Astronomical Time-Series Anomaly Detection Framework

Build a Python framework for detecting and classifying anomalies in astronomical time-series data from telescope observations. Implement unsupervised machine learning techniques like isolation forests and local outlier factor for identifying unusual celestial events. Create a modular system that can ingest multiple astronomical data formats, perform feature extraction, generate interactive visualizations, and produce detailed anomaly reports with statistical significance assessments.