Automated Detection of Oscillating Regions in the Solar Atmosphere

TL;DR

This paper presents an automated Bayesian spectral analysis algorithm for detecting oscillating regions in the solar atmosphere, addressing the inefficiency of manual detection in large datasets like SDO.

Contribution

The work introduces a novel automated detection method using Bayesian analysis and image filtering, improving efficiency and accuracy over manual detection for solar oscillations.

Findings

Successfully applied to TRACE datasets

Provides estimates of oscillation parameters and errors

Suitable for large-scale SDO data analysis

Abstract

Recently observed oscillations in the solar atmosphere have been interpreted and modeled as magnetohydrodynamic wave modes. This has allowed the estimation of parameters that are otherwise hard to derive, such as the coronal magnetic-field strength. This work crucially relies on the initial detection of the oscillations, which is commonly done manually. The volume of Solar Dynamics Observatory (SDO) data will make manual detection inefficient for detecting all of the oscillating regions. An algorithm is presented which automates the detection of areas of the solar atmosphere that support spatially extended oscillations. The algorithm identifies areas in the solar atmosphere whose oscillation content is described by a single, dominant oscillation within a user-defined frequency range. The method is based on Bayesian spectral analysis of time-series and image filtering. A Bayesian approach sidesteps the need for an a-priori noise estimate to calculate rejection criteria for the observed signal, and it also provides estimates of oscillation frequency, amplitude and noise, and the error in all these quantities, in a self-consistent way. The algorithm also introduces the notion of quality measures to those regions for which a positive detection is claimed, allowing simple post-detection discrimination by the user. The algorithm is demonstrated on two Transition Region and Coronal Explorer (TRACE) datasets, and comments regarding its suitability for oscillation detection in SDO are made.