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arxiv: 2604.26036 · v1 · submitted 2026-04-28 · 🌌 astro-ph.SR

Brightenings AnD Polarity Inversion Tracking (BADPIT) method for studying solar active region evolution before major solar flares

Augustin Andr\'e-Hoffmann , Marianna B. Kors\'os , Alexander Nindos , Spiros Patsourakos , Manolis K. Georgoulis , Robertus Erd\'elyi This is my paper

Pith reviewed 2026-05-07 14:53 UTC · model grok-4.3

classification 🌌 astro-ph.SR
keywords solar flaresactive regionsEUV transient brighteningsflare predictionSDO/AIABADPIT methodpolarity inversion lines
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The pith

The BADPIT method detects up to five times more EUV transient brightenings in flaring active regions than in non-flaring ones with similar sunspot types.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces the Brightenings AnD Polarity Inversion Tracking method to detect EUV transient brightenings across multiple SDO/AIA channels in solar active regions. It tests two independent detection thresholds, a 3-sigma intensity criterion and a power-law divergence approach, on 24-hour datasets from a flaring active region and a quiescent one that share similar Hale classifications. Significantly more brightenings appear in the flaring region, with power-law events frequent only there and largely absent in the non-flaring case. The results indicate that both threshold methods can serve as diagnostic tools to flag imminent major flares several hours before onset.

Core claim

The BADPIT method applied to flare-productive AR 11429 and quiescent AR 13186 shows up to five times more 3-sigma thresholded transient brightenings in the flaring region, while power-law thresholded events occur frequently only in the flaring AR and are mostly absent in the non-flaring AR. Both the power-law threshold method and the 3-sigma method therefore appear useful for distinguishing between regions that will produce major flares and those that will not, several hours before the onset.

What carries the argument

BADPIT method that detects and tracks EUV transient brightenings with dual independent thresholds (3-sigma intensity and power-law divergence) while following polarity inversion lines.

If this is right

  • Frequent transient brightenings signal higher flare productivity in an active region.
  • Power-law threshold detections are especially diagnostic because they rarely appear in non-flaring regions.
  • Monitoring these brightenings can provide early indications of major flares hours in advance.
  • The dual-threshold approach is suitable for scaling to a full statistical study across many active regions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could be combined with existing magnetic-field or coronal measures to refine flare-probability estimates.
  • Transient brightenings may mark the early release of stored coronal energy that precedes large flares.
  • Extending the analysis to additional AIA channels or longer time windows could tighten the lead time for warnings.

Load-bearing premise

The differences in detected transient brightenings between the two active regions result from their differing flare productivity rather than from other unaccounted factors such as region size or magnetic details.

What would settle it

A larger sample in which non-flaring active regions show comparable numbers of 3-sigma or power-law brightenings to flaring regions would falsify the diagnostic distinction.

Figures

Figures reproduced from arXiv: 2604.26036 by Alexander Nindos, Augustin Andr\'e-Hoffmann, Manolis K. Georgoulis, Marianna B. Kors\'os, Robertus Erd\'elyi, Spiros Patsourakos.

