Asteroids and Solar Storms, One Dashboard: AI-Powered Tracking and Real-Time Space Weather Could Save Satellites and Billions

Operationally, a Kp 6 storm is not just a number. It is a signal to throttle attitude control, reschedule sensitive instrument modes, harden GNSS timing, and reroute polar flights to avoid HF radio outages. Every 10–30 minutes of earlier warning can mean avoiding a non‑critical maneuver that burns propellant or a mode change that interrupts service. On the asteroid side, rapid confirmation of a newly detected object—hours, not days—can drastically cut the size of the orbit uncertainty cone and prevent over‑tasking of expensive follow‑up telescope time.

Consider the week of August 17: 87 NEOs in the NASA feed, with one at ≈0.77 lunar distances. In the same 10‑day span, NASA’s DONKI system logged more than 20 CME alerts and a moderate geomagnetic storm (Kp 6). A fused dashboard would tie these alerts to asset‑level playbooks—"bias the momentum wheels," "switch to lower‑rate TLE updates," "pause star tracker calibrations"—quantifying the cost of each action and recommending the minimum‑cost plan that meets risk limits.

The economic stakes are real: each unnecessary GEO maneuver can cost days of station‑keeping budget over a spacecraft’s life; a single polar reroute can add tens of thousands of dollars in fuel and time; a mis‑timed GNSS anomaly can cascade into downstream user costs. The goal isn’t eliminating risk; it is paying only for the risk that actually arrives.

NEOs are asteroids and comets whose orbits bring them near Earth. Most are small and harmless, but a fraction are large enough to matter; their orbits are refined with each observation. Space weather encompasses solar flares, CMEs, high‑speed streams, and energetic particles that buffet Earth’s magnetosphere and ionosphere, disturbing satellites, radio links, and power grids.

Two kinds of uncertainty dominate operations. For asteroids, orbit solutions carry statistical covariances that can expand quickly if follow‑up is delayed. Small non‑gravitational forces—chiefly the Yarkovsky effect—add subtle drift over years. For solar storms, forecasts blend coronagraph imagery, in‑situ solar wind measurements, and magnetospheric indices (Kp, Dst, AE). Operators care about lead time (minutes to days), intensity (e.g., Kp 5–9), and duration.

The promise of a fused dashboard is not just one pane of glass, but shared probability language: comparable risk scores that make east‑coast grid operators, GEO satellite controllers, and airline dispatchers react consistently to the same solar and asteroid context.

Faster asteroid‑confirmation pipelines mean fewer false alarms, less wasted telescope time, and earlier orbit convergence. For constellation operators, avoiding even a handful of unnecessary avoidance burns per year preserves station‑keeping budget and extends service life. On the space‑weather side, aligning alerts to operational thresholds—e.g., Kp ≥ 5 triggers instrument hardening, Kp ≥ 7 triggers GEO safe‑mode prep—reduces on‑orbit anomalies and protects ground infrastructure.

Risk translates directly to cost: avoided outage minutes (SLA credits), avoided propellant (capacity to sell more service), avoided reroutes (fuel/time), and avoided equipment damage (insurer claims). When an integrated system measures uncertainty and ties actions to explicit thresholds and playbooks, it unlocks quantified return on investment: you can simulate what would have happened with and without the dashboard and count the dollars.

According to Deep learning-assisted near-Earth asteroid tracking in astronomical images, researchers built a learning‑assisted pipeline tuned for messy survey data—variable PSFs, streaked backgrounds, and fast movers—reducing false positives and operator workload. According to Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect, a neural operator accelerates thermal modeling so those subtle forces can be incorporated sooner into orbit updates.

According to Adaptive Detection of Fast Moving Celestial Objects Using a Mixture of Experts and Physical-Inspired Neural Network, a physics‑informed mixture‑of‑experts design encodes telescope PSF and observation mode, improving generalization across different survey cameras and tracking strategies—critical when a new survey (or weather) changes image statistics and can inflate false alarms.

On the operations side, A Markov Decision Process Framework for Early Maneuver Decisions in Satellite Collision Avoidance formalizes satellite collision‑avoidance decisions using probability of collision approximations and explicit propellant cost models, enabling policies that balance fuel versus risk under uncertainty. According to Prediction of Solar Energetic Events Impacting Space Weather Conditions, forecasting techniques for solar energetic events can translate coronagraph and in‑situ data into probabilistic warnings, which a dashboard can convert into targeted operational actions.

