The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
S. Centro;C. Farnese;D. Gibin;G. Meng;L. Stanco;
2020
Abstract
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.| File | Dimensione | Formato | |
|---|---|---|---|
| 
									
										
										
										
										
											
												
												
												    
												
											
										
									
									
										
										
											2006.15052.pdf
										
																				
									
										
											 accesso aperto 
											Descrizione: Articolo
										 
									
									
									
										
											Tipologia:
											Published (Publisher's Version of Record)
										 
									
									
									
									
										
											Licenza:
											
											
												Creative commons
												
												
													
													
													
												
												
											
										 
									
									
										Dimensione
										2.73 MB
									 
									
										Formato
										Adobe PDF
									 
										
										
								 | 
								2.73 MB | Adobe PDF | Visualizza/Apri | 
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.




