An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology

The solar corona is extremely dynamic. Every leap in observational capabilities has been accompanied by unexpected revelations of complex dynamic processes. The ever more sensitive instruments now allow us to probe events with increasingly weaker energetics. A recent leap in the low-frequency radio solar imaging ability has led to the discovery of a new class of emissions, namely weak impulsive narrowband quiet Sun emissions (WINQSEs). They are hypothesized to be the radio signatures of coronal nanoflares and could potentially have a bearing on the long standing coronal heating problem. In view of the significance of this discovery, this work has been followed up by multiple independent studies. These include detecting WINQSEs in multiple data sets, using independent detection techniques and software pipelines, and looking for their counterparts at other wavelengths. This work focuses on investigating morphological properties of WINQSEs and also improves upon the methodology used for detecting WINQSEs in earlier works. We present a machine learning-based algorithm to detect WINQSEs, classify them based on their morphology, and model the isolated ones using 2D Gaussians. We subject multiple data sets to this algorithm to test its veracity. Interestingly, despite the expectations of their arising from intrinsically compact sources, WINQSEs tend to be resolved in our observations. We propose that this angular broadening arises due to coronal scattering. Hence, WINQSEs can provide ubiquitous and ever-present diagnostic of coronal scattering (and, in turn, coronal turbulence) in the quiet Sun regions, which has not been possible until date.

Automated summary

The solar corona is highly dynamic, and recent advancements in observational capabilities have led to the discovery of a new class of emissions known as weak impulsive narrowband quiet Sun emissions (WINQSEs). These emissions are hypothesized to be related to coronal nanoflares and may help solve the coronal heating problem. Multiple independent studies have followed up on this discovery by detecting WINQSEs in various data sets and investigating their properties. A machine learning-based algorithm is proposed to detect and classify WINQSEs based on their morphology, and the phenomenon of angular broadening observed suggests the presence of coronal scattering, providing new insights into coronal turbulence in the quiet Sun regions.