Statistics of Solar White-light Flares. I. Optimization and Application of Identification Methods

Abstract White-light flares (WLFs) are energetic activity in the stellar atmosphere. However, observed solar WLFs are relatively rare compared to stellar WLFs or solar flares observed at other wavelengths, which limits our further understanding of solar/stellar WLFs through statistical studies. By analyzing flare observations from the Solar Dynamics Observatory, here we improve WLF identification methods to obtain more solar WLFs and their accurate light curves from two aspects: (1) imposing constraints defined by the typical temporal and spatial distribution characteristics of WLF-induced signals; and (2) setting the intrinsic threshold for each pixel in the flare ribbon region according to its inherent background fluctuation rather than a fixed threshold for the whole region. Applying the optimized method to 90 flares (30 C-class flares, 30 M-class flares, and 30 X-class flares) for a statistical study, we identified a total of nine C-class WLFs, 18 M-class WLFs, and 28 X-class WLFs. The WLF identification rate of C-class flares reported here reaches 30%, which is the highest to date to our best knowledge. It is also revealed that in each GOES energy level the proportion of WLFs is higher in confined flares than that in eruptive flares. Moreover, a power-law relation is found between the WLF energy ( E ) and duration ( τ ): τ ∝ E 0.22 , similar to those of solar hard/soft X-ray flares and other stellar WLFs. These results indicate that we could recognize more solar WLFs through optimizing the identification method, which will lay a base for future statistical and comparison study of solar and stellar WLFs.

Authors

Publication Details

Journal
The Astrophysical Journal
Published
2024-10-25
DOI
https://doi.org/10.3847/1538-4357/ad793b
Citations
8
Primary Topic
Solar and Space Plasma Dynamics
Type
article
Field-Weighted Citation Impact
2.65
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article

Statistics of Solar White-light Flares. I. Optimization and Application of Identification Methods

Yijun Hou, Ting Li, Jifeng Liu, Yingjie Cai
8 citations
The Astrophysical Journal
Solar and Space Plasma Dynamics
2.65
article

Statistics of Solar White-light Flares. I. Optimization and Application of Identification Methods

Yijun Hou, Ting Li, Jifeng Liu, Yingjie Cai
article en
8 citations

Abstract

Abstract White-light flares (WLFs) are energetic activity in the stellar atmosphere. However, observed solar WLFs are relatively rare compared to stellar WLFs or solar flares observed at other wavelengths, which limits our further understanding of solar/stellar WLFs through statistical studies. By analyzing flare observations from the Solar Dynamics Observatory, here we improve WLF identification methods to obtain more solar WLFs and their accurate light curves from two aspects: (1) imposing constraints defined by the typical temporal and spatial distribution characteristics of WLF-induced signals; and (2) setting the intrinsic threshold for each pixel in the flare ribbon region according to its inherent background fluctuation rather than a fixed threshold for the whole region. Applying the optimized method to 90 flares (30 C-class flares, 30 M-class flares, and 30 X-class flares) for a statistical study, we identified a total of nine C-class WLFs, 18 M-class WLFs, and 28 X-class WLFs. The WLF identification rate of C-class flares reported here reaches 30%, which is the highest to date to our best knowledge. It is also revealed that in each GOES energy level the proportion of WLFs is higher in confined flares than that in eruptive flares. Moreover, a power-law relation is found between the WLF energy ( E ) and duration ( τ ): τ ∝ E 0.22 , similar to those of solar hard/soft X-ray flares and other stellar WLFs. These results indicate that we could recognize more solar WLFs through optimizing the identification method, which will lay a base for future statistical and comparison study of solar and stellar WLFs.

The Astrophysical JournalVol. 975(1)
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Solar and Space Plasma Dynamics
2.65
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