Swirls of FIRE: Spatially Resolved Gas Velocity Dispersions and Star Formation Rates in FIRE-2 Disk Environments
Matthew E. Orr, Christopher C. Hayward, Anne M. Medling, Philip F., Hopkins, Norman Murray, Jorge L. Pineda, Claude-Andr\'e Faucher-Gigu\`ere,, Du\v{s}an Kere\v{s}, and Kung-Yi Su

TL;DR
This study uses FIRE-2 simulations to analyze the spatially resolved gas velocity dispersion and star formation rates in Milky Way-like galaxy disks, revealing feedback-regulated dynamics and oscillatory star formation cycles.
Contribution
It provides the first detailed simulation-based analysis of the spatially resolved $\sigma$-SFR relation, confirming feedback's dominant role over gravitational turbulence.
Findings
$\sigma$ remains relatively flat across a wide SFR range.
Higher dense gas fractions correlate with higher SFRs at constant $\sigma$.
Simulated star formation cycles oscillate with feedback timescales of 10-100 Myr.
Abstract
We study the spatially resolved (sub-kpc) gas velocity dispersion ()--star formation rate (SFR) relation in the FIRE-2 (Feedback in Realistic Environments) cosmological simulations. We specifically focus on Milky Way mass disk galaxies at late times. In agreement with observations, we find a relatively flat relationship, with km/s in neutral gas across 3 dex in SFRs. We show that higher dense gas fractions (ratios of dense gas to neutral gas) and SFRs are correlated at constant . Similarly, lower gas fractions (ratios of gas to stellar mass) are correlated with higher at constant SFR. The limits of the - relation correspond to the onset of strong outflows. We see evidence of "on-off" cycles of star formation in the simulations, corresponding to feedback injection timescales of 10-100 Myr, where SFRs oscillate…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
