galsyn 0.1.0

A Python package for generating astrophysical images of galaxies from hydrodynamical simulations.

GalSyn

GalSyn is a modular Python package designed for generating realistic synthetic spectrophotometric observations of galaxies from hydrodynamical simulation data. By employing particle-by-particle spectral modeling to 3D data from hydrodynamical simulation such as IllustrisTNG and EAGLE, GalSyn enables the generation of realistic synthetic spectrophotometric data cubes, including broadband imaging and Integral Field Unit (IFU) spectroscopy. Beyond light synthesis, the tool produces comprehensive 2D physical property maps of the stellar populations, gas, and dust, as well as the decoupled kinematics of both stellar and gaseous components.

A core philosophy of GalSyn is providing extensive flexibility over the physical ingredients involved in the synthesis procedure. This includes highly flexible control over the stellar population synthesis (SPS) modeling, and customize underlying components such as Initial Mass Functions (IMFs), stellar isochrones (e.g., MIST, Padova, BaSTI), stellar spectral libraries (e.g., MILES, BaSeL), and binary stellar evolution (BPASS). Furthermore, GalSyn implements highly flexible analytical dust attenuation models, allowing users to choose between fixed empirical laws or dynamic prescriptions with variable UV bump strengths and power-law slopes.

While traditional radiative transfer codes offer high physical rigor, they are often computationally intensive and offer limited flexibility regarding stellar population choices. GalSyn is built for computational efficiency and highly flexible user control, allowing for large-scale population studies and systematic exploration of how different physical assumptions (like IMF or dust laws) impact emergent galaxy light.

Check out the sections below to learn more about GalSyn's capabilities, how to use it, and its API. For more detailed information about the physical ingredients and algorithms, please see Abdurro'uf et al. (2026, in prep.).