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arxiv: 2604.22685 · v1 · submitted 2026-04-24 · 🌌 astro-ph.IM · astro-ph.EP· cs.NI· cs.PF

CosmicDancePro -- Measuring LEO satellite's orbital decay and network connectivity implications during solar storms

Suvam Basak , Amitangshu Pal , Debopam Bhattacherjee This is my paper

Pith reviewed 2026-05-08 09:53 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.EPcs.NIcs.PF
keywords LEO satellitessolar stormsStarlinkorbital decaynetwork connectivityspace weatherCosmicDanceProatmospheric density
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The pith

A new open-source tool called CosmicDancePro measures how solar storms drive LEO orbital decay and connectivity loss in networks like Starlink.

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

The paper introduces CosmicDancePro to combine space weather data from satellites, upper-atmospheric density models, satellite trajectory records, and network performance traces into a single analysis pipeline for solar storm effects. When applied to the May 2024 superstorm, the tool shows that Starlink switches to distinct fleet management tactics that differ from its usual orbit corrections and that these tactics produce a characteristic W-shaped altitude pattern across orbital planes. The same data integration also documents concrete connectivity problems during storm peaks, such as short repeated outages, latency jumps above 500 ms, up to 60 percent higher uplink loss, altered daily latency cycles, and user data-rate drops exceeding 10 Mbps.

Core claim

CosmicDancePro integrates multimodal datasets including space weather measurements from several satellites, upper-atmospheric density conditions from data-driven and high-fidelity physics-based models, and LEO satellite trajectory and LEO network measurement traces to quantify orbital decay driven by enhanced atmospheric density and network connectivity degradation. Analysis of the Starlink constellation during the May 2024 solar superstorm identifies the specific fleet management strategies adopted and how they differ from regular orbit-correction strategy, identifies the mechanisms driving the previously unexplained W-shaped altitude variation pattern across orbital planes of LEO constell-

What carries the argument

CosmicDancePro, an open-source tool that fuses space weather observations, physics-based density models, trajectory data, and network traces to isolate and quantify solar-storm effects on LEO orbits and connectivity.

If this is right

  • Starlink switches to distinct fleet management strategies during solar superstorms rather than following its standard orbit-correction procedures.
  • The W-shaped altitude variation pattern across LEO orbital planes is produced by storm-enhanced atmospheric density combined with the fleet's response tactics.
  • Network-layer performance exhibits transient disruptions including short-lived outages, latency spikes above 500 ms, up to 60 percent uplink loss increase, distorted diurnal patterns, and over 10 Mbps end-user data-rate drops during storm peaks.
  • Orbital decay and connectivity loss can be systematically measured by integrating space weather, density models, trajectories, and network traces.

Where Pith is reading between the lines

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

  • Operators could pre-load storm-specific orbit and routing rules to reduce the observed latency and loss spikes once the pattern is confirmed in additional events.
  • Extending the same multimodal fusion approach to other LEO constellations would show whether the W-pattern and connectivity signatures are common or unique to Starlink's design.
  • Repeated application after future storms could produce empirical decay rates usable for updating long-term constellation lifetime models.

Load-bearing premise

The data-driven and high-fidelity physics-based models for upper-atmospheric density, when combined with trajectory and network traces, accurately isolate and quantify solar-storm-driven orbital decay and connectivity effects without major unaccounted confounding variables.

What would settle it

Re-analysis of the identical Starlink trajectory and network traces from the May 2024 storm period using independent density models or direct altitude measurements that shows neither measurable extra orbital decay nor the listed connectivity degradations would falsify the tool's claimed quantifications.

Figures

Figures reproduced from arXiv: 2604.22685 by Amitangshu Pal, Debopam Bhattacherjee, Suvam Basak.

