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

1 Pith paper cite this work, alongside 1 external citations. Polarity classification is still indexing.

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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.

fields

astro-ph.IM 1

years

2026 1

verdicts

CONDITIONAL 1

representative citing papers

Microlensing Detection and Inference via Learned Bayes Factors

astro-ph.IM · 2026-07-22 · conditional · novelty 6.0

A unified transformer-based pipeline detects 99.9% of recoverable simulated microlensing events and outperforms literature hard cuts in the short-duration finite-source regime with amortized neural posterior inference.

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Showing 1 of 1 citing paper.

  • Microlensing Detection and Inference via Learned Bayes Factors astro-ph.IM · 2026-07-22 · conditional · none · ref 14 · internal anchor

    A unified transformer-based pipeline detects 99.9% of recoverable simulated microlensing events and outperforms literature hard cuts in the short-duration finite-source regime with amortized neural posterior inference.