popclass: a python package for classifying microlensing events

TL;DR

popclass is a Python package that offers a probabilistic framework for classifying gravitational microlensing events by matching observed signals to Galactic simulations, enabling flexible and uncertainty-aware lens type predictions.

Contribution

It introduces a versatile Python toolkit that integrates Galactic models and inference methods for improved microlensing event classification.

Findings

Provides probabilistic lens classification based on Galactic simulations

Supports flexible constraints and models for microlensing signals

Includes tools for visualization and uncertainty quantification

Abstract

popclass is a python package that provides a flexible, probabilistic framework for classifying the lens of a gravitational microlensing event. popclass allows a user to match characteristics of a microlensing signal to a simulation of the Galaxy to calculate lens type probabilities for an event. Constraints on any microlensing signal characteristics and any Galactic model can be used. popclass comes with an interface to common inference libraries for microlensing signal constraints, pre-loaded Galactic models, plotting functionality, and classification uncertainty quantification methods.