Automatic detection technique for solar filament oscillations in GONG data

¹ Departament Física, Universitat de les Illes Balears, 07122 Palma de Mallorca, Spain
e-mail: manuel.luna@uib.es
² Institute of Applied Computing & Community Code (IAC 3), UIB, Spain
³ Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France

Received: 3 June 2022
Accepted: 8 September 2022

Abstract

Context. Solar filament oscillations have been known for decades. The new capabilities of the new telescopes have afforded routine observations of these periodic motions. Oscillations in filaments show key aspects of their structure. A systematic study of filament oscillations over the solar cycle can shed light on the evolution of the prominences.

Aims. This work is a proof of concept that aims to automatically detect and parametrise these oscillations using Hα data from the GONG network of telescopes.

Methods. The proposed technique studies the periodic fluctuations of every pixel of the Hα data cubes. Using the fast Fourier transform, we computed the power spectral density (PSD). We defined a criterion to consider whether it is a real oscillation or a spurious fluctuation. This consisted of considering that the peak in the PSD must be greater than several times the background noise with a confidence level of 95%. The background noise is well fitted to a combination of red and white noise. We applied the method to several observations that were reported in the literature to determine its reliability. We also applied the method to a test case, which was a data set in which the oscillations of the filaments were not known a priori.

Results. The method shows that the filaments contain areas in which the PSD is above the threshold value. The periodicities we obtained generally agree with the values that were obtained by other methods. In the test case, the method detects oscillations in several filaments.

Conclusions. We conclude that the proposed spectral technique is a powerful tool for automatically detecting oscillations in prominences using Hα data.