Robust 1-norm periodograms for analysis of noisy non-Gaussian time series with irregular cadences: Application to VLBI astrometry of quasars

TL;DR

The paper introduces a robust 1-norm periodogram method for analyzing irregular, non-Gaussian astronomical time series, demonstrated on VLBI quasar data, improving detection of quasi-periodic signals.

Contribution

It presents a new robust periodogram technique based on 1-norm estimation, suitable for irregular and non-Gaussian data, with application to VLBI quasar measurements.

Findings

Identified 49 quasars with significant quasi-periodic position changes.

Compared the new method with classical periodogram, showing improved robustness.

Applied the technique to 259 VLBI quasars with over 200 measurements each.

Abstract

Astronomical time series often have non-uniform sampling in time, or irregular cadences, with long gaps separating clusters of observations. Some of these data sets are also explicitly non-Gaussian with respect to the expected model fit, or the simple mean. The standard Lomb-Scargle periodogram is based on the least squares solution for a set of test periods and, therefore, is easily corrupted by a subset of statistical outliers or an intrinsically non-Gaussian population. It can produce completely misleading results for heavy-tailed distribution of residuals. We propose a robust 1-norm periodogram technique, which is based on the principles of robust statistical estimation. This technique can be implemented in weighted or unweighted options. The method is described in detail and compared with the classical least squares periodogram on a set of astrometric VLBI measurements of the ICRF quasar IERS B0642+449. It is uniformly applied to a collection of 259 ICRF3 quasars each with more than 200 epoch VLBI measurements, resulting in a list of 49 objects with quasi-periodic position changes above the $3\sigma$ level, which warrant further investigation.