Model-based clustering in the presence of measurement error

Metro Toronto Convention Centre 

Observations on measurement error are commonly available in many physical and engineering applications, including astronomy. We develop methodology for model-based clustering in the presence of measurement error, and use that to classify gamma ray bursts (GRBs) from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) mission. Our statistical model incorporates the observations on measurement error along with a mixture of Gaussian factor analyzers that can explain the variability in each cluster via a small set of latent factors, and is equipped with a matrix-free computational framework that enables efficient parameter estimation. The proposed method allows for the characterization of different kinds of GRBs in terms of a few underlying variables, and provides a more comprehensive understanding of the spectral characteristics governing the different kinds of GRBs.