| Abstract |
We use new HCN(1-0) data from the ALMOND (ACA Large-sample Mapping Of Nearby galaxies in Dense gas) survey to trace the kpc-scale molecular gas density structure and CO(2-1) data from PHANGS-ALMA to trace the bulk molecular gas across 25 nearby, star-forming galaxies. At 2.1 kpc scale, we measure the density-sensitive HCN/CO line ratio and the SFR/HCN ratio to trace the star formation efficiency in the denser molecular medium. At 150 pc scale, we measure structural and dynamical properties of the molecular gas via CO(2-1) line emission, which is linked to the lower resolution data using an intensity-weighted averaging method. We find positive correlations (negative) of HCN/CO (SFR/HCN) with the surface density, the velocity dispersion and the internal turbulent pressure of the molecular gas. These observed correlations agree with expected trends from turbulent models of star formation, which consider a single free-fall time gravitational collapse. Our results show that the kpc-scale HCN/CO line ratio is a powerful tool to trace the 150 pc scale average density distribution of the molecular clouds. Lastly, we find systematic variations of the SFR/HCN ratio with cloud-scale molecular gas properties, which are incompatible with a universal star formation efficiency. Overall, these findings show that mean molecular gas density, molecular cloud properties and star formation are closely linked in a coherent way, and observations of density-sensitive molecular gas tracers are a useful tool to analyse these variations, linking molecular gas physics to stellar output across galaxy discs. |
| Authors |
Lukáš Neumann  , Molly Gallagher , Frank Bigiel , Adam K. Leroy , Ashley T. Barnes , A. Usero , Jakob S. den Brok , Francesco Belfiore , Ivana Bešlić , Yixian Cao , Mélanie Chevance , Daniel A. Dale  , Cosima Eibensteiner , Simon C. O. Glover , Kathryn Grasha , Jonathan D. Henshaw , María J. Jiménez-Donaire , Ralf S. Klessen , J. M. Diederik Kruijssen , Daizhong Liu , Sharon E. Meidt , J. Pety , Johannes Puschnig , Miguel Querejeta , Erik Rosolowsky , E. Schinnerer , Andreas Schruba , Mattia C. Sormani , Jiayi Sun , Yu-Hsuan Teng , Thomas G. Williams
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