Anomaly Analytics Overview

Slingshot’s Anomaly Analytics view uses Agatha AI, our AI Anomaly Detection and Characterization Engine, to identify unusual or unexpected satellite behaviors and characteristics using high-quality, multi-source space domain data.

April 7, 2026

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.

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.