A new wavelet-based approach for the automated treatment of large sets of lunar occultation data

TL;DR

This paper presents an automated wavelet-based pipeline for processing large sets of lunar occultation data, enabling efficient extraction, analysis, and identification of resolved or binary sources from infrared array observations.

Contribution

The authors introduce a novel wavelet-based automated reduction pipeline specifically designed for array data in lunar occultation studies, improving efficiency and robustness.

Findings

Successfully processed large occultation datasets at Calar Alto Observatory.

Enabled automatic identification of resolved and binary sources.

Pipeline is robust and publicly available.

Abstract

The introduction of infrared arrays for lunar occultations (LO) work and the improvement of predictions based on new deep IR catalogues have resulted in a large increase in the number of observable occultations. We provide the means for an automated reduction of large sets of LO data. This frees the user from the tedious task of estimating first-guess parameters for the fit of each LO lightcurve. At the end of the process, ready-made plots and statistics enable the user to identify sources which appear to be resolved or binary and to initiate their detailed interactive analysis. The pipeline is tailored to array data, including the extraction of the lightcurves from FITS cubes. Because of its robustness and efficiency, the wavelet transform has been chosen to compute the initial guess of the parameters of the lightcurve fit. We illustrate and discuss our automatic reduction pipeline by analyzing a large volume of novel occultation data recorded at Calar Alto Observatory. The automated pipeline package is available from the authors.