MagicTelescope: Simulated Gamma-Ray Telescope Data for Particle Classification

Monte Carlo generated data simulating the registration of high-energy gamma particles in a ground-based atmospheric Cherenkov gamma telescope. The dataset was created using the Corsika simulation program and belongs to 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 originates from the MAGIC telescope project.

Use Cases

• Classifying gamma-ray events from background cosmic rays based on Hillas parameters described in the summary.
• Training regression models to predict particle energy from Cherenkov shower image characteristics.
• Benchmarking tabular classification algorithms using simulated, physics-based feature sets.
• Analyzing patterns in simulated shower images for feature engineering in particle detection.

Strengths

• Data is generated via a documented Monte Carlo simulation program (Corsika), providing a controlled benchmark environment.
• The dataset is part of a published tabular data benchmark, suggesting a degree of curation and validation.
• The original source is the UCI Machine Learning Repository, a well-known and trusted archive.

Limitations

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

Provenance

Source

UCI Machine Learning Repository, donated by P. Savicky from the Institute of Computer Science, AS of CR, Czech Republic.

Collection Method

Generated by the Corsika Monte Carlo simulation program.