Kyle Boone



My research focuses on developing novel statistical methods for astronomy and cosmology. I am particularly interested in using Type Ia supernovae to probe the accelerated expansion of the universe that we believe is due to some form of “dark energy”.

One aspect of my research focuses on identifying Type Ia supernovae among the millions of astronomical transients that upcoming astronomical surveys such as the Large Synoptic Survey Telescope (LSST) will discover. I am currently developing the “avocado” software package that uses machine learning to classify different kinds of astronomical transients from sparsely sampled light curves with heteroskedastic noise. I am interested in understanding how biases in our observational strategies affect the performance of photometric classifiers and lead to biases in our cosmological measurements.

I am also working on developing better methods to estimate the distances to Type Ia supernovae. This involves using manifold learning techniques to parametrize the diversity of Type Ia supernovae and building non-linear models of their light curves.