Figure 1
Figure 1. Figure 1: (a) Solar cycle progression and the launch date of the LEO satellite constellations (b) Solar storm view at source ↗
Figure 2
Figure 2. Figure 2: Detecting the orbit raise complete and deorbit start with (a) Starlink and (b) OneWeb’s TLE timeseries. view at source ↗
Figure 3
Figure 3. Figure 3: Dissecting two major solar events (a) May 2024 solar superstorm (NOAA G5) and (b) October 2024 view at source ↗
Figure 4
Figure 4. Figure 4: Cherry-picked one satellite from each shell of Starlink to demonstrate the nature of orbital decay at view at source ↗
Figure 5
Figure 5. Figure 5: Shell-wise distribution of altitude decay compared to the last day in (a) OneWeb, and (b)-(f ) Starlink view at source ↗
Figure 6
Figure 6. Figure 6: Starlink satellite altitude decayed during 10-11th October 2024 solar storm too. However, Starlink did view at source ↗
Figure 7
Figure 7. Figure 7: Counting the number of Starlink satellites per shell altitude decaying (solid orange line) and raising view at source ↗
Figure 8
Figure 8. Figure 8: The distribution of Starlink satellite orbital altitude changes (left y-axis), along with the maximum view at source ↗
Figure 9
Figure 9. Figure 9: Color mesh in background represents the global atmospheric density at 550 km altitude, showing view at source ↗
Figure 10
Figure 10. Figure 10: Color mesh in background represents dynamics of spatiotemporal global atmospheric density view at source ↗
Figure 11
Figure 11. Figure 11: Altitude variation of OneWeb satellites at 1,200 km shows that the ‘W’ pattern nearly disappears. view at source ↗
Figure 12
Figure 12. Figure 12: Amazon Leo currently has 212 satellites in orbit (a) going through the orbit raise maneuver, (b) at view at source ↗
Figure 13
Figure 13. Figure 13: Time series of mean RTT per second (gray dots), and Exponentially Weighted Moving Average view at source ↗
Figure 14
Figure 14. Figure 14: Time series of mean RTT per second (gray dots), and Exponentially Weighted Moving Average view at source ↗
Figure 15
Figure 15. Figure 15: Zooming into day-wise latency pattern before the solar storm (gray lines) and during the solar view at source ↗
Figure 16
Figure 16. Figure 16: Zooming into day-wise latency pattern before the solar storm (gray lines) and during the solar view at source ↗
Figure 17
Figure 17. Figure 17: Comparing the pre-storm (green dashed line) and during solar superstorm (red solid line) latency view at source ↗
Figure 18
Figure 18. Figure 18: Comparing the pre-storm (green dashed line) and during October 2024 solar storm (red solid line) view at source ↗
Figure 19
Figure 19. Figure 19: Comparing the timeseries of individual latency observation (blue dots) from Frankfurt (c)-(d) during view at source ↗
Figure 20
Figure 20. Figure 20: Latency observations (blue dots) from Vancouver in panels (c)–(d) during the peak of the solar super view at source ↗
Figure 21
Figure 21. Figure 21: Packet loss in downlink (dashed green line) and uplink (solid red line) from four vantage points from view at source ↗
Figure 22
Figure 22. Figure 22: Packet loss in downlink (dashed green line) and uplink (solid red line) from two vantage points from view at source ↗
Figure 23
Figure 23. Figure 23: Comparing the pre-storm (dashed line) and during May 2024 solar superstorm (solid line) uplink (red) view at source ↗
Figure 24
Figure 24. Figure 24: Comparing the pre-storm (dashed line) and during October 2024 solar storm (solid line) uplink (red) view at source ↗
Figure 25
Figure 25. Figure 25: Country-wise available RIPE Atlas probes and shortlisted probes (i.e., not shifting RTTs in different view at source ↗
Figure 26
Figure 26. Figure 26: Overall throughput distribution of worldwide speed test results during (a)-(d) May 2024 solar view at source ↗
Figure 27
Figure 27. Figure 27: Distribution of region-wise throughput degradation from speed test results during (a)-(d) May 2024 view at source ↗
Figure 28
Figure 28. Figure 28: Overall normalized latency distribution of worldwide speed test results during (a)-(d) May 2024 view at source ↗
Figure 29
Figure 29. Figure 29: Distribution of region-wise latency inflation from speed test results during (a)-(d) May 2024 solar view at source ↗
Figure 30
Figure 30. Figure 30: Overall jitter distribution of worldwide speed test results during (a) May 2024 solar superstorm shows view at source ↗
Figure 31
Figure 31. Figure 31: Overall packet loss distribution of worldwide speed test results during (a)-(d) May 2024 solar super view at source ↗
Figure 32
Figure 32. Figure 32: Distribution of region-wise loss inflation from speed test results during (a)-(d) May 2024 solar view at source ↗
read the original abstract

The May 2024 solar superstorm highlighted the vulnerability of rapidly expanding low Earth orbit (LEO) satellite networks to severe space weather events. To systematically evaluate LEO network resilience, we introduce an open-source tool, CosmicDancePro. It enables a comprehensive analysis of the effects of solar storms in the LEO satellite network. It integrates real-world multimodal datasets, including space weather measurements from several satellites, upper-atmospheric density conditions from data-driven and high-fidelity physics-based models, and LEO satellite trajectory and LEO network measurement traces to quantify orbital decay driven by enhanced atmospheric density and network connectivity degradation. We utilize CosmicDancePro to analyze the Starlink constellation's behavior during two recent major solar storms. First, we identify the specific fleet management strategies Starlink adopts during the May 2024 solar superstorm and how they differ from its regular orbit-correction strategy. Second, we identify the mechanisms driving the previously unexplained 'W'-shaped altitude variation pattern across orbital planes of LEO constellations. Finally, our network-layer analysis quantifies the connectivity degradation during these storms, revealing transient disruptions that include repetitive short-lived outages, reconfiguration latency spikes above 500 ms, up to 60% increase in uplink loss, distorted diurnal latency patterns, and a 10+ Mbps drop in end-user data rates during storm peaks.

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

2 major / 2 minor

Summary. The manuscript introduces CosmicDancePro, an open-source tool integrating space weather data from satellites, data-driven and physics-based upper-atmospheric density models, TLE-based LEO trajectories, and network measurement traces. Applied to the Starlink constellation during the May 2024 solar superstorm, it claims to identify distinct fleet management strategies differing from regular orbit corrections, explain the mechanisms behind the 'W'-shaped altitude variation pattern across orbital planes, and quantify network degradations including repetitive short-lived outages, reconfiguration latency spikes above 500 ms, up to 60% uplink loss increase, distorted diurnal latency patterns, and >10 Mbps drops in end-user data rates.

