MagicTelescope: Simulated Gamma-Ray Telescope Events for Classification

Monte Carlo generated data simulating the registration of high-energy gamma particles in a ground-based atmospheric Cherenkov telescope. The dataset was created using the Corsika simulation program and is part of a benchmark for regression on numerical features. It was donated by P. Savicky from the Institute of Computer Science, AS of CR, Czech Republic, and the original source is the UCI Machine Learning Repository.

Use Cases

• Classifying gamma-ray signals from hadronic background events based on Hillas parameters.
• Training regression models to predict particle energy from Cherenkov shower image characteristics.
• Benchmarking tabular machine learning algorithms on numerical feature data from a physics simulation.

Strengths

• Data is generated via a documented Monte Carlo simulation (Corsika), providing a controlled benchmark environment.
• The dataset is part of a published benchmark (tabular-benchmark), suggesting a standardized transformation for comparison.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

UCI Machine Learning Repository, originally from the MAGIC (Major Atmospheric Gamma Imaging Cherenkov) Telescope project.

Collection Method

Generated by the Corsika Monte Carlo simulation program.