On August 9, NASA issued a geomagnetic storm alert: Kp reached 6 (moderate). Between August 3–14, DONKI registered over twenty CME alerts. Meanwhile, the NEO feed for August 17–24 listed 87 objects, with 2025 PY1 passing at ≈0.00197 AU (~0.77 lunar distances). A fused dashboard would have:

- Pulled real‑time solar wind and CME alerts to estimate Kp and Dst trajectories, offering 15–60 minutes of lead time from L1 monitors for geomagnetic onset and several hours to days from CME propagation models.

- Overlayed satellite orbits and power‑grid geomagnetic vulnerability maps, auto‑generating playbooks: defer non‑critical attitude calibrations; prepare GEO safe‑mode criteria if Kp ≥ 7 persists; increase orbit determination cadence for LEO.

- Prioritized follow‑up for new NEOs based on uncertainty growth, solar elongation, and observatory availability, minimizing duplicate tasking while cutting orbit covariance volumes.

The result: fewer unnecessary maneuvers during the storm window, fewer on‑orbit anomalies, and less wasted telescope time. The same tools support planetary defense drills by showing which follow‑ups shrink uncertainty fastest and which sensors add the most value per minute of observing time.

Asteroids

- Discovery and astrometry: survey pipelines (Pan‑STARRS, ATLAS, LSST/Vera Rubin Observatory, and—soon—NEO Surveyor) produce detections and submit measurements to the Minor Planet Center (MPC). JPL’s CNEOS ingests MPC data, maintains orbit solutions, and publishes close approaches and Sentry risk analyses.

- Early screening: Scout (at JPL) assesses impact probability for new objects within minutes to hours as observations arrive; Sentry performs deeper, continuous risk analysis over extended windows.

- Latency targets: initial orbit or impact screening within ≤60 minutes of first digestible tracklet; follow‑up tasking within 1–3 hours for fast movers; routine orbit updates within 6–12 hours as new astrometry lands.

Space weather

- Observations: L1 monitors (ACE/DSCOVR) deliver solar wind and IMF data with ≈1–5 minute cadence, offering ≈15–60 minutes lead time to geomagnetic impact; STEREO and SOHO/GOES coronagraphs feed CME kinematics; Parker Solar Probe and Solar Orbiter add heliospheric context.

- Forecasts: NOAA SWPC is the operational authority; NASA’s DONKI/CCMC provides research and modeling alerts. Operators consume Kp, Dst, AE indices and radiation dose proxies.

- Latency targets: hazard advisories to operators ≤5 minutes from Kp threshold crossings; CME arrival windows updated hourly; radiation alerts pushed within minutes of in‑situ detections.

Access and formats

- CNEOS/SBDB/close‑approach APIs: JSON/CSV; rate‑limited but openly accessible.

- Space weather alerts and solar wind feeds: JSON/RSS/telemetry; operator dashboards should ingest both operational and research feeds, annotating provenance and latency.

Pan‑STARRS: Wide‑field optical survey with nightly cadence across accessible sky; single‑visit limiting magnitudes typically around the low‑20s (conditions dependent). Strong track record on main‑belt and NEO discovery.

ATLAS: All‑sky rapid‑cadence system optimized for smaller, nearer objects with shorter warning times; shallower limiting magnitudes than larger surveys but higher revisit rates enable rapid recovery.

LSST/Vera Rubin Observatory (commissioning phase): Projected single‑visit limiting magnitude in the mid‑20s with high revisit frequency and a real‑time alert stream designed for minute‑scale distribution. Its difference imaging pipeline will be a major source of fast NEO candidates.

NEO Surveyor (infrared): Space‑based IR detection targets low‑albedo NEOs difficult in visible light. While not directly comparable to optical magnitudes, IR sensitivity translates into improved completeness for potentially hazardous NEOs at relevant size ranges. Together, these systems span depth (LSST), cadence (ATLAS), sky coverage (Pan‑STARRS), and spectral domain (NEO Surveyor) to close discovery gaps.

The discovery pipeline begins with image differencing and streak detection, then linking detections into tracklets and initial orbits. False positives arise from detector artifacts, cosmic rays, satellites, airplane trails, and transient optics. Researchers have developed learning‑assisted filters that reject artifacts while keeping fast movers, as reported in Deep learning-assisted near-Earth asteroid tracking in astronomical images. The physics‑informed mixture‑of‑experts design in Adaptive Detection of Fast Moving Celestial Objects Using a Mixture of Experts and Physical-Inspired Neural Network improves robustness across PSFs and tracking modes—reducing retraining overhead when instrument configurations change.

Downstream, JPL’s Scout provides rapid impact screening on scant data, flagging follow‑ups to shrink uncertainties; Sentry incorporates fuller arc data and small‑force models (e.g., Yarkovsky) to stabilize long‑term risk. A fused dashboard should mirror these workflows: score candidates by uncertainty growth, visibility windows, and cost of follow‑up, then auto‑generate observing requests and de‑duplicate across partners.

