Inferring Distributions and Correlations of Astrophysical Quantities Derived from Observables

Astronomy and Astrophysics – Astronomy

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Scientific paper

Recent advances in astronomy have made it possible to assemble massive data sets with numerous observables. In addition, both observational and theoretical advances have made it possible to relate observables, such as flux and color, to many derived quantities, such as the stellar mass of a galaxy, its star formation history, or the mass of its central black hole. Modern surveys therefore offer the potential to measure how these derived quantities are distributed, thus providing greater insight into the underlying astrophysical processes governing populations of objects. For example, in order to understand how supermassive black holes grow, or how stellar mass is assembled in galaxies, it is necessary to understand the distribution and evolution of these derived quantities, using the distribution and evolution of a set of observables as a proxy. Unfortunately, understanding the distribution of derived quantities presents new and difficult statistical challenges, as conventional methods developed for observed quantities are not applicable. I will discuss how to constrain the distribution of derived quantities in a manner that self-consistently corrects for statistical uncertainties in the derived quantities, and corrects for incompleteness due to flux selection. I will use the quasar black hole mass function as an example throughout my talk, to both illustrate the failure of traditional statistical methods and the success of correctly relating the distribution of the derived quantities (black hole mass and Eddington ratio in this example) to the observable quantities.

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