Estimating microlensing parameters from observables and stellar isochrones with pyLIMASS

TL;DR

pyLIMASS is a new algorithm that estimates microlensing system parameters by integrating observables and stellar isochrones, achieving accurate lens property estimations with minimal bias, especially for future space telescope surveys.

Contribution

The paper introduces pyLIMASS, a novel method combining observables and stellar isochrones using Gaussian mixtures to estimate microlensing parameters, validated on simulated and real events.

Findings

Estimates are within 1-$\sigma$ of published results.

Median lens mass precision of 20% for Roman Space Telescope data.

Effective in predicting lens properties with minimal bias.

Abstract

We present pyLIMASS, a novel algorithm for estimating the physical properties of the lensing system in microlensing events. The main idea of pyLIMASS is to combine all available information regarding the microlensing event, defined as observables, and to estimate the parameter distributions of the system, such as the lens mass and distance. The algorithm is based on isochrones for the stars model and combine the observables using a Gaussian Mixtures approach. After describing the mathematical formalism and its implementation, we discuss the algorithm's performance on simulated and published events. Generally, the pyLIMASS estimations are in good agreement (i.e., within 1-$\sigma$) with the results of the selected published events, making it an effective tool to estimate the lens properties and their distribution. The applicability of the method was tested by using a catalog of realistically simulated events that could be observed by the future Galactic Bulge Time Domain Survey of the Nancy Grace Roman Space Telescope. By solely using constraints from the Roman lightcurves and images, pyLIMASS estimates the masses of the lens of the Roman catalog with a median precision of 20% with almost no bias.