Robust Cross-correlation-based Measurement of Clump Sizes in Galaxies
Stars form in molecular complexes that are visible as giant clouds ($\sim 10^{5-6} \mathrm{M}_\odot$) in nearby galaxies and as giant clumps ($\sim 10^{8-9}\mathrm{M}_\odot$) in galaxies at redshifts $z\approx1$$-$$3$. Theoretical inferences on the origin and evolution of these complexes often require robust measurements of their characteristic size, which is hard to measure at limited resolution and often ill-defined due to overlap and quasi-fractal substructure. We show that maximum and luminosity-weighted sizes of clumps seen in star formation maps (e.g.\ H$α$) can be recovered statistically using the two-point correlation function (2PCF), if an approximate stellar surface density map is taken as the normalizing random field. After clarifying the link between Gaussian clumps and the 2PCF analytically, we design a method for measuring the diameters of Gaussian clumps with realistic quasi-fractal substructure. This method is tested using mock images of clumpy disk galaxies at different spatial resolutions and perturbed by Gaussian white noise. We find that the 2PCF can recover the input clump scale at $\sim20\%$ accuracy, as long as this scale is larger than the spatial resolution. We apply this method to the local spiral galaxy NGC 5194, as well as to three clumpy turbulent galaxies from the DYNAMO-HST sample. In both cases, our statistical H$α$-clump size measurements agree with previous measurements and with the estimated Jeans lengths. However, the new measurements are free from subjective choices when fitting individual clumps.