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We find ourselves on the brink of an exciting era in observationalastrophysics, driven by groundbreaking facilities like JWST, Euclid, Rubin,Roman, SKA, or ELT. Simultaneously, computational astrophysics has shownsignificant strides, yielding highly realistic galaxy formation simulations,thanks to both hardware and software enhancements. Bridging the gap betweensimulations and observations has become paramount for meaningful comparisons.We introduce py-ananke, a Python pipeline designed to generate syntheticresolved stellar surveys from cosmological simulations, adaptable to variousinstruments. Building upon its predecessor, ananke by Sanderson et al. 2020(arXiv:1806.10564), which produced Gaia DR2 mock star surveys, the py-anankepackage offers a user-friendly "plug & play" experience. The pipeline employscutting-edge phase-space density estimation and initial mass function samplingto convert particle data into synthetic stars, while interpolating pre-computedstellar isochrone tracks for photometry. Additionally, it includes modules forestimating interstellar reddening, dust-induced extinctions, and forquantifying errors through dedicated modeling approaches. py-ananke promises toserve as a vital bridge between computational astrophysics and observationalastronomy, facilitating preparations and making scientific predictions for thenext generation of telescopes.

Generating synthetic star catalogs from simulated data for next-gen observatories with py-ananke