Investigating the Soft X-Ray Spectra of Solar Flare Onsets
Bibliographic record
Abstract
Abstract In this study, we present the analysis of six solar flare events that occurred in 2022, using new data from the third-generation Miniature X-Ray Solar Spectrometer, also known as the Dual-zone Aperture X-ray Solar Spectrometer (DAXSS). The primary focus of this study is on the flare’s onset phase , which is characterized by elevated soft X-ray emissions even before the flare’s impulsive phase. We analyze the temporal evolution of plasma temperature, emission measure, and elemental abundance factors during the flare onset phase by fitting the DAXSS spectra with the Astrophysical Plasma Emission Code model. The model fitting results indicate that the flaring plasma is already at a high temperature (10–15 MK) during the onset period. The temperature rises during the onset phase, followed by a decrease and subsequent increase during the impulsive phase. Elemental abundance factors show a trend of falling below preflare values during the onset phase, with some recovery before the impulsive phase. During the impulsive phase, the abundance factors decrease from elevated coronal values to about photospheric values. We also analyze images from the 193 Å channel of the Atmospheric Imaging Assembly (AIA), highlighting the formation or brightening of coronal loop structures during the onset phase. Two distinct onset loop configurations are observed, which are referred to as one-loop and two-loop onsets. Both DAXSS and AIA observations indicate that the flare onset phase exhibits hot coronal plasma properties similar to those of the impulsive phase, suggesting that the onset phase may act as a preconditioning effect for some flares.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".