Cosmic phenomena display a broad range of time-dependent behavior. Until recently, resources were sufficiently limited that only selected populations could be extensively monitored in the time domain, but modern telescopes and detectors can survey cosmic volumes much more quickly than before. As a consequence, synoptic time-domain astronomy - the study of cosmic variability across sizeable populations - is becoming a dominant mode of study. Such surveys provide not simply more data, but a different kind of data, requiring a new approach to statistical learning capable of extracting information from large ensembles of multivariate, irregularly and asynchronously sampled light curves. Astronomy is not the only discipline facing this change in the nature of data. Functional data analysis (FDA) is a rapidly growing area of statistics that addresses inference from datasets that sample ensembles of related functions. The present interdisciplinary project will use FDA concepts and techniques to develop new methods to address several problem areas of survey data analysis, from astrophysical modeling of variability using catalog data, to the detection of variable and transient sources in images. One main application will be the analysis of light curves of periodic variable stars using functional mixed effects models. In particular, Cepheid variable stars are a foundation of the cosmic distance scale, and the hope is that these new functional models can provide more accurate brightness estimates and therefore ease limits on how accurately important cosmological parameters can be measured. Other problems well suited to FDA methods include the detection of dim intermittent signals, and the classification of sources from features in their light curves.
The team includes experts in astronomy and in statistics, and will pursue innovative research in both disciplines. This research will enhance their partnership by direct collaboration, by dissemination of results in both communities, and by training a graduate student in information sciences to work on problems in astronomy. The methods produced will improve the science return on the large investments being made in astronomical surveys. The investigators are also involved with the Penn State University's interdisciplinary Summer School in Statistics for Astronomers, helping to train young astronomers in advanced statistics and machine learning methods.