Figure 1
Figure 1. Figure 1: Example AIA 94 Å saturated frame (panel a) and its desaturated result (panel b) from AR 11429 observations on 06/03/2012 at 03:27:03UT. In panel A, the circle and boxes highlight examples of artefacts that must be removed before applying BADPIT: (1) saturated pixels, (2) diffraction arms, and (3) saturation trails. In panel B, remnants of the desaturation seen as dark diagonal lines can be observed but do … view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of the distribution function of all pixel’s intensities (in DN · s −1 ) of the field-of-view from all frames over the 24-hour interval of interest of AR 11429, for the original 193 Å data (black) and the desaturated data (red). not affect our study due to the detection criteria that will be presented in Section 3.2, we do not try to detect and correct such artefacts. Our desaturation procedure r… view at source ↗
Figure 3
Figure 3. Figure 3: Resulting difference image (c) of two consecutive desaturated images (a and b). The two images used are AR 11429 AIA 94Å images, 12 seconds apart. • Spatial scale: Detected patches must span an area larger than 3 contiguous pixels (i.e., greater than 5.67 × 105 km2 ). • Temporal scale: Detected events must persist for more than five consecutive frames (i.e., at least one minute). These criteria restrict th… view at source ↗
Figure 4
Figure 4. Figure 4: Density distribution of the intensity (black curve) compared to different fitting methods, namely power-law (red), lognormal (green), and exponentially modified Gaussian (blue). The exponent values of the power-law fit are 4.81 for AR 13186 (panel a) and 1.96 for AR 11429 (panel b). The vertical dashed line represents the value at which the data separates from the power-law fit. The bottom plots correspond… view at source ↗
Figure 5
Figure 5. Figure 5: HMI magnetogram (a) and AIA 1600 Å (b) observations of AR 11429 averaged over a 3-hour period starting from 2012-03-06 at 09:00. The orange and red contours represent the AR area and the PIA, respectively. shown in Fig. 5a (magnetogram) and Fig. 5b (1600 Å), following the methodology presented in Schrijver (2007). LoS magnetograms are sufficient here since we restrict our analysis to ARs within central mer… view at source ↗
Figure 6
Figure 6. Figure 6: Multiple-wavelength observations of the flare-imminent (AR 11429 on top) and the non-flaring (AR 13186 in the bottom) configurations. Each panel shows observations made on a 3-hour interval: starting from 06/03/2012 09:00UT for AR 11429, and from 16/01/2023 00:00UT for AR 13186. The colour scale for the masks shows the AR mask (grey), the PIA (white) and the 3-𝜎 TBs (orange) view at source ↗
Figure 7
Figure 7. Figure 7: Evolution of the two ARs through the 24 hours of observation and the brightenings distribution associated with every time sequence. The location of the PIA is indicated by white pixels. The orange pixels show the brightenings detected through the 3-𝜎 threshold method, while in red are the ones from the power-law threshold method. Every panel here shows the activity corresponding of 4-hour intervals. AR. We… view at source ↗
Figure 8
Figure 8. Figure 8: Number of brightenings detected using the 3-𝜎 (black) and the power-law (blue) threshold methods in the 94 Å channel. Panel a shows the results obtained for AR 13186, and Panel b for AR 11429. The orange and red vertical dashed lines correspond, respectively, to the peak times of C- and M-class flares that occurred in the ARs. The TBs were counted over timeseries of 1-hour intervals. (a) Intensity (b) Unsi… view at source ↗
Figure 9
Figure 9. Figure 9: Cumulative evolution of the total 94 Å intensity (panel a) and unsigned magnetic flux (panel b) of the 3-𝜎 brightenings detected in AR 13186 (non-flaring) and 11429 (flaring). The measure is done over timeseries of 1-hour intervals. however, a significant difference in the number of such TBs should be expected and looks promising, with a five times difference in our case studywith a factor of five differen… view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of the extensive (cumulative intensity; black) and intensive (cumulative intensity per pixel; red) measures of the intensity associated with the 3-𝜎 brightenings detected through 94 Å observations of AR 13186 on the left panel (a) and AR 11429 on the right one (b). Here, we use a sampling interval of 1 hour. intensity stayed consistent and was almost not affected by the changes in the sampling … view at source ↗
read the original abstract

This study investigates the relationship between extreme ultraviolet (EUV) transient brightenings (TBs) and the onset of GOES X-class solar flares in active regions (ARs). We introduce the Brightenings AnD Polarity Inversion Tracking (BADPIT) method that detects TBs across multiple SDO/AIA channels. To identify TBs, we impose two independent thresholds: a 3-(sigma) intensity-based criterion and a power-law divergence approach. We apply the BADPIT method to datasets of a flaring and a non-flaring AR for 24 hours as a pathfinder to a comprehensive statistical study for a complete performance verification: the flare-productive AR 11429 and the quiescent AR 13186, both sharing a similar Hale sunspot classification. Preliminary results are encouraging: significantly more TBs are detected in the flaring AR, with up to five times more 3-(sigma) thresholded TBs, while power-law thresholded events were frequent only in the flaring AR and mostly absent in the non-flaring AR. We find that both the power-law threshold method and the 3-(sigma) method can be useful diagnostic tools for distinguishing between imminent flaring or not, several hours before the onset of major solar flares.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces the BADPIT method for detecting EUV transient brightenings (TBs) in solar active regions via polarity inversion tracking and two independent thresholds (3-sigma intensity and power-law divergence). It applies the method over 24 hours to the flare-productive AR 11429 (X-class flare) and the quiescent AR 13186 (matched Hale class), reporting up to 5 times more 3-sigma TBs in the flaring region and power-law events present only in the flaring AR. The authors conclude that both thresholds can serve as useful diagnostics for distinguishing imminent flaring several hours in advance, framing the work as a pathfinder for future statistical verification.

