Object Classification from JWST Catalogs

Abstract

JWST's exquisite data have opened the doors to new possibilities in detecting broad classes of astronomical objects, but also to new challenges in classifying those objects. In this work, we introduce SESHAT, the Stellar Evolutionary Stage Heuristic Assessment Tool for the identification of Young Stellar Objects, field stars (main sequence through asymptotic giant branch), brown dwarfs, white dwarfs, and galaxies, from any JWST observation. This identification is done using the machine learning method XGBoost to analyze thousands of rows of synthetic photometry, modified at run-time to match the filters available in the data to be classified. We validate this tool on real data of both star-forming regions and cosmological fields, and find we are able to reproduce the observed classes of objects to a minimum of 80\% recall across every class, without additional information on the ellipticity or spatial distribution of the objects. Furthermore, this tool can be used to test the filter choices for JWST proposals, to verify whether the chosen filters are sufficient to identify the desired class of objects. SESHAT is released as a Python package to the community for general use.