GalSyn I: A Forward-Modeling Code for Synthetic Galaxy Observations from Hydrodynamical Simulations and First Data Release from IllustrisTNG

Abstract

We present GalSyn (Galaxy Synthesizer), a modular and flexible Python package for generating synthetic observations from hydrodynamical galaxy simulations. GalSyn generates synthetic spectrophotometric data cubes for individual galaxies from simulation cutouts, employing a particle-by-particle spectral modeling approach that enables the rapid production of large synthetic datasets required for statistical population studies, offering a computationally efficient alternative to full radiative transfer codes. Users have full control over the spectral modeling choices, including the stellar population synthesis engine, stellar isochrones, spectral libraries, and initial mass functions. Dust attenuation is modeled at spatially resolved scales using a line-of-sight column-density method, with a comprehensive suite of fixed and adaptive attenuation laws. A decoupled kinematics model independently Doppler-shifts the stellar and nebular components, enabling realistic synthetic integral field unit data cubes. It also provides features to add observational realism, including PSF convolution and noise simulation. Beyond generating imaging and spectroscopic data cubes, GalSyn reconstructs spatially resolved physical property maps and star formation histories (SFHs) of a galaxy. Alongside this paper, we present the first data release of synthetic imaging observations and resolved SFHs generated from the IllustrisTNG simulations. This release includes four mock extragalactic survey fields together with data cubes of the individual galaxies within them, data cubes of 290 local massive galaxies and their progenitors tracked across $0<z<5$, and 259 major-merger systems. Each galaxy data cube contains imaging across 47 filters spanning HST, JWST, Euclid, Rubin/LSST, and the Roman Space Telescope. GalSyn is publicly available at https://github.com/aabdurrouf/GalSyn.