We apply gravity-based and density-based methods to identify clouds in numerical simulations of the star-forming , three-phase interstellar medium ( ISM ) , and compare their properties and their global correlation with the star formation rate over time .
The gravity-based method identifies bound objects , which have masses M \sim 10 ^ { 3 } -10 ^ { 4 } M _ { \odot } at densities n _ { \mathrm { H } } \sim 100 { cm } ^ { -3 } , and traditional virial parameters \alpha _ { v } \sim 0.5 - 5 .
For clouds defined by a density threshold n _ { H,min } , the average virial parameter decreases , and the fraction of material that is genuinely bound increases , at higher n _ { H,min } .
Surprisingly , these clouds can be unbound even when \alpha _ { v } < 2 , and high mass clouds ( 10 ^ { 4 } -10 ^ { 6 } M _ { \odot } ) are generally unbound .
This suggests that the traditional \alpha _ { v } is at best an approximate measure of boundedness in the ISM .
All clouds have internal turbulent motions increasing with size as \sigma \sim 1 { km } { s } ^ { -1 } ( R / { pc } ) ^ { 1 / 2 } , similar to observed relations .
Bound structures comprise a small fraction of the total simulation mass , with star formation efficiency per free-fall time \epsilon _ { \mathrm { ff } } \sim 0.4 .
For n _ { H,min } = 10 - 100 { cm } ^ { -3 } , \epsilon _ { \mathrm { ff } } \sim 0.03 - 0.3 , increasing with density .
Temporal correlation analysis between \mathrm { SFR } ( t ) and aggregate mass M ( n _ { H,min } { } ;t ) at varying n _ { H,min } shows that time delays to star formation are t _ { \mathrm { delay } } \sim t _ { \mathrm { ff } } ( n _ { H,min } ) .
Correlation between \mathrm { SFR } ( t ) and M ( n _ { H,min } ;t ) systematically tightens at higher n _ { H,min } .
Considering moderate-density gas , selecting against high virial parameter clouds improves correlation with SFR , consistent with previous work .
Even at high n _ { H,min } , the temporal dispersion in ( \mathrm { SFR } - \epsilon _ { \mathrm { ff } } M / t _ { \mathrm { ff } } ) / \langle \mathrm { SFR } \rangle is \sim 50 \% , due to the large-amplitude variations and inherent stochasticity of the system .