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arxiv: 2606.19243 · v1 · pith:ZIFL7FNRnew · submitted 2026-06-17 · 🌌 astro-ph.SR · astro-ph.IM

Multi-Thermal CME Detection with ALMANAC

Thomas Williams , Christopher B. Prior , David MacTaggart , Huw Morgan This is my paper

Pith reviewed 2026-06-26 19:23 UTC · model grok-4.3

classification 🌌 astro-ph.SR astro-ph.IM
keywords CME detectionmulti-thermal EUVALMANAConset localisationspatiotemporal clusteringspace weather forecastingprecursor activitymagnetic helicity
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The pith

Multi-thermal ALMANAC detects low-coronal CME origins with clearer separation and more consistent onset times than single-channel or coronagraph methods.

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

The paper re-engineers the ALMANAC algorithm into a multi-wavelength system that combines EUV observations from channels with complementary temperature responses. Spatiotemporal clustering merges detections across channels to cut fragmentation and projection ambiguities while keeping near-real-time speed. Benchmarking on twenty halo CMEs from the CDAW catalogue shows gains in event coherence, interpretability, and alignment of onset times with independent estimates. The method also pairs with ARTop to link EUV intensity changes to photospheric magnetic winding and helicity, revealing pre-eruptive kurtosis spikes. This targets the longstanding problem that coronagraphs miss the low-coronal launch signatures needed for early space-weather warnings.

Core claim

Extending ALMANAC to a multi-thermal EUV framework with complementary channel responses and spatiotemporal clustering produces detections that separate eruptions more cleanly, localise onsets more consistently relative to coronagraph timings, and capture precursor activity missed by white-light catalogues; when run alongside ARTop these signatures often precede magnetic helicity changes and X-class activity.

What carries the argument

Multi-thermal ALMANAC algorithm that merges detections across EUV channels via spatiotemporal clustering to reduce bifurcation while preserving parallel-computing performance.

Load-bearing premise

Complementary temperature responses from multiple EUV channels reduce projection effects and wavelength ambiguities without introducing new selection biases or post-hoc tuning that would erase the reported gains in onset consistency.

What would settle it

A new sample of halo or non-halo CMEs where multi-thermal ALMANAC onset times show larger scatter against independent low-coronal markers than single-channel ALMANAC or CDAW estimates would falsify the claimed improvement.

Figures

Figures reproduced from arXiv: 2606.19243 by Christopher B. Prior, David MacTaggart, Huw Morgan, Thomas Williams.

Figure 1
Figure 1. Figure 1: Flowchart illustrating the five stages of the ALMANAC algorithm with two example use-cases for the outputs with respect to space weather forecasting. their onset times, and estimate their low-coronal source locations. In contrast to the original implementation, this version emphasizes modularity, scalability, and robustness, enabling high-throughput processing of multi-wavelength datasets using modern Pyth… view at source ↗
Figure 2
Figure 2. Figure 2: Example CME and associated X2.2 solar flare from AR 11158 (CDAW Index 2) captured in ALMANAC across all SDO/AIA channels (ALMANAC Index 3). On the left of each panel are the SDO/AIA emission profiles that have been sharpened with Multi-Scale Gaussian Normalisation (H. Morgan & M. Druckm¨uller 2014, MGN) and the corresponding ALMANAC detection mask (black) on the right. Note that an animated version is only… view at source ↗
Figure 3
Figure 3. Figure 3: The same as [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The same as [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The top four panels show 24-hour plots for kurtosis on the AIA intensity and ALMANAC Ratio datacubes and their 1-hour smoothed time series for NOAA AR 11158. The bottom two panels shows the corresponding time series for the current-carrying components of the magnetic winding and magnetic helicity. The 1-hour running mean and the 1 and 2-sigma envelopes are shown by the shaded regions. ALMANAC detections ar… view at source ↗
Figure 6
Figure 6. Figure 6: The same as [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The same as [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: NOAA AR 11158 maps centred around spike 1 in [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: NOAA AR 11158 maps centred around spike 2 in [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: NOAA AR 11158 maps centred around spike 3 in [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: NOAA AR 12673 maps centred around spike 1 in [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: NOAA AR 12673 maps centred around spike 2 in [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: NOAA AR 12673 maps centred around spike 3 in [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: NOAA AR 12699 maps centred around spike 1 in [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: NOAA AR 12699 maps centred around spike 3 in [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: AIA 131 intensity snapshots that have been enhanced with MGN. The contours of ALMANAC indexes 10 and 11 ( [PITH_FULL_IMAGE:figures/full_fig_p025_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: AIA 193 intensity snapshots that have been enhanced with MGN. The contours of ALMANAC indexes 10 and 11 ( [PITH_FULL_IMAGE:figures/full_fig_p026_17.png] view at source ↗
read the original abstract

Reliable identification of low-coronal CME origins remains a key limitation in space weather forecasting with coronagraphs not directly resolving low-coronal signatures. We present a re-engineered multi-thermal implementation of the ALMANAC algorithm, designed to detect eruptive signatures in EUV observations. The framework extends the method to a multi-wavelength system, improving robustness against projection effects, instrumental artifacts, and wavelength-dependent ambiguities via complementary temperature responses. A spatiotemporal clustering scheme merges detections across channels, reducing event bifurcation and improving coherence while maintaining NRT performance through parallel computing. Benchmarking against 20 halo CMEs from CDAW shows improved interpretability and operational usability, with clearer separation of eruptions and more consistent onset localisation relative to coronagraph estimates. The main benefits arise from improved event discrimination, reduced fragmentation, and more interpretable source region identification. ALMANAC shows sensitivity to precursor low-coronal activity not always captured in white-light catalogues, highlighting its advantages for early warning detection. When coupled with the ARTop framework, it enables co-analysis of coronal intensity variability and photospheric magnetic evolution. In this context, kurtosis time-series from multi-wavelength EUV data exhibit recurrent pre-eruptive spikes that frequently align with enhancements in magnetic winding and helicity injection. Across multiple regions, these signatures often precede solar activity, including potential discrimination of X-class flares, while remaining suppressed during magnetically quiet intervals. Overall, integrating coronal diagnostics with photospheric topology offers a pathway toward improved eruption forecasting and space weather prediction.

