Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector.
A. GavrikovV. CerroneA. SerafiniR. BrugneraA. GarfagniniM. GrassiB. JelminiL. LastrucciS. AielloG. AndronicoV. AntonelliA. BarresiD. BasilicoM. BerettaA. BergnoliM. BorghesiA. BrigattiR. BrunoA. BudanoB. CaccianigaA. CammiR. CarusoD. ChiesaC. ClementiS. DusiniA. FabbriG. FeliciF. FerraroM. G. GiammarchiN. GiugiceR. M. GuizzettiN. GuardoneC. LandiniI. LippiS. LoffredoL. LoiP. LombardiC. LombardoF. MantovaniS. M. MariA. MartiniL. MiramontiM. MontuschiM. NastasiD. OrestanoF. OrticaA. PaoloniE. PercalliF. PetrucciE. PrevitaliG. RanucciA. C. ReM. RedchuckB. RicciA. RomaniP. SaggeseG. SavaC. SirignanoM. SistiL. StancoE. Stanescu FarillaV. StratiM. D. C. TorriA. TriossiC. TuvéC. VenettacciG. VerdeL. VotanoPublished in: CoRR (2024)
Keyphrases
- machine learning
- learning algorithm
- pattern recognition
- machine learning methods
- information extraction
- semi supervised learning
- feature selection
- active learning
- learning problems
- x ray
- knowledge acquisition
- inductive learning
- learning tasks
- machine learning algorithms
- knowledge representation
- mathematical model
- artificial intelligence
- data mining
- kernel methods
- explanation based learning
- selection strategy
- electron beam
- electron microscopy
- supervised machine learning
- real time
- detection method
- detection algorithm
- supervised learning
- support vector machine
- knowledge discovery
- data analysis
- computer science
- neural network