Cross-Analyzing Radio and $\gamma$-Ray Time Series Data: Fermi Marries Jansky

TL;DR

This paper presents algorithms for analyzing radio and gamma-ray time series data from active galactic nuclei, enabling characterization of their variability and physical processes through various statistical tools.

Contribution

It introduces a suite of time series analysis algorithms, including Bayesian blocks, for cross-analyzing radio and gamma-ray data with different sampling schemes.

Findings

Algorithms effectively characterize variability in AGN light curves.

Bayesian blocks improve local structure detection in time series.

Methods facilitate multi-wavelength correlation analysis.

Abstract

A key goal of radio and $\gamma-$ray observations of active galactic nuclei is to characterize their time variability in order to elucidate physical processes responsible for the radiation. I describe algorithms for relevant time series analysis tools -- correlation functions, Fourier and wavelet amplitude and phase spectra, structure functions, and time-frequency distributions, all for arbitrary data modes and sampling schemes. For example radio measurements can be cross-analyzed with data streams consisting of time-tagged gamma-ray photons. Underlying these methods is the Bayesian block scheme, useful in its own right to characterize local structure in the light curves, and also prepare raw data for input to the other analysis algorithms. One goal of this presentation is to stimulate discussion of these methods during the workshop.