Orbit solutions carry covariance matrices that propagate with process noise. Small non‑gravitational accelerations (Yarkovsky) introduce secular drifts: da/dt ∝ cos(γ) × thermal parameters, where γ is obliquity. Classical thermophysical models are accurate but slow; according to Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect, neural operators approximate the mapping from material/rotation parameters to thermal forces orders of magnitude faster—useful for rapid screening when an object first lands on a risk table.

Impact probability metrics blend orbit state uncertainty, gravitational keyholes, and planetary ephemerides. Early arcs inflate uncertainty and can transiently overstate risk; additional astrometry typically collapses probability mass away from Earth. The dashboard should expose: covariance volume over time, sensitivity to assumed thermal parameters, and a conservative error budget that brackets unknowns (e.g., albedo, thermal inertia), preventing over‑confident recommendations.

To unify asteroid and space‑weather risks, fuse calibrated probabilities rather than raw scores. Example structure:

- Asteroid component: Pimpact (time‑windowed) from CNEOS/Sentry or Scout; uncertainty bands from covariance propagation with optional Yarkovsky priors; value exposure by asset category (e.g., reentry casualty expectation, operator obligations).

- Space‑weather component: Phazard(t, region) from Kp/Dst/AE forecasts and CME arrival windows; asset‑specific fragility curves (e.g., reaction wheel trips vs Kp, HF radio outage vs polar cap absorption).

- Decision function: compute expected loss E[L] = Σ_assets ∫ P(event) × Vulnerability(asset|event) × Consequence(asset) dt, and recommend actions that minimize ΔE[L] − Cost(action), subject to constraints (e.g., propellant budgets, service‑level agreements). Display logit‑space combinations to avoid double counting and include provenance weights for each data source.

This approach preserves interpretability: each sub‑probability is auditable, and operators can adjust weights or thresholds per policy.

Indices

- Kp (0–9): planetary geomagnetic activity; operators often set action thresholds at Kp ≥ 5 (instrument hardening) and Kp ≥ 7 (prepare safe modes for GEO and sensitive sensors).

- Dst (nT): storm strength in the ring current; thresholds like Dst ≤ −100 nT (strong) may trigger enhanced anomaly watchlists.

- AE (nT): auroral electrojet; used for polar aviation and HF radio planning.

Lead time and latency

- L1 solar wind gives ≈15–60 minutes before magnetospheric impact—enough to gate attitude modes or delay burns.

- CME modeling yields hours‑to‑days heads‑up but with wider uncertainty, refined as coronagraph fits converge.

Operational requirement: deliver alerts to human operators and automation within ≤5 minutes of threshold crossing; present confidence intervals and recommended actions with costs and expected benefit.

Ingestion layer

- APIs: REST/JSON for CNEOS, SBDB, close‑approach data; JSON/RSS/telemetry for DONKI and solar wind. Support CSV exports for audit. Use message buses for low‑latency fan‑out.

- Standards: CCSDS for telemetry; consistent JSON schemas for alerts; time synchronization via PTP/NTP with GPS holdover.

Compute and latency

- Stream processing for sub‑minute alerts; batch refinement every 10–60 minutes. Reserve GPU for ML inference (image filtering, neural operators). Target end‑to‑end alert latency ≤60 seconds for space‑weather indices and ≤60 minutes from first asteroid tracklet to preliminary orbit/impact screen.

Data governance

- Provenance tags on every metric; immutability of raw data; role‑based access controls. Keep research and operational feeds separated but co‑visualized with clear labeling. Provide a public API for non‑safety‑critical outputs and private channels for operational customers.

Who acts on alerts?

- Satellite operators (LEO/MEO/GEO): maneuver timing, mode changes, payload scheduling.

- GNSS providers and timing networks: integrity flags, differential corrections, storm‑mode operations.

- Power grid operators: GIC mitigation, transformer loading strategies, reactive power reserves.

- Airlines (polar routes): HF/VHF planning, reroutes, fuel and crew replans.

- Insurers/reinsurers: dynamic risk pricing, claims triage during events.

SOP elements

- Clear decision authority, escalation paths, and pre‑approved playbooks per threshold. Human‑in‑the‑loop gates on high‑impact actions.

Policy and coordination

- Align with international bodies (SMPAG, IAWN, UN COPUOS) for planetary‑defense messaging and exercises. Use consistent public‑facing language to avoid mixed signals during rare but high‑profile events.

