By combining data from the Slingshot Global Sensor Network (SGSN), Seradata, and other proprietary and public sources, Agatha AI helps detect deviations that may be difficult to identify through traditional single-source analysis alone.
What The Analytics Provides
Out of Family Analytics:
Continuously monitors satellite behavior
Identifies statistically significant deviations
Ranks spacecraft by “Interest Factor”
Surfaces high-priority objects for investigation
Provides explainable metrics to support operational decisions
Operational Cadence
AI Anomaly Detection runs on a scheduled cadence:
Frequency: Daily
Lookback Window:
LEO: 45 days
GEO: 14 days
Coverage: Focused on satellites launched after 2010
Understanding the Interest Factor
The Interest Factor is a relative measure of how unusual a satellite’s recent behavior is compared to its baseline.
Values range from 0 to 1
Higher values indicate greater deviation
Used to rank satellites for investigation
Important:
The Interest Factor is comparative — it highlights objects that stand out relative to peers and historical behavior.
Satellite Interest Factors Chart
The primary visualization displays satellites plotted by NORAD ID and Interest Factor score.
Each point represents a satellite
Color coding reflects relative deviation
Date of run is displayed above the chart
Filtering Options
You can refine results using:
Orbit Regime Toggle: LEO or GEO
Date Selector: Select analysis date
Filter Icon: Apply additional filters
Top Five Spacecraft of Interest
The system highlights the top-ranked spacecraft based on Interest Factor.
For each object, you’ll see:
Name
Country
Orbit regime
Launch year
Key contributing factors (e.g., orbital motion, brightness variability)
Investigation Panel
Selecting a spacecraft opens the Investigation View.
This view includes:
Satellite metadata
Country
Orbit regime
Launch year
Interest Factor
Compare – Compare with other spacecraft
View Metadata – Access detailed satellite information
View in 3D – View object in Portal’s Digital Space Twin.
Investigation Metrics
Each spacecraft includes detailed expandable analytics panels in the Investigation tab.
At a high level, the Investigation metrics help users answer questions such as:
What type of behavior is making this object notable?
Is the anomaly related to orbital motion, brightness, proximity, or position-keeping?
Does the object appear to be behaving differently from its peers or from its own recent history?
The metrics shown can vary depending on whether the selected object is in LEO or GEO.
LEO metrics
For LEO objects, Investigation metrics are typically focused on behaviors such as:
Semi-Major Axis, to understand changes in orbital altitude of the object
Close Approach, to assess proximity to other objects
Apparent Magnitude, to evaluate changes in observed brightness over time
comparisons to Cluster, Shell, and Ring reference populations
These metrics are most useful for understanding whether a LEO object is behaving differently from similar objects in nearby orbital environments.
GEO metrics
For GEO objects, Investigation metrics are typically focused on behaviors such as:
Longitude Drift Rate, to understand east-west motion
Longitude, to show position along the geostationary arc
Inclination, to track changes in orbital plane
Close Approach, where relevant, to assess proximity to nearby objects
brightness-related metrics such as Apparent Magnitude when available
These metrics are most useful for understanding position-keeping, relocation, and other changes in geostationary behavior.
Not sure what the charts mean? Use this guide to help you interpret the analytic charts. View more
Using Compare
The comparison view is important because our anomaly detection engine isn’t just about seeing a pattern — it is about seeing whether that pattern is unusual relative to the population. Comparison is what turns a chart from an interesting trend into a more meaningful behavioral signal.
Use this to:
Identify relative behavior differences
Validate anomaly severity
Contextualize activity within a peer group
Exporting Data
Each investigation metric includes export options as PNGs.
Best Practices
For effective use:
Start with the Top Five Spacecraft
Investigate sustained Interest Factor spikes
Cross-reference brightness with altitude changes
Compare to other satellites in family
Use metadata to contextualize behavior
When to Escalate
Consider deeper analysis if:
Interest Factor .01 (For Leo)
Sustained altitude or drift changes
Repeated close approaches
Combined brightness and orbital anomalies
Powered by Agatha AI
AI Anomaly Detection is powered by Slingshot’s Agatha AI framework, enabling multi-source fusion and built-in explainability.
Next Steps
After identifying an anomaly, you can:
Monitor the object in Operations
Submit a tracking request
Compare against similar satellites
Export data for external reporting
You’re Ready to Investigate 🚀
AI Anomaly Detection gives you advanced, explainable analytics to surface and investigate unusual satellite behavior with confidence.