Power of wavelets in analyses of transit and phase curves in the presence of stellar variability and instrumental noise. II. Accuracy of the transit parameters

TL;DR

This study evaluates the accuracy of transit parameter estimation in exoplanet light curves affected by stellar and instrumental noise, demonstrating that wavelet-based methods outperform traditional white-noise assumptions in handling correlated noise.

Contribution

The paper introduces a bootstrapping test for assessing transit fitting algorithms and shows that wavelet-based TLCM provides more reliable parameter estimates under correlated noise conditions.

Findings

Wavelet-based TLCM accurately handles correlated noise.

FITSH's white-noise assumption leads to biased results.

Wavelet method yields consistent error intervals.

Abstract

Context. Correlated noise in exoplanet light curves, such as noise from stellar activity, convection noise, and instrumental noise, distorts the exoplanet transit light curves and leads to biases in the best-fit transit parameters. An optimal fitting algorithm can provide stability against the presence of correlated noises and lead to statistically consistent results, namely, the actual biases are usually within the error interval. This is not automatically satisfied by most of the algorithms in everyday use and the testing of the algorithms is necessary. Aims. In this paper, we describe a bootstrapping-like test to handle with the general case and we apply it to the wavelet-based Transit and Light Curve Modeller (TLCM) algorithm, testing it for the stability against the correlated noise. We compare and contrast the results with regard to the FITSH algorithm, which is based on an assumption of white noise. Methods. We simulated transit light curves with previously known parameters in the presence of a correlated noise model generated by an Autoregressive Integrated Moving Average (ARIMA) process. Then we solved the simulated observations and examined the resulting parameters and error intervals. Results. We have found that the assumption of FITSH, namely, that only white noise is present, has led to inconsistencies in the results: the distribution of best-fit parameters is then broader than the determined error intervals by a factor of 3-6. On the other hand, the wavelet-based TLCM algorithm handles the correlated noise properly, leading to both properly determined parameter and error intervals that are perfectly consistent with the actual biases