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 in | The Astrophysical journal Vol. 954; no. 1; pp. 39 - 55 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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ISSN | 0004-637X 1538-4357 |
DOI | 10.3847/1538-4357/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. |
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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 |
Author_xml | – sequence: 1 givenname: Shabbir surname: Bawaji fullname: Bawaji, Shabbir organization: e4r, ThoughtWorks ; India – sequence: 2 givenname: Ujjaini surname: Alam fullname: Alam, Ujjaini organization: e4r, ThoughtWorks ; India – sequence: 3 givenname: Surajit orcidid: 0000-0002-2325-5298 surname: Mondal fullname: Mondal, Surajit organization: New Jersey Institute of Technology Center for Solar-Terrestrial Research, 323 M L King Jr Boulevard, Newark, NJ 07102-1982, USA – sequence: 4 givenname: Divya orcidid: 0000-0002-4768-9058 surname: Oberoi fullname: Oberoi, Divya organization: S.P. Pune University National Centre for Radio Astrophysics, Tata Institute of Fundamental Research, Pune 411007, India – sequence: 5 givenname: Ayan orcidid: 0000-0002-1741-6286 surname: Biswas fullname: Biswas, Ayan organization: Royal Military College of Canada Department of Physics, Kingston, Ontario K7K 7B4, Canada |
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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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