Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction

Antonio Pagliaro, G. Cusumano, A. La Barbera, Valentina La Parola, Saverio Lombardi

Applied Sciences 2023 article cited by 11

Source Open-access PDF More in Physics & Astronomy

importance 5/10 ai assisted Astrophysics and Cosmic Phenomena gamma-ray astronomy stage: research

Problem

Imaging Atmospheric Cherenkov Telescope data is overwhelmingly dominated by cosmic-ray background, requiring efficient gamma/hadron separation and energy reconstruction.

Why it matters

Rejecting background is essential to discovering and characterizing very high-energy gamma-ray astrophysical sources.

What the AI did

Supervised machine learning methods, including Random Forest, were assessed and compared for gamma/hadron separation and energy reconstruction on simulated ASTRI Mini-Array data.

Results

not stated in source

Impact

ML methods were evaluated for event reconstruction on simulated ASTRI telescope data.

Record

Venue	Applied Sciences
Published	2023
Work type	article
Authors	Antonio Pagliaro, G. Cusumano, A. La Barbera, Valentina La Parola, Saverio Lombardi
Citations	11 (OpenAlex, at import time)
References	25
Model or method	Random Forest and other supervised ML methods
Organization	not stated in source
Primary source	https://doi.org/10.3390/app13148172
Open access	gold
Keywords	machine learning, Cherenkov telescopes, gamma-ray astronomy, event reconstruction, background rejection

Importance rating

5 / 10

real but narrower advance

Background rejection is essential for discovering and characterizing VHE gamma-ray sources, a specialized astrophysics subfield; no broad societal scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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