pyLIMA : an open source package for microlensing modeling. I. presentation of the software and analysis on single lens models

TL;DR

pyLIMA is an open-source Python software package designed for efficient microlensing event modeling, crucial for upcoming large-scale surveys detecting exoplanets, with initial focus on single-lens models and validation on real and simulated data.

Contribution

This paper introduces pyLIMA, the first comprehensive, publicly available microlensing modeling software written in Python, optimized for large datasets and future complex phenomena.

Findings

pyLIMA effectively models real microlensing events

The software performs well on simulated data

It provides a foundation for future complex modeling

Abstract

Microlensing is a unique tool, capable of detecting the 'cold' planets between 1-10 AU from their host stars, and even unbound 'free-floating' planets. This regime has been poorly sampled to date owing to the limitations of alternative planet-finding methods, but a watershed in discoveries is anticipated in the near future thanks to the planned microlensing surveys of WFIRST-AFTA and Euclid s Extended Mission. Of the many challenges inherent in these missions, the modeling of microlensing events will be of primary importance, yet is often time consuming, complex and perceived as a daunting barrier to participation in the field. The large scale of future survey data products will require thorough but efficient modeling software, but unlike other areas of exoplanet research, microlensing currently lacks a publicly-available, well-documented package to conduct this type of analysis. We present first version 1.0 of pyLIMA: Python Lightcurve Identification and Microlensing Analysis. This software is written in Python and uses existing packages as much as possible, to make it widely accessible. In this paper, we describe the overall architecture of the software and the core modules for modeling single-lens events. To verify the performance of this software, we use it to model both real datasets from events published in the literature and generated test data, produced using pyLIMA s simulation module. Results demonstrate that pyLIMA is an efficient tool for microlensing modeling. We will expand pyLIMA to consider more complex phenomena in the following papers.