English

The HyLight model for hydrogen emission lines in simulated nebulae

Astrophysics of Galaxies 2026-04-08 v2 Instrumentation and Methods for Astrophysics

Abstract

Hydrogen recombination lines provide key diagnostics of ionized gas in galaxies, yet most hydrodynamical simulations estimate hydrogen level populations using interpolated emissivity tables rather than computing them directly from local physical conditions. We present HyLight, a Python-based atomic model that calculates hydrogen level populations and line emissivities from the gas density, temperature, and ionization state, enabling accurate predictions in both equilibrium and non-equilibrium environments. Benchmark comparisons show that HyLight reproduces Cloudy predictions for Balmer, Paschen, and Brackett emissivities to within 1 per cent under typical photoionized nebular conditions, while discrepancies of several tens of per cent arise relative to other published calculations. As an illustrative application, we use HyLight to compute photoionization-to-line intensity ratios in an HII nebula and generate synthetic hydrogen emission maps from a radiation-hydrodynamical simulation that includes non-equilibrium thermochemistry. Combining physical consistency with flexibility, HyLight provides a robust framework for connecting hydrodynamical simulations with observational diagnostics of photoionized regions, and enhances our ability to interpret hydrogen emission in complex, non-equilibrium astrophysical environments.

Keywords

Cite

@article{arxiv.2509.20361,
  title  = {The HyLight model for hydrogen emission lines in simulated nebulae},
  author = {Yuankang Liu and Tom Theuns and Tsang Keung Chan and Alexander J. Richings and Anna F. McLeod},
  journal= {arXiv preprint arXiv:2509.20361},
  year   = {2026}
}

Comments

30 pages, 28 figures. Accepted for publication in MNRAS. The HyLight package is available at https://github.com/YuankangLiu/HyLight . It can also be accessed via PyPI at https://pypi.org/project/hylightpy/ . The input scripts for the Cloudy models used in this paper can be accessed at https://doi.org/10.5281/zenodo.16911175