MicroLIA detects gravitational microlensing events in wide-field time-series surveys using a machine-learning classification engine. It can simulate point-source point-lens (PSPL) light curves with adaptive cadence or ingest user light-curve collections to build training sets, compute an extensive set of time-series features (including derivative-space metrics and error-weighted statistics), and perform feature selection. The package includes data-imputation methods, Bayesian optimization of classifier hyperparameters, and utilities to save, load, and visualize models and performance. MicroLIA outputs candidate events intended for further vetting by modeling tools such as pyLIMA (ascl:1906.022).

pyLIMA (python Lightcurve Identification and Microlensing Analysis) fits microlensing lightcurves and derives the physical quantities of lens systems. The package provides microlensing modeling, and the magnification estimation for high cadence lightcurves has been optimized. pyLIMA is designed to make microlensing modeling and event simulation widely available to the community.