MSPA - AST: Sparse Representation and Efficient Inference for Astronomical Spectra

点击翻译按钮获取中文摘要

Recent astronomical surveys have built catalogs of hundreds of millions of objects, a volume of data that was inconceivable as recently as a decade ago. Among these data are high-resolution galaxy spectra that offer an unprecedented opportunity for elucidating the structure and evolution of galaxies. New statistical techniques are needed to harness the full diagnostic power of these high-quality data. This project focuses on three interrelated aims, each pointed towards the goal of extracting useful information from spectra, while attaching meaningful measures of uncertainty to results. First, the investigators study efficient methods for extracting and identifying features in galaxy spectra that are essential for subsequent inference tasks, such as manifold learning, classification, and regression. Second, the investigators create a non-linear manifold representation of these data that reflects the intrinsic geometry and natural variations in the entire data ensemble. Third, the investigators probe the relationship between astronomical spectra and key physical/structural properties. The representations achieved via the methods of the first two goals are crucial, as one needs to discard uninformative dimensions in spectra and keep a small number of physically meaningful ones for efficient inference and prediction.The current rate of data collection in astrophysics far outstrips the rate at which scientists can analyze the data. The new statistical tools that the investigators develop allow a rapid computational analysis of millions of galaxy spectra. The extraction of information is guided by the inherent structure of the data as well as the particular questions being asked. Finally, while the investigators focus on galaxy spectra, their methods are directly applicable to the analysis of other highly complex databases; for example, hyperspectral remote sensing image data, spectroscopy data for medical research, and human genetic data. The research will advance statistical theory and provide new methodologies broadly applicable to a range of scientific disciplines.