An edge-detection neural network combined with agglomerative pre-clustering improves cluster reconstruction accuracy in simulated CALIFA calorimeter events by about 34% relative to the standard R3B algorithm.
Bertini, R3BRoot, simulation and analysis framework for the R3B experiment at FAIR, in: Journal of Physics: Conference Series, V ol
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
physics.ins-det 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B
An edge-detection neural network combined with agglomerative pre-clustering improves cluster reconstruction accuracy in simulated CALIFA calorimeter events by about 34% relative to the standard R3B algorithm.