An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images

Abstract

Recent instrumentation has demonstrated that the solar atmosphere supports omnipresent transverse waves, which could play a key role in energizing the solar corona. Large-scale studies are required in order to build up an understanding of the general properties of these transverse waves. To help facilitate this, we present an automated algorithm for identifying and tracking features in solar images and extracting the wave properties of any observed transverse oscillations. We test and calibrate our algorithm using a set of synthetic data, which includes noise and rotational effects. The results indicate an accuracy of 1%-2% for displacement amplitudes and 4%-10% for wave periods and velocity amplitudes. We also apply the algorithm to data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and find good agreement with previous studies. Of note, we find that 35%-41% of the observed plumes exhibit multiple wave signatures, which indicates either the superposition of waves or multiple independent wave packets observed at different times within a single structure. The automated methods described in this paper represent a significant improvement on the speed and quality of direct measurements of transverse waves within the solar atmosphere. This algorithm unlocks a wide range of statistical studies that were previously impractical.

A basal contribution from p-modes to the Alfvénic wave flux in the Sun’s corona

“…We have shown previously that the transverse modes in the corona can be directly measured with SDO/AIA even in regions with low signal-tonoise [17] [34]. These previous measurements were manual, meaning it required a substantial time to collect a statistically significant number of samples and the results were open to subjective event-selection biases.…”

Contribution of observed multi frequency spectrum of Alfvén waves to coronal heating

“…To this end, it is key to assess the heating effectiveness of this mechanisms not only from a theoretical point of view, but also testing whether the heating following from the damping of the kind of oscillations present in the solar corona can actually counteract the radiative losses of the coronal plasma. Morton et al (2016) measured the power spectrum of transverse oscillations in different regions of the solar corona using the Coronal Multi-channel Polarimeter (CoMP, Tomczyk et al 2008) and further empowered by the technique introduced by Weberg et al (2018), where an automated algorithm that identifies transverse oscillations and extract their wave properties is introduced. They find that the power spectrum of the corona can be described with the superposition of three different components: a power law that describes long-periods oscillations (more than ∼ 500 s), a plateau near the 5 minutes oscillations, and a different power law for the shortperiod oscillations.…”

Investigating “Dark” Energy in the Solar Corona Using Forward Modeling of MHD Waves

“…where periods (hence, ω's) are chosen from the observed log-normal distribution (Thurgood et al 2014;Weberg et al 2018;Morton et al 2015Morton et al , 2019. The magnitude of U i and V i are randomly chosen from a uniform distribution of…”

“…These authors reported waves with amplitudes of ∼ 20 km s −1 , suggesting that they are capable of providing the energy flux of 100-200 W m −2 to accelerate fast solar wind and balance radiative losses in quiet corona. The coronal counterpart to these propagating Alfvénic waves was observed using Doppler velocity data (Tomczyk et al 2007(Tomczyk et al , 2008Tomczyk & McIntosh 2009;Morton et al 2015) from the Coronal Multi-Channel Polarimeter (CoMP Tomczyk et al 2008), and through direct measurements with SDO/AIA (Thurgood et al 2014;Weberg et al 2018;Morton et al 2019). Both decaying (Nakariakov et al 1999;Aschwanden et al 2002) and decayless Anfinogentov et al 2013) transverse oscillations have been observed in the solar atmosphere through direct imaging.…”