# Vortragsdetails

Mittwoch, 7. November 2012 - 16:15

Rahul Shetty:

"Evidence for a non-universal Kennicutt-Schmidt relationship using hierarchical Bayesian linear regression"

**Zusammenfassung.** I will present a hierarchical Bayesian linear regression method that rigorously treats measurement uncertainties, and accounts for hierarchical data structure. The method simultaneously estimates the intercept, slope, and scatter about the regression line of each individual subject (e.g. a galaxy) and the population (e.g. an ensemble of galaxies). I will compare the hierarchical Bayesian method with traditional least squares methods, and demonstrate that it more accurately recovers the underlying parameters of both the individuals and the population. To investigate the correlation between star formation rate and gas surface density, I have applied the hierarchical Bayesian method to estimate the Kennicutt-Schmidt (KS) parameters of a sample of spiral galaxies compiled by Bigiel et al. (2008). There is significant variation in the KS parameters, indicating that no single KS relationship holds for all galaxies. This suggests that the relationship between molecular gas and star formation differs from galaxy to galaxy, possibly due to the influence of other physical properties within a given galaxy, such as metallicity, molecular gas fraction, stellar mass, and/or magnetic fields. In four of the seven galaxies the slope estimates are well below unity, especially for M51, even at the 2sigma level. We estimate the mean index of the KS relationship for the population to be 0.84, with 2sigma range [0.63, 1.0]. The sub-linear KS relationship estimated for a number of the individual galaxies suggests that CO emission is tracing some molecular gas which is not directly associated with star formation.