Paper detail

Understanding better (some) astronomical data using Bayesian methods

Current analysis of astronomical data are confronted with the daunting task of modeling the awkward features of astronomical data, among which heteroscedastic (point-dependent) errors, intrinsic scatter, non-ignorable data collection (selection effects), data structure, non-uniform populations (often called Malmquist bias), non-Gaussian data, and upper/lower limits. This chapter shows, by examples, how modeling all these features using Bayesian methods. In short, one just need to formalize, using maths, the logical link between the involved quantities, how the data arise and what we already known on the quantities we want to study. The posterior probability distribution summarizes what we known on the studied quantities after the data, and we should not be afraid about their actual numerical computation, because it is left to (special) Monte Carlo programs such as JAGS. As examples, we show how to predict the mass of a new object disposing of a calibrating sample, how to constraint cosmological parameters from supernovae data and how to check if the fitted data are in tension with the adopted fitting model. Examples are given with their coding. These examples can be easily used as template for completely different analysis, on totally unrelated astronomical objects, requiring to model the same awkward data features.