Abstract
We investigate star-forming scaling relations using Bayesian inference on a comprehensive data sample of low-(z < 0.1) and high-redshift (1 < z < 5) star-forming regions. This full data set spans a wide range of host galaxy stellar mass (M-*similar to 10(6)-10(11) M-circle dot) and clump star formation rates (SFR similar to 10(-5)-10(2)M(circle dot)yr(-1)). We fit the power-law relationship between the size (r(H alpha)) and luminosity (L-H alpha) of the star-forming clumps using the Bayesian statistical modeling tool Stan, which makes use of Markov Chain Monte Carlo (MCMC) sampling techniques. Trends in the scaling relationship are explored for the full sample and subsets based on redshift and selection effects between samples. In our investigation, we find neither evidence of redshift evolution of the size-luminosity scaling relationship nor a difference in slope between lensed and unlensed data. There is evidence of a break in the scaling relationship between high and low SFR surface density (Sigma(SFR)) clumps. The size-luminosity power-law fit results are L-H alpha similar to r(H alpha)(2.8) and L-H alpha similar to r(H alpha)(1.7) for low and high Sigma(SFR) clumps, respectively. We present a model where star-forming clumps form at locations of gravitational instability and produce an ionized region represented by the Stromgren radius. A radius smaller than the scale height of the disk results in a scaling relationship of L proportional to r(3)(high Sigma(SFR) clumps), and a scaling of L proportional to r(2) (low Sigma(SFR) clumps) if the radius is larger than the disk scale height.