A tightly correlated star formation rate–stellar mass relation of star forming galaxies , or star-forming sequence ( SFS ) , is a key feature in galaxy property-space that is predicted by modern galaxy formation models .
We present a flexible data-driven approach for identifying this SFS over a wide range of star formation rates and stellar masses using Gaussian mixture modeling ( GMM ) .
Using this method , we present a consistent comparison of the z { = } 0 SFSs of central galaxies in the Illustris , EAGLE , and Mufasa hydrodynamic simulations and the Santa Cruz semi-analytic model ( SC-SAM ) , alongside data from the Sloan Digital Sky Survey .
We find , surprisingly , that the amplitude of the SFS varies by up to { \sim } 0.7 \mathrm { dex } ( factor of { \sim } 5 ) among the simulations with power-law slopes range from 0.7 to 1.2 .
In addition to the SFS , our GMM method also identifies sub-components in the star formation rate–stellar mass relation corresponding to star-burst , transitioning , and quiescent sub-populations .
The hydrodynamic simulations are similarly dominated by SFS and quiescent sub-populations unlike the SC-SAM , which predicts substantial fractions of transitioning and star-burst galaxies at stellar masses above and below 10 ^ { 10 } M _ { \sun } , respectively .
All of the simulations also produce an abundance of low-mass quiescent central galaxies in apparent tension with observations .
These results illustrate that , even among models that well reproduce many observables of the galaxy population , the z { = } 0 SFS and other sub-populations still show marked differences that can provide strong constraints on galaxy formation models .