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arxiv: 2605.11822 · v1 · submitted 2026-05-12 · 🌌 astro-ph.IM · astro-ph.SR

Recognition: 2 theorem links

· Lean Theorem

Real-time detection of solar flares from ground-based VLF data

Pauline Teysseyre , Carine Briand , Morris Cohen
Authors on Pith no claims yet

Pith reviewed 2026-05-13 04:35 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.SR
keywords solar flaresVLFionosphereD-regionreal-time detectionX-ray fluxphase analysisground-based monitoring
0
0 comments X

The pith

Ground-based VLF phase trend analysis detects 82.7 percent of M and X solar flares within one fourth of their rise time.

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

The paper presents a method to detect solar flares in real time by monitoring Very Low Frequency radio signals received at ground stations. These signals propagate through the Earth-ionosphere waveguide, and their phase shifts when the D-region electron density rises due to solar X-ray bursts. An incremental algorithm identifies the flares by locating these phase trend changes, achieving detection of most major events quickly. The approach also combines signals from multiple transmitters to estimate solar X-ray flux and uses propagation models to derive D-region density profiles. A reader would care because the technique relies only on ground equipment, reducing latency and providing a backup when satellites are unavailable or delayed.

Core claim

By seeking trend changes in VLF phase data from several transmitters, an incremental algorithm identifies solar flares produced by sudden increases in D-region electron density from solar X-ray radiation. It detects 82.7 percent of M and X class flares within one fourth of their rise time. Phase variations across multiple paths further allow estimation of the Sun's X-ray flux, while combination with models such as LMP or LWPC yields D-region electron density profiles. This ground-based system delivers alerts with latency comparable to or shorter than satellite observations.

What carries the argument

Incremental algorithm that detects trend changes in VLF phase data from multiple simultaneous transmitters, integrated with propagation models to estimate X-ray flux and D-region profiles.

If this is right

  • Real-time alerts for most M and X flares arrive within one fourth of the flare rise time.
  • Simultaneous monitoring of multiple VLF transmitters yields an estimate of the Sun's X-ray flux.
  • VLF measurements combined with propagation models produce D-region electron density profiles.
  • Alerts can match or precede satellite-based notifications due to lower data latency.
  • The system operates entirely from ground stations, serving as a maintainable backup if satellites fail.

Where Pith is reading between the lines

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

  • A distributed network of inexpensive VLF receivers could deliver continuous global space-weather monitoring.
  • The phase-trend approach might be extended to identify other D-region disturbances such as those from lightning or energetic particles.
  • Hybrid use with satellite data could reduce false detections while preserving the low-latency advantage.

Load-bearing premise

Trend changes in VLF phase data are caused primarily by solar-flare increases in D-region electron density and can be distinguished from other ionospheric disturbances.

What would settle it

Observing frequent phase trend changes during periods with no M or X flares according to satellite X-ray records, or missing detection of most known M and X flares in the VLF data, would show the method does not work as claimed.

Figures

Figures reproduced from arXiv: 2605.11822 by Carine Briand, Morris Cohen, Pauline Teysseyre.

Figure 1
Figure 1. Figure 1: Phase (Φ, in °, black curve) and soft X-ray flux (in W/m2 , green curve) on 2024/07/28. Three flares are presented and taken as illustration for the reminder of the paper. They are denoted by their soft X-ray flux peak time: 10.71, 12.01 and 12.84 UT. δ is a user-defined threshold; the new breakpoint is kept when it satisfies (Lchange − Lnochange) Lnochange > δ (3) Then, the phase slope associated with the… view at source ↗
Figure 2
Figure 2. Figure 2: NRK phase (black line, also shown in [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Number of flares for which a positive phase slope was detected (left) and median time delay (right) of the detection method applied to one year of phase data from NRK for different combina￾tions of δ and tr . forecast time. tr is thus set to 60 s, a rate that optimizes the code running-time and makes it more manageable. With these parameters, 82.7% M and X flares visible in the training set were detected. … view at source ↗
Figure 4
Figure 4. Figure 4: Ratio between the detection time and the phase rise time (r) vs phase rise time (in minutes). VLF-flare detection is on average 7.1 minutes after the start of an X-ray flux increase. However, the average time from VLF phase increase to flare detection is 2.6 minutes. For comparison, GOES alerts are triggered by M5 threshold with a latency of 4 to 6 minutes (George et al., 2019; Hudson, 2025). Our method re… view at source ↗
Figure 5
Figure 5. Figure 5: Breakpoint identification scheme. It starts from a ”New breakpoint detection” (left side), and continue with the slope s sign, which will determine whether the breakpoint is due to a flare onset, flare maximum, flare decay or is still in quiescent periods. After building a catalogue of all breakpoints from the one-year period described previously, they are sorted by ‘flare’ and ‘quiet period’. Comparison w… view at source ↗
Figure 6
Figure 6. Figure 6: Probability density functions (PDF) for NRK. Top panel: ∆Φ vs. GOES X-ray flux. Bottom left panel: PDFs for discretized phase. Bottom right: PDF fitted by a normalized Gaussian distribu￾tion. 4.2. PDFs from a single transmitter To compute the PDF in Equation 5, it is necessary to discretise the values of ∆Φ and the X-ray flux. ∆Φ is computed from fully resolved M and X flares following Eq. 4, and discretiz… view at source ↗
Figure 7
Figure 7. Figure 7: Probability density functions for the four transmitters monitored in Nanc¸ay. The PDFs shown in the bottom left panel for NRK were already present in [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Estimation of the X-flux using one, two or three transmitters for: top panel, M2.9 flare (14.32 UT on 2024/11/05); middle panel, M9.5 flare (12.11 UT on 2024/11/10); bottom panel, M2.4 flare (13.14 UT on 2024/12/07). The posterior probability density functions obtained with Equation 5 are indicated, combining information from one (black), two (brown) or three (red) transmitters. only, NRK + NAA or the thre… view at source ↗
Figure 9
Figure 9. Figure 9: Maps representing the electron density at 70 km in quiet times (left) or at the peak of the M7 flare on 2024/07/28 (right). 5. Electron density estimate Computing the D-region electron density is necessary to estimate the solar flare impact on HF absorption, and is achieved through the Longwave Mode Propagator (LMP, Gasdia and Marshall, 2021). The electron density profile is described by the Wait profile (… view at source ↗
Figure 10
Figure 10. Figure 10: Diagram of the main steps of the real-time data correction, flare detection and electron density computation from the vlf4ions package. The different methods and results are detailed in the next sections. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Detection of the X5 flare occuring on 2025/11/11. The soft and hard X-rays fluxes are represented in black (full and dashed lines respectively) and the phase from NRK in green. The red vertical lines represent the first detections of this flare, and the blue vertical lines the moment at which the flux estimation reaches an M5 threshold. Full lines show the time detection with δt = 60s and dashed lines tho… view at source ↗
read the original abstract

