{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/popclass-a-python-package-for-classifying","title":"popclass: a python package for classifying microlensing events","arxiv_id":"2410.14076","date":"2024-10-17","proceeding":null,"authors":["Greg Sallaberry","Zofia Kaczmarek","Peter McGill","Scott E. Perkins","William A. Dawson","Caitlin G. Begbie"],"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.","url_abs":"https://arxiv.org/abs/2410.14076v1","url_pdf":"https://arxiv.org/pdf/2410.14076v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"popclass-a-python-package-for-classifying","repo_url":"https://github.com/llnl/popclass","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}