REVIEW 2 cited by
Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In recent years, Imaging Atmospheric Cherenkov Telescopes (IACTs) have discovered a rich diversity of very high energy (VHE, > 100 GeV) gamma-ray emitters in the sky. These instruments image Cherenkov light emitted by gamma-ray induced particle cascades in the atmosphere. Background from the much more numerous cosmic-ray cascades is efficiently reduced by considering the shape of the shower images, and the capability to reduce this background is one of the key aspects that determine the sensitivity of a IACT. In this work we apply a tree classification method to data from the High Energy Stereoscopic System (H.E.S.S.). We show the stability of the method and its capabilities to yield an improved background reduction compared to the H.E.S.S. Standard Analysis.
Forward citations
Cited by 2 Pith papers
-
Prospects for the Use of Photosensor Timing Information with Machine Learning Techniques in Background Rejection
Proposes feeding seven 2D histograms of waveform parameters into ML algorithms alongside integrated charge images to better reject background in IACT observations.
-
Machine Learning for Event Reconstruction in Imaging Atmospheric Cherenkov Telescopes
This review describes the IACT event reconstruction pipeline and the role of machine learning for classification and regression, highlighting timing features and ensemble methods as improvements over baseline approaches.
Discussion (0). Continue with ORCID to comment.