A method for real-time solar flare detection and characterization using ground-based Very Low Frequency (VLF, 15-45 kHz) data is presented. The D-region, the ionosphere's lowest region, is monitored by VLF waves propagating in the Earth-Ionosphere waveguide. The D-region electron density increases during sudden surges in X-ray radiation from solar flares. This subsequently enhances HF absorption. By seeking trend changes in VLF phase data, an incremental algorithm finds solar flares. 82.7% of M and X solar flares are detected within one fourth of their rise time. In addition, several VLF transmitters are monitored simultaneously. Combining information from their phase variations leads to an estimation of the Sun's X-ray flux. Last, propagation models such as LMP or LWPC are combined with the VLF measurements to compute D-region electron density profiles. This method and its implementation in a new Python package are a step towards building a more resilient system for flare detection and alerts. Its reliance on ground-based data alone ensures an easy maintenance and a backup in case a satellite failure. It also provides alerts comparable to or faster than those obtained through satellite data, due to shortened data latency.

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

0 major / 4 minor

Summary. The manuscript presents a method for real-time solar flare detection and characterization from ground-based VLF (15-45 kHz) phase data. An incremental algorithm identifies flares by detecting trend changes in VLF phase, which are attributed to D-region electron density increases from flare X-ray flux. It reports detecting 82.7% of M and X class flares within one fourth of their rise time, uses simultaneous monitoring of multiple transmitters to estimate solar X-ray flux, and combines the measurements with propagation models (LMP, LWPC) to derive D-region electron density profiles. The approach is implemented in a Python package and positioned as a resilient, low-latency ground-based complement to satellite data.

Significance. If the reported performance holds, the work provides a useful contribution to space-weather instrumentation by demonstrating a ground-based flare detection system with potential for reduced latency and independence from satellite assets. Credit is due for the explicit comparison against GOES X-ray lists, the reporting of false-alarm statistics during both quiet and disturbed intervals, and the release of a Python package that supports reproducibility.

minor comments (4)
  1. The abstract states the 82.7% detection figure without accompanying information on the total number of M/X events analyzed, the observation period, or the precise definition of 'within one fourth of their rise time'; adding these details would allow readers to assess the statistical robustness of the central claim.
  2. The procedure for combining phase variations from multiple transmitters to estimate X-ray flux is described at a high level; a short algorithmic outline or pseudocode would improve clarity and reproducibility.
  3. Acronyms LMP and LWPC should be expanded at first use, and a brief reference to the specific versions or implementations employed would aid readers unfamiliar with the propagation models.
  4. Figure captions and the text would benefit from explicit cross-references to the GOES event list and the quiet/disturbed interval statistics to make the validation steps easier to follow.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript and for recommending minor revision. No specific major comments were raised in the report, so we have no individual points to address. We will incorporate any minor editorial or presentational improvements in the revised version.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents an empirical detection algorithm that identifies trend changes in observed VLF phase data and reports its performance (82.7% detection of M/X flares within 1/4 rise time) against an external GOES X-ray flare catalog. No equations or steps reduce by construction to fitted parameters, self-citations, or renamed inputs; the central claim is a measured success rate on independent validation data, with explicit handling of false alarms during quiet and disturbed periods. The method relies on established propagation physics (LMP/LWPC) only for post-detection density profiling, not for the detection statistic itself. This is a standard observational pipeline with external benchmarking and contains no load-bearing self-referential loops.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on standard domain knowledge of VLF propagation in the Earth-ionosphere waveguide and the known effect of X-ray flares on D-region density. No new free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption VLF phase variations are driven by changes in D-region electron density caused by solar X-ray flares
    This link is invoked to justify using phase trend changes as the flare signature.

pith-pipeline@v0.9.0 · 5514 in / 1256 out tokens · 88267 ms · 2026-05-13T04:35:52.657891+00:00 · methodology

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