PANCAKE: Python bAsed Numerical Color-magnitude-diagram Analysis pacKagE

TL;DR

PANCAKE is an open-source Python package designed for efficient and accurate analysis of color-magnitude diagrams to infer star formation histories of nearby galaxies, supporting upcoming space telescope data.

Contribution

It introduces a user-friendly, customizable, and validated Python tool for CMD fitting, optimized for new space telescope data and compatible with various isochrone libraries.

Findings

Performs well on mock and observational data

Quantifies uncertainties in SFH recovery

Consistent SFR estimates with FUV photometry

Abstract

Stellar populations serve as a fossil record of galaxy formation and evolution, providing crucial information about the history of star formation and galaxy evolution. The color-magnitude diagram (CMD) stands out as the most accurate tool currently available for inferring the star formation histories (SFHs) of nearby galaxies with stellar-resolved multiband data. The launch of new space telescopes, including JWST, EUCLID, and the upcoming CSST and Roman, will significantly increase the number of stellar-resolved galaxies over the next decade. A user-friendly and customizable CMD fitting package would be valuable for galaxy evolution studies with these data. We develop an open-source Python-based package named \textsc{pancake}, which is fast and accurate in determining SFHs and stellar population parameters in nearby galaxies. We have validated our method via a series of comprehensive tests. First, \textsc{pancake} performs well on mock data, meanwhile the random and systematic uncertainties are quantified. Second, \textsc{pancake} performs well on observational data containing a star cluster and 38 dwarf galaxies (50 fields). Third, the star formation rate (SFR) from \textsc{pancake} is consistent with the SFR from FUV photometry. To ensure compatibility and accuracy, we have included isochrone libraries generated using PARSEC for most of the optical and near-infrared filters used in space telescopes such as HST, JWST, and the upcoming CSST.