Time-Series Modeling of High-Resolution Radio Spectra

Abstract: We present a modeling technique to characterize high-resolution radio spectra based on ARIMA (AutoRegressive Integrated Moving Average) modeling from statistical time series analysis. ARIMA isolates the dependence of a spectrum's shape upon both its signal and structured noise components, making fewer assumptions about a spectrum's velocity structure than standard Gaussian component fitting, and is intended to serve as a complement to the latter. Structural dependence modeling can: improve summary moment calculations, provide alternative approaches to signal noise estimation (which can be modeled channel-wise if desired), and help characterize the provenance of any observed structure in a cube's spectra (as signal, structured noise, or white noise). ARIMA modeling is computationally lightweight, backed by statistical theory and, as a first step to an analytical pipeline, can inform further downstream tasks such as identifying when Gaussian component fitting is appropriate.

Speaker Bio: Josh Taylor is a Research Associate in the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin. His research advances unsupervised machine learning methods for use in unbiased, data-driven exploratory, summary, and inferential analysis of astrophysical data; in particular, of data arising from observations and simulations of the star formation process. Dr. Taylor received his PhD in statistics from Rice University.