Significance. If the multimodal fusion cleanly isolates storm-driven effects without major confounding, the work would offer timely, concrete metrics on LEO constellation vulnerability to space weather with direct implications for fleet operations and network design. The open-source tool and use of real-world multimodal datasets are strengths that support reproducibility and extension by the community.

major comments (2)
  1. [Fleet management strategies and 'W'-shaped altitude analysis] Fleet management and orbital decay analysis: the attribution of the 'W'-shaped altitude pattern and specific storm strategies to enhanced atmospheric density lacks any described ablation, baseline subtraction of non-storm reconfiguration maneuvers, or control for autonomous orbit-raising burns that Starlink performs routinely; without this, commanded delta-v cannot be ruled out as the source of the observed patterns.
  2. [Network connectivity degradation analysis] Network-layer quantification: the reported degradations (repetitive outages, >500 ms latency spikes, 60% uplink loss increase, 10+ Mbps rate drop) are presented without error bars, data exclusion criteria, or validation steps to separate storm effects from ground-segment or user-load variables, undermining the claim that these are cleanly storm-driven.
minor comments (2)
  1. [Abstract] Abstract: headline percentages and patterns are stated without reference to validation, uncertainty quantification, or sample sizes, reducing immediate assessability of robustness.
  2. [Methods] Notation and figures: ensure all density model inputs and TLE processing steps are explicitly defined with units and sources in the methods section for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments on our manuscript. These suggestions highlight important areas for strengthening the attribution of observed effects to space weather and for improving the rigor of our network analysis. We address each major comment below and will make corresponding revisions to the manuscript.

read point-by-point responses

  1. Referee: [Fleet management strategies and 'W'-shaped altitude analysis] Fleet management and orbital decay analysis: the attribution of the 'W'-shaped altitude pattern and specific storm strategies to enhanced atmospheric density lacks any described ablation, baseline subtraction of non-storm reconfiguration maneuvers, or control for autonomous orbit-raising burns that Starlink performs routinely; without this, commanded delta-v cannot be ruled out as the source of the observed patterns.

    Authors: We agree that the current version does not explicitly describe ablation studies or detailed baseline controls, which limits the strength of the attribution. In the revised manuscript we will add a new subsection that (1) performs baseline subtraction using TLE-derived trajectories from equivalent non-storm periods in the same orbital planes, (2) identifies and removes routine autonomous orbit-raising burns by cross-referencing historical Starlink maneuver patterns and delta-v statistics, and (3) conducts an ablation comparing density-driven decay predictions against a null model that includes only commanded maneuvers. These additions will demonstrate that the W-shaped pattern and the distinct fleet strategies cannot be explained by routine delta-v alone. revision: yes

  2. Referee: [Network connectivity degradation analysis] Network-layer quantification: the reported degradations (repetitive outages, >500 ms latency spikes, 60% uplink loss increase, 10+ Mbps rate drop) are presented without error bars, data exclusion criteria, or validation steps to separate storm effects from ground-segment or user-load variables, undermining the claim that these are cleanly storm-driven.

    Authors: The degradations were obtained by direct comparison of storm-peak traces against pre- and post-storm baselines from the same terminals. To address the referee's valid concern, the revised version will include (1) error bars computed from the standard deviation across multiple orbital planes and repeated storm events, (2) explicit data exclusion criteria (e.g., removal of intervals with documented ground-segment maintenance or anomalous user-load spikes), and (3) validation steps that correlate the timing and magnitude of each metric with independent space-weather indices while controlling for diurnal user-load patterns. These changes will more clearly isolate storm-driven contributions. revision: yes

Circularity Check

0 steps flagged

No significant circularity; analysis fuses external multimodal datasets and standard models

full rationale

The paper's core contribution is an open-source tool (CosmicDancePro) that ingests independent real-world inputs: space-weather satellite measurements, pre-existing data-driven and physics-based upper-atmosphere density models, TLE trajectory data, and network traces. It then performs observational attribution of orbital decay patterns and connectivity metrics during documented solar-storm intervals. No equation or procedure defines a quantity in terms of itself, renames a fitted parameter as a prediction, or relies on a self-citation chain whose validity is presupposed. The reported 'W'-shaped altitude behavior and network degradations are presented as outputs of the fusion process applied to external data, not as quantities forced by internal normalization or ansatz. The reader's assessment of score 1.0 is consistent with this self-contained structure.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the accuracy of pre-existing space-weather and atmospheric models plus the assumption that multimodal data fusion cleanly attributes observed decay and outages to solar-storm forcing.

axioms (1)
  • domain assumption Integrated space-weather, density-model, and network-trace datasets accurately isolate solar-storm effects on orbital decay and connectivity
    Invoked throughout the tool description and Starlink analysis sections of the abstract.

pith-pipeline@v0.9.0 · 5559 in / 1372 out tokens · 40943 ms · 2026-05-08T09:53:20.339948+00:00 · methodology

discussion (0)

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