Department of Statistics Seminar
North Carolina State University


David Jones and Hyungsuk Tak


Two Topics in Astrostatistics


 We present novel approaches to two statistical problems in modern astronomy.

(1) David Jones -- Improving Exoplanet Detection Power: Multivariate Gaussian Process Models for Stellar Activity

The radial velocity method is one of the most successful techniques for detecting exoplanets. It works by detecting the velocity of a host star induced by the gravitational effect of an orbiting planet, specifically the velocity along our line of sight, which is called the radial velocity of the star. Unfortunately, radial velocity signals are typically contaminated by various "stellar activity" phenomena, such as dark spots on the star surface. This signal contamination makes it difficult to reliably detect low mass planets and planets orbiting magnetically active stars. A principled approach to recovering planet radial velocity signals in the presence of stellar activity was proposed by Rajpaul et al. (2015) and involves the use of a multivariate Gaussian process model to jointly capture time series of the apparent radial velocity and multiple indicators of stellar activity. We build on this work in two ways: (i) we propose using dimension reduction techniques to construct more informative stellar activity indicators that make use of a larger portion of the stellar spectrum; (ii) we extend the Rajpaul et al. (2015) model to a larger class of models and use a model comparison procedure to select the best model for the particular stellar activity indicators at hand. A novel aspect of the Rajpaul et al. (2015) model and our larger class of models is that they use both a Gaussian process and its derivatives, which imposes scientifically motivated structure. By combining our high-information stellar activity indicators, Gaussian process models, and model selection procedure, we achieve substantially improved planet detection power compared to previous state-of-the-art approaches. 

(2) Hyungsuk Tak -- A Repelling-Attracting Metropolis Algorithm for Astronomical Time Delay Estimation

Astronomical time delay estimation problems often suffer from multimodality. To handle this, we propose the repelling-attracting Metropolis (RAM) algorithm that maintains the simple-to-implement nature of the Metropolis algorithm, but is more likely to jump between modes. The RAM algorithm is a Metropolis-Hastings algorithm with a proposal that consists of a downhill move in density that aims to make local modes repelling, followed by an uphill move in density that aims to make local modes attracting. This down-up movement in density increases the probability of a proposed move to a different mode. Because the acceptance probability of the proposal involves a ratio of intractable integrals, we introduce an auxiliary variable which creates a term in the acceptance probability that cancels with the intractable ratio. Using several examples, we demonstrate the potential for the RAM algorithm to explore a multimodal distribution more efficiently than a Metropolis algorithm and with less tuning than is commonly required by tempering-based methods.

Friday, 1 December 2017
10:30-11:30 am
G 20 Kamphoefner  Hall

Refreshments will be served in the 5th floor commons at 10:00 am