2019/11/12 by S. Alwin Mao, Eve C. Ostriker, Chang-Goo Kim
Physics and Astronomy · #Astronomy and Astrophysical Research #Astrophysics and Star Formation Studies #Galaxies: Formation, Evolution, Phenomena #Gravitational collapse #Interstellar cloud #Interstellar medium #Mass fraction #Measure (data warehouse) #Molecular cloud #Star formation #Virial mass #Virial theorem #astro-ph.GA
paper · pdf · doi:10.3847/1538-4357/ab989c
31 pages, 17 figures, submitted to ApJ
arxiv created 2019/11/12 · openalex created_date 2019/11/22 · openalex publication_date 2020/07/01 · arxiv updated 2020/07/29 · openalex updated_date 2026/08/06
Abstract We apply gravity- and density-based methods to identify clouds in self-consistent numerical simulations of the star-forming, multiphase interstellar medium (ISM) and compare their properties and global correlation with the star formation rate (SFR) over time. The gravity-based method identifies bound objects, which have masses at densities , and virial parameters α v ∼ 0.5–5. For clouds defined by a density threshold , the average virial parameter decreases, and the fraction of material that is genuinely bound increases, with increasing . Surprisingly, clouds defined by density thresholds can be unbound even when α v < 2, and high-mass clouds ( ) are generally unbound. This suggests that the traditional α v is at best an approximate measure of boundedness in the ISM. All clouds have internal turbulent motions increasing with size as , similar to observed relations. Bound structures comprise a small fraction of the total simulation mass and have a star formation efficiency per freefall time ∼ 0.4. For , ∼ 0.03–0.3, increasing with density threshold. A temporal correlation analysis between and aggregate mass at varying shows that time delays to star formation are . The correlation between and systematically tightens at higher . Considering moderate-density gas, selecting against high virial parameter clouds improves correlation with the SFR, consistent with previous work. Even at high , the temporal dispersion in is ∼50%, due to the large-amplitude variations and inherent stochasticity of the system.