MagicTelescope: Monte Carlo Simulated Gamma-Ray Shower Images for Classification

MagicTelescope is a dataset used in a tabular data benchmark, originally from the UCI repository. The data are Monte Carlo generated to simulate the registration of high-energy gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the imaging technique. It was created by the MAGIC project and donated by P. Savicky of the Institute of Computer Science, AS of CR, Czech Republic.

Use Cases

• Binary classification of gamma-ray signals from cosmic-ray background based on simulated shower image parameters.
• Feature engineering and analysis of ellipse characteristics (Hillas parameters) derived from Cherenkov photon patterns.
• Benchmarking regression or classification algorithms on numerical features from a physics simulation context.

Strengths

• Dataset is part of a published tabular data benchmark, suggesting a standardized format for comparison.
• Data generation is described as using the established CORSIKA Monte Carlo simulation code.
• Original source is the authoritative UCI Machine Learning Repository.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale modeling.
• 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

Monte Carlo simulation using the CORSIKA code.