain Monte Carlo algorithm. Results: Using Bayesian priors can accelerate microlens fitting codes by reducing the time spent considering physically implausible models, and helps us to discriminate between alternative models based on the physical plausibility of their parameters.