Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend

TL;DR

This paper introduces a Bayesian Generalised Lomb-Scargle Periodogram with Trend (BGLST) for more accurate period estimation in unevenly sampled astronomical data, especially when trends are present, outperforming traditional methods.

Contribution

The paper develops and validates a new probabilistic method, BGLST, that effectively handles trends and noise variations in period estimation from astronomical time series data.

Findings

BGLST outperforms traditional methods in synthetic tests.

Handling trends directly improves period detection accuracy.

Noise model choice depends on data sampling and noise characteristics.

Abstract

Period estimation is one of the central topics in astronomical time series analysis, where data is often unevenly sampled. Especially challenging are studies of stellar magnetic cycles, as there the periods looked for are of the order of the same length than the datasets themselves. The datasets often contain trends, the origin of which is either a real long-term cycle or an instrumental effect, but these effects cannot be reliably separated, while they can lead to erroneous period determinations if not properly handled. In this study we aim at developing a method that can handle the trends properly, and by performing extensive set of testing, we show that this is the optimal procedure when contrasted with methods that do not include the trend directly to the model. The effect of the form of the noise (whether constant or heteroscedastic) on the results is also investigated. We introduce a Bayesian Generalised Lomb-Scargle Periodogram with Trend (BGLST), which is a probabilistic linear regression model using Gaussian priors for the coefficients and uniform prior for the frequency parameter. We show, using synthetic data, that when there is no prior information on whether and to what extent the true model of the data contains a linear trend, the introduced BGLST method is preferable to the methods which either detrend the data or leave the data untrended before fitting the periodic model. Whether to use noise with different than constant variance in the model depends on the density of the data sampling as well as on the true noise type of the process.