Automating Daily Conjunction Data Screening and False Positive Filtering

The daily manual screening of hundreds of Conjunction Data Messages (CDMs) is a significant operational bottleneck for satellite operators. Analysts spend hours sifting through alerts, many of which are false positives caused by high covariance or poor-quality orbit determination. Automating this screening with a custom workflow directly reduces labor costs by 70-80% and compresses risk assessment from hours to minutes. The business value lies in freeing expert analysts to focus on high-fidelity threats and strategic planning, rather than repetitive data triage. This implementation requires integrating with data feeds from SOCRATES, CARA, or commercial SSA providers, and demands robust exception handling for data gaps or format changes.

Implementation centers on a LangGraph or custom Python orchestrator that sequences specialized agents for parsing, rule evaluation, and ML inference. The ML model, trained on historical analyst decisions, scores CDMs for 'actionability' based on features like covariance realism and miss distance uncertainty. Filtered alerts are pushed to a dashboard like Grafana or a custom ops UI with the original data and filtering rationale attached for traceability. Critical controls include a human-in-the-loop review gate for all filtered-out alerts to prevent missed threats, and continuous model monitoring for drift against analyst decisions. The architecture must be deployed within the operator's secure environment, integrating with existing mission control systems.

This pipeline automates the first, most labor-intensive layer of space traffic management: screening daily CDM feeds. It directly targets the operational bottleneck where analysts spend hours manually reviewing hundreds of low-probability events, many with high covariance or unrealistic miss distances. The business value is pure labor leverage, converting analyst time from repetitive screening to high-value risk assessment and response planning. Savings come from eliminating 70-80% of manual review load, compressing daily screening from hours to minutes, and reducing alert fatigue that leads to missed signals.

Implementation integrates with SOCRATES or CARA APIs for CDM ingestion and uses a rules engine (e.g., Drools) for initial filters like TLE age and covariance thresholds. A secondary agent runs a lightweight ML model (e.g., Scikit-learn) trained on historical false positives. The shortlist is pushed to a dashboard like Grafana or a custom React frontend. Critical controls include a human-in-the-loop review gate for all shortlisted events, immutable logging of all discards for audit, and continuous model performance monitoring to retrain on analyst overrides, ensuring the system adapts to changing debris environments.

The daily manual screening of hundreds of Conjunction Data Messages (CDMs) is a high-volume, repetitive bottleneck that consumes analyst time and risks alert fatigue from false positives. Automating this workflow directly reduces operational overhead by 80%, allowing human experts to focus on high-fidelity threats. The business case is clear: faster, more reliable risk triage improves decision velocity and satellite safety, while cutting labor costs associated with manual data sifting through services like SOCRATES or CARA.

Implementation follows a phased, three-tier architecture. Phase 1 builds the ingestion and normalization layer for TLE/OD data, integrating with existing SSA feeds. Phase 2 deploys the filtering logic, combining deterministic rules (e.g., minimum miss distance) with an ML model trained to identify high-covariance false alarms. Phase 3 adds the orchestration and observability layer, routing prioritized alerts to a dashboard like Elastic or Grafana, with full audit trails and exception handling for ambiguous cases requiring manual review.

This workflow automates the daily, labor-intensive task of sifting through hundreds of raw Conjunction Data Messages (CDMs) from services like SOCRATES or CARA. It directly targets the operational bottleneck of analyst alert fatigue by applying domain-specific rules and ML models to filter high-covariance events and other false positives. The savings come from reducing manual screening time by 70-80%, allowing human experts to focus on high-fidelity risks and accelerating daily operational readiness. Implementation requires integrating with CDM APIs, a rules engine for initial filtering, and a lightweight ML model for covariance realism scoring.

The architecture is built on an orchestrator, typically using a framework like LangGraph, that sequences the filtering stages. The first stage applies deterministic rules to discard CDMs with known false-positive signatures. The second uses a trained model to assess covariance realism, a key predictor of alert quality. Exceptions are routed to a human-in-the-loop review queue via a system like ServiceNow. The final shortlist is pushed to an operator dashboard and integrated with downstream risk scoring systems. Controls include daily performance monitoring of filter accuracy and periodic model retraining on newly labeled conjunction outcomes.

This workflow directly targets the operational bottleneck of analyst alert fatigue, where hundreds of daily CDMs from sources like SOCRATES or CARA must be manually triaged. By automating the initial screening, operators save 4-6 hours of skilled labor daily, reallocating analyst focus to high-fidelity risk assessment and maneuver planning. The architecture ingests raw CDMs via secure APIs, parses them against a normalized schema, and applies a multi-stage filter to isolate actionable alerts, preventing costly operational delays and reducing the risk of missed threats due to human oversight.

Implementation integrates with existing SSA platforms and mission control systems like AGI's STK or custom orbital propagators. The core agentic logic, built with LangGraph, sequences the filtering stages: first, a rules-based filter discards events with unrealistic covariance; second, a trained classifier model evaluates trajectory geometry and historical false-positive patterns; finally, a context agent checks against the satellite's current operational state. All decisions are logged with rationale in an immutable audit trail, and low-confidence events are routed to a secondary queue for periodic human review, ensuring continuous model feedback and operational governance.