Significance. If the TB rate differences can be shown to generalize beyond the current pair of regions and to be independent of other active-region properties, the dual-threshold BADPIT approach could provide a new observational diagnostic for flare productivity. The cross-validation between the two detection methods is a constructive element of the design.

major comments (1)
  1. The central claim that both the 3-sigma and power-law methods 'can be useful diagnostic tools for distinguishing between imminent flaring or not, several hours before the onset of major solar flares' rests on a single-pair comparison of AR 11429 and AR 13186. These regions share Hale class but are not matched on total unsigned flux, area, or evolutionary stage; no statistical tests, error bars, or controls for confounders are presented. Consequently the reported excess (up to 5× for 3-sigma TBs; power-law events absent in the non-flaring AR) cannot yet be attributed specifically to flare productivity.
minor comments (2)
  1. The methodology section should supply the precise algorithmic steps and any tunable parameters for the power-law divergence criterion so that the detection can be reproduced independently.
  2. Time-series plots of TB detections should include uncertainty estimates or Poisson errors on the counts to permit quantitative assessment of the reported differences.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the current results constitute a limited pathfinder comparison and will revise the manuscript to more clearly reflect this limitation while preserving the preliminary findings.

read point-by-point responses

  1. Referee: The central claim that both the 3-sigma and power-law methods 'can be useful diagnostic tools for distinguishing between imminent flaring or not, several hours before the onset of major solar flares' rests on a single-pair comparison of AR 11429 and AR 13186. These regions share Hale class but are not matched on total unsigned flux, area, or evolutionary stage; no statistical tests, error bars, or controls for confounders are presented. Consequently the reported excess (up to 5× for 3-sigma TBs; power-law events absent in the non-flaring AR) cannot yet be attributed specifically to flare productivity.

    Authors: The manuscript already frames the work as a pathfinder study intended 'for a complete performance verification' in future statistical work. We will revise the abstract, conclusions, and add a dedicated limitations subsection to explicitly state that the two regions differ in unsigned flux, area, and evolutionary stage, that the observed TB excess cannot yet be attributed solely to flare productivity, and that formal statistical tests are not applicable to a sample of two. Error bars on TB counts will be added based on the detection thresholds. The wording of the diagnostic utility claim will be toned down to reflect its preliminary nature pending larger-sample verification. revision: partial

Circularity Check

0 steps flagged

No significant circularity; purely observational detection and comparison

full rationale

The paper introduces the BADPIT method as an observational detection technique that applies two independent thresholds (3-sigma intensity and power-law divergence) to EUV data from SDO/AIA. It then directly counts and compares transient brightenings in one flaring AR (11429) versus one non-flaring AR (13186) over 24 hours. No mathematical derivations, parameter fitting presented as prediction, self-definitional loops, or load-bearing self-citations appear in the chain. The central claim that the methods may serve as pre-flare diagnostics rests on the empirical count difference rather than any reduction to the input data by construction. This is a standard self-contained pathfinder observational study.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The abstract provides limited technical detail; the main parameters are the detection thresholds, treated as inputs rather than derived.

free parameters (2)
  • 3-sigma intensity threshold
    Chosen as a standard deviation multiple for detection; specific value may be conventional but applied here without justification in abstract.
  • power-law divergence criterion
    Used to identify TBs; details on how the power-law is applied or thresholded not specified.
axioms (1)
  • domain assumption Transient brightenings in EUV are associated with magnetic activity in active regions and can be detected across multiple AIA channels.
    Core to the method's applicability to studying AR evolution before flares.

pith-pipeline@v0.9.0 · 5570 in / 1475 out tokens · 138143 ms · 2026-05-07T14:53:12.642385+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages

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    work page doi:10.3847/1538-4357/adbf13 2023

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    DOI. ADS. Krista, L.D., Chih, M.: 2021, A DEFT Way to Forecast Solar Flares.Astrophys. J.922, 218. DOI. ADS. Kusano, K., Bamba, Y., Yamamoto, T.T., Iida, Y., Toriumi, S., Asai, A.: 2012, Magnetic Field Structures Triggering Solar Flares and Coronal Mass Ejections.Astrophys. J.760, 31. DOI. ADS. Kusano,K.,Iju,T.,Bamba,Y.,Inoue,S.:2020,Aphysics-basedmethodt...

    work page doi:10.1086/377511 2021