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...

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Published inThe Astrophysical journal Vol. 954; no. 1; pp. 39 - 55
Main Authors Bawaji, Shabbir, Alam, Ujjaini, Mondal, Surajit, Oberoi, Divya, Biswas, Ayan
Format Journal Article
LanguageEnglish
Published Philadelphia The American Astronomical Society 01.09.2023
IOP Publishing
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ISSN0004-637X
1538-4357
DOI10.3847/1538-4357/ace042

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Abstract 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.
AbstractList 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.
Author Oberoi, Divya
Mondal, Surajit
Biswas, Ayan
Bawaji, Shabbir
Alam, Ujjaini
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Snippet The solar corona is extremely dynamic. Every leap in observational capabilities has been accompanied by unexpected revelations of complex dynamic processes....
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SubjectTerms Algorithms
Astrophysics
Corona
Coronal heating
Datasets
LF radio
Machine learning
Morphology
Narrowband
Quiet Sun
Scattering
Solar corona
Solar coronal heating
Solar coronal transients
Solar imagery
Solar radio emission
Sun
Unsupervised learning
Wavelengths
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Title An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology
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