Modeling CO Line Luminosity Using FIRE Simulations: I. Methodology and Validation of Molecular Gas and CO Abundance
Year: 2026
Type: preprint
Abstract: We present a post-processing pipeline that predicts CO line luminosities, molecular hydrogen masses, and CO abundances for galaxies in the Feedback in Realistic Environments (FIRE-2) simulations, as a first step toward CO response functions for line intensity mapping mocks. SKIRT gives the radiation field reaching each gas cell. CLOUDY, tabulated on a five-parameter grid (metallicity, density, turbulent velocity, radiation field, cloud scale) and interpolated, gives $L'_{\rm CO(1-0)}$ and the $\rm H_2$ and CO masses. In this first of three papers we validate the pipeline against observations. For Milky Way (MW) molecular clouds, the predicted $L'_{\rm CO(1-0)}$ rises sharply at $A_V\sim1$ and matches the observed values above that column. For a uniform $3\,Z_\odot$ the summed luminosity is within 35\% of the observed total. A factor of three increase in metallicity changes the total $L'_{\rm CO(1-0)}$ by an order of magnitude. Along MW sight lines, the predicted $N_{\rm CO}/N_{\rm H_2}$ reproduces the observed upturn within 0.3 dex in $N_{\rm H_2}$ but falls below the data at higher columns. At $z=0$ our molecular gas masses agree with xCOLD GASS for ${\rm SFR}\gtrsim0.3\,M_\odot\,yr^{-1}$ and are converged across our simulation resolutions. At $z\gtrsim1$ observed gas masses exceed ours at fixed stellar mass, though selection effects and conversion-factor assumptions limit this comparison. CO luminosities are compared with extragalactic observations in Paper II. Capturing the metallicity-dependent dust shielding of CO from photodissociation is essential for modeling CO emission in low-mass and high-redshift galaxies.
Source: arXiv (Cornell University)
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Aeronautics and Space Administration, Canadian Institute for Advanced Research, Government of Ontario, University of Toronto, Simon Fraser University, Alliance de recherche numérique du Canada, Ames Research Center, Natural Sciences and Engineering Research Council of Canada
OpenAlex ID: W7221086448