Improving Time-Series Analysis using Multitapering: Efficient and precise modeling of stellar oscillations to understand the Milky Way and beyond

Speaker: Aarya Patil (Max Planck Institute for Astronomy) In Person and zoom 

Zoom info: https://stanford.zoom.us/j/98604058568

I am an LSST Discovery Alliance Catalyst Fellow working on Galactic archaeology. In this talk, I will describe the power spectrum analysis method I developed, the multitaper Non-Uniform Fast Fourier Transform (mtNUFFT), which tackles the statistical problems faced by the standard Lomb-Scargle (LS) periodogram.

Asteroseismic time-series data have imprints of stellar oscillation modes, whose detection and characterization through time-series analysis allows us to probe stellar interior physics. Such analyses usually occur in the Fourier domain by computing the Lomb-Scargle (LS) periodogram, an estimator of the power spectrum underlying unevenly-sampled time-series data. However, the LS periodogram suffers from the statistical problems of (1) inconsistency (or noise) and (2) bias due to high spectral leakage. mtNUFFT tackles the inconsistency and bias problems of the LS periodogram, thereby allowing efficient and precise frequency estimates of stellar oscillations. This increase in efficiency has promising implications for Galactic archaeology, in addition to stellar structure and evolution studies. 

My new frequency analysis method is implemented in the open-source Python package, tapify, that can be applied to time-domain astronomy in general, e.g., the LSST survey. I will talk about its application to time-series data of RR Lyrae stars, and its prospects to model the Milky Way galaxy using LSST.