Chelyabinsk (2013): a ~20‑meter meteoroid exploded over Russia, injuring ~1,600 people and causing damage widely estimated around a billion dollars. While ultra‑small objects are hard to see far in advance, faster confirmation and follow‑up pipelines improve warning time and public messaging when objects are detected inbound.

Hydro‑Québec (1989): a geomagnetic storm induced currents that tripped protection, leading to a nine‑hour blackout affecting millions; economic costs ran into the hundreds of millions. Earlier, clearer warnings tied to grid‑specific thresholds could have enabled more aggressive GIC mitigation.

Halloween storms (2003): strong storms disrupted satellites, GPS accuracy, and HF communications over days. Sector‑specific actions—duty‑cycle reductions for sensitive hardware, dynamic GNSS integrity flags, targeted aviation reroutes—can materially reduce losses. A fused dashboard quantifies these trades ahead of time so operators trigger only what pays off.

Commercial SSA and operations providers already cover parts of the stack: LeoLabs (LEO tracking and conjunction data), ExoAnalytic (optical surveillance), Spire (GNSS‑RO and space weather data), Slingshot Aerospace (space traffic), and AGI/Ansys (STK/Sigma for analysis). A fused dashboard should integrate their feeds where available, not displace them. The value lies in risk fusion, playbooks, and quantified economics across asteroid and solar domains, while preserving vendor neutrality and auditability.

Because recommendations can trigger real operations, the dashboard must be treated as safety‑adjacent infrastructure. Priorities include: signed and reproducible containers; mutual TLS and hardware‑backed key storage; zero‑trust segmentation; source‑of‑truth cross‑checks (e.g., duplicate ingest from independent APIs); data diodes or read‑only relays from operations centers; and disaster‑recovery runbooks. Supply‑chain risks—tampered ML models or packages—are mitigated with SBOMs, signature verification, and model‑card audits. During solar storms, expect intermittent comms: cache the last good ephemerides and model outputs locally on satellites and ground segments.

Onboard autonomy is best for sub‑minute responses (e.g., radiation spikes, momentum management), while ground fusion excels at multi‑sensor uncertainty and economics. The dashboard should publish maneuver windows and constraints that flight software can honor autonomously when contact is lost. For conjunctions and storm windows that straddle passes, operators can upload policy envelopes (e.g., do‑not‑maneuver periods, rate limits) in advance. The MDP framework in A Markov Decision Process Framework for Early Maneuver Decisions in Satellite Collision Avoidance can inform such envelopes by quantifying propellant‑versus‑risk trade‑offs under data latency.

NEOs are rare but consequential; orbital debris and micrometeoroids create persistent background risk. The dashboard should:

- Keep planetary‑defense alerts isolated and auditable, with separate severity scales and public‑communications templates.

- Present debris and micrometeoroid flux as continuous baselines, tying to shielding and operational constraints.

- Prioritize operator attention to events that change decisions today: e.g., Kp rising above flight thresholds; a new NEO on a potential impact corridor; a conjunction crossing a PoC threshold. Everything else updates trend dashboards.

Model design

- Image pipelines: combine physics‑informed inputs (PSF, tracking mode) with CNN/Transformer backbones and mixture‑of‑experts gating, as described in Adaptive Detection of Fast Moving Celestial Objects Using a Mixture of Experts and Physical-Inspired Neural Network. Keep a classical Hough/streak detector in parallel as a check on domain shift.

- Yarkovsky: use neural operators to accelerate thermal‑force estimates, per Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect, but validate against thermophysical baselines.

Benchmarks and metrics

- Track precision/recall, false‑positive rate per megapixel, calibration (Brier score), and robustness under PSF drift and sky background changes. Maintain drift dashboards that compare model confidence to human‑verified outcomes.

Explainability and safety

- Require saliency overlays and PSF attribution for image detections; publish model cards with training data provenance, augmentation policies, and known failure modes. Human‑in‑the‑loop gates for high‑impact recommendations; rollback to classical pipelines if drift alarms trigger.

Backtests

- Replay historical storms (e.g., 2003, 2015) and NEO discovery arcs; measure lead time, alert accuracy, and avoided actions relative to operator thresholds. Use CNEOS close‑approach data to quantify orbit‑uncertainty collapse with and without accelerated follow‑up.

Red‑team exercises

- Inject synthetic false positives (asteroid streak impostors; spurious Kp spikes) and confirm the system fails safe. Stress‑test latency budgets during simulated solar radio blackouts.

Continuous monitoring

- KPIs: alert latency (p50/p90), precision/recall for event classification, calibration error, operator adoption (actions taken vs recommended), cost savings (propellant saved, outage minutes avoided, reroute costs avoided), and post‑mortem timeliness. Establish clear human‑override thresholds when model‑based recommendations exceed confidence bounds.