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

3 major / 1 minor

Summary. The paper presents a re-engineered multi-thermal version of the ALMANAC algorithm for detecting eruptive signatures in multi-wavelength EUV observations. It extends the method with complementary temperature responses across channels and a spatiotemporal clustering scheme to merge detections, aiming to reduce projection effects, fragmentation, and wavelength-dependent ambiguities while preserving near-real-time performance. Benchmarking is reported against 20 halo CMEs from the CDAW catalogue, claiming improved interpretability, clearer event separation, and more consistent onset localisation relative to coronagraph estimates. The work also couples ALMANAC with the ARTop framework to examine pre-eruptive kurtosis spikes in EUV data alongside photospheric magnetic evolution, suggesting utility for eruption forecasting.

Significance. If the reported gains in coherence and localisation are shown to be robust and not artifacts of event selection or hyperparameter tuning, the approach could meaningfully advance low-coronal CME source identification for space weather applications by leveraging multi-thermal EUV data to complement white-light observations. The integration with ARTop for joint coronal-photospheric analysis is a positive step toward multi-diagnostic forecasting, though its predictive value remains to be quantified.

major comments (3)
  1. [Abstract] Abstract: the claim of 'more consistent onset localisation relative to coronagraph estimates' is presented without any quantitative metric (e.g., mean temporal offset, RMS residual, or Kolmogorov-Smirnov test against CDAW times) or error bars; this absence makes it impossible to evaluate whether the improvement exceeds the scatter already present in single-channel or white-light catalogues.
  2. [Abstract] Abstract / benchmarking description: no comparison is shown to the single-thermal ALMANAC implementation on the identical set of 20 halo CMEs; without this control it cannot be established that the multi-thermal clustering step, rather than other algorithmic changes, produces the reported reduction in bifurcation and improved coherence.
  3. [Abstract] Abstract: the 20-event benchmark set is introduced without stating selection criteria, independence from algorithm tuning, or exclusion rules; if the events or clustering hyperparameters were chosen or adjusted with reference to CDAW onsets, the apparent gain in localisation consistency could be circular.
minor comments (1)
  1. [Abstract] The abstract states that ALMANAC 'shows sensitivity to precursor low-coronal activity not always captured in white-light catalogues' but provides no example events or false-positive rate to support this operational advantage.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below and outline the revisions we will make to strengthen the presentation of our results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of 'more consistent onset localisation relative to coronagraph estimates' is presented without any quantitative metric (e.g., mean temporal offset, RMS residual, or Kolmogorov-Smirnov test against CDAW times) or error bars; this absence makes it impossible to evaluate whether the improvement exceeds the scatter already present in single-channel or white-light catalogues.

    Authors: We agree that the abstract would benefit from explicit quantitative support for the localisation claim. In the revised manuscript we will add the mean temporal offset, RMS residual, and associated uncertainties between ALMANAC onsets and CDAW times directly in the abstract, together with a brief statement of the statistical comparison performed. revision: yes

  2. Referee: [Abstract] Abstract / benchmarking description: no comparison is shown to the single-thermal ALMANAC implementation on the identical set of 20 halo CMEs; without this control it cannot be established that the multi-thermal clustering step, rather than other algorithmic changes, produces the reported reduction in bifurcation and improved coherence.

    Authors: The single-thermal version was described in earlier work, but we accept that a direct head-to-head evaluation on the same 20 events is necessary to isolate the contribution of the multi-thermal clustering. We will add this comparison (including metrics for fragmentation and coherence) to the revised manuscript. revision: yes

  3. Referee: [Abstract] Abstract: the 20-event benchmark set is introduced without stating selection criteria, independence from algorithm tuning, or exclusion rules; if the events or clustering hyperparameters were chosen or adjusted with reference to CDAW onsets, the apparent gain in localisation consistency could be circular.

    Authors: The 20 halo CMEs were selected as all events in the CDAW catalogue during a defined observing window that possessed simultaneous multi-channel EUV coverage; no tuning of clustering hyperparameters was performed against CDAW onset times. We will insert an explicit description of these selection criteria and the independence from CDAW timing into the revised abstract and methods section. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on external benchmarking and algorithmic description

full rationale

The abstract and description present ALMANAC as a re-engineered multi-thermal detection framework whose central claims (improved coherence via spatiotemporal clustering, better onset localisation relative to CDAW) are evaluated by direct comparison to an independent external catalogue of 20 halo CMEs. No equations, fitted parameters, or self-citations are shown that would make the reported improvements equivalent to the inputs by construction. The derivation chain is therefore self-contained against external benchmarks, consistent with the default expectation that most papers exhibit no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; all claims rest on the prior ALMANAC framework and the CDAW catalog without further specification.

pith-pipeline@v0.9.1-grok · 5804 in / 1097 out tokens · 20613 ms · 2026-06-26T19:23:41.911537+00:00 · methodology

discussion (0)

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