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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ace042 |
| _version_ | 1797694409337208832 |
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| author | Shabbir Bawaji Ujjaini Alam Surajit Mondal Divya Oberoi Ayan Biswas |
| author_facet | Shabbir Bawaji Ujjaini Alam Surajit Mondal Divya Oberoi Ayan Biswas |
| author_sort | Shabbir Bawaji |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T02:57:27Z |
| format | Article |
| id | doaj.art-c4bd29dd51d34e1a9094015aea7ab5b7 |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-12T02:57:27Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-c4bd29dd51d34e1a9094015aea7ab5b72023-09-03T15:13:18ZengIOP PublishingThe Astrophysical Journal1538-43572023-01-0195413910.3847/1538-4357/ace042An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their MorphologyShabbir Bawaji0Ujjaini Alam1Surajit Mondal2https://orcid.org/0000-0002-2325-5298Divya Oberoi3https://orcid.org/0000-0002-4768-9058Ayan Biswas4https://orcid.org/0000-0002-1741-6286e4r, ThoughtWorks India ; surajit.mondal@njit.edue4r, ThoughtWorks India ; surajit.mondal@njit.eduCenter for Solar-Terrestrial Research, New Jersey Institute of Technology , 323 M L King Jr Boulevard, Newark, NJ 07102-1982, USANational Centre for Radio Astrophysics, Tata Institute of Fundamental Research, S.P. Pune University , Pune 411007, IndiaNational Centre for Radio Astrophysics, Tata Institute of Fundamental Research, S.P. Pune University , Pune 411007, India; Department of Physics, Engineering Physics & Astronomy, Queen’s University , Kingston, Ontario K7L 3N6, Canada; Department of Physics, Royal Military College of Canada , Kingston, Ontario K7K 7B4, CanadaThe 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.https://doi.org/10.3847/1538-4357/ace042Solar radio emissionQuiet sunSolar coronal transientsSolar coronal heating |
| spellingShingle | Shabbir Bawaji Ujjaini Alam Surajit Mondal Divya Oberoi Ayan Biswas An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology The Astrophysical Journal Solar radio emission Quiet sun Solar coronal transients Solar coronal heating |
| title | An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology |
| title_full | An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology |
| title_fullStr | An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology |
| title_full_unstemmed | An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology |
| title_short | An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology |
| title_sort | unsupervised machine learning based algorithm for detecting weak impulsive narrowband quiet sun emissions and characterizing their morphology |
| topic | Solar radio emission Quiet sun Solar coronal transients Solar coronal heating |
| url | https://doi.org/10.3847/1538-4357/ace042 |
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