AI-Powered SSA: Anomaly Detection with Gemma and Explainable AI

The increasing congestion of orbital space necessitates advanced Space Situational Awareness (SSA) capabilities to identify anomalous object behavior, predict potential collisions, obtain insights into mission profiles, and assess potential threats. This challenge focuses on developing an intelligent system that uses synthetic or real-world space object telemetry to detect and categorize anomalous events. Participants will design, implement, and evaluate a machine learning pipeline capable of identifying unusual orbital maneuvers, conjunction events, or non-standard satellite operations. The system should go beyond simple detection by leveraging large language models to provide actionable, explainable insights into the nature and potential implications of detected anomalies, crucial for both civil and defense space tracking efforts. The solution will integrate a robust anomaly detection model, potentially built with or informed by Gemma 2 principles, processing diverse sensor data (e.g., TLEs, radar, optical observations). Furthermore, it will utilize the Langroid framework to build an interactive agent that queries the anomaly detection system and uses the Anthropic API to generate rich, context-aware explanations for identified anomalies. This will enable human operators to quickly understand complex situations and make informed decisions, directly supporting enhanced Space Situational Awareness and threat assessment capabilities.

• Master the principles of Space Situational Awareness (SSA), including orbital mechanics, conjunction analysis, and space debris tracking, to inform anomaly detection model design and data interpretation.

• Implement advanced time-series anomaly detection algorithms (e.g., Isolation Forest, Autoencoders, LSTM-based anomaly detection) using Python libraries like `scikit-learn` and `TensorFlow` or `PyTorch` on simulated or publicly available satellite telemetry.

• Design a feature engineering pipeline for satellite orbital parameters (e.g., semi-major axis, eccentricity, inclination, orbital period change rates) to optimize anomaly detection model performance using `pandas` and `numpy`.

• Build and fine-tune a model inspired by Gemma 2's architecture and capabilities to classify and explain observed changes in satellite behavior, potentially using techniques like few-shot learning or instruction tuning with synthetic anomaly descriptions.

• Develop an interactive conversational agent using the `Langroid` framework that can query the anomaly detection system, filter results, and request further analysis based on user input, acting as an intelligent interface.

• Integrate the Anthropic API to generate detailed, human-readable explanations of detected anomalies, including potential causes, implications, and recommended actions, by providing context from the anomaly detection model's output.

• Optimize the end-to-end system for low-latency anomaly detection and explanation generation, considering the constraints of real-time SSA operations and data processing.

• Evaluate the performance of the anomaly detection system using metrics such as Precision, Recall, F1-score for classification, and perform qualitative assessment for the generated explanations' relevance and actionability.