02061nam0 22004693i 450 VAN0028605220250404104616.862N978303143583620250203d2023 |0itac50 baengCH|||| |||||i e bcrDomain Generalization with Machine Learning in the NOvA ExperimentDoctoral Thesis accepted by the University of Virginia, USAAndrew T. C. SuttonChamSpringer2023xi, 170 p.ill.24 cm001VAN001041932001 Springer thesesrecognizing outstanding Ph.D. research210 BerlinSpringer2010-00A79 (77-XX)Physics [MSC 2020]VANC023182MF68-XXComputer science [MSC 2020]VANC019670MF68TxxArtificial intelligence [MSC 2020]VANC021266MF81V35Nuclear physics [MSC 2020]VANC023270MF3-flavor analysisKW:KAdversarial domain generalizationKW:KEvent reconstructionKW:KMachine learning in HEPKW:KNOvA experimentKW:KNeutrino OscillationKW:KParticle identificationKW:KPhysics beyond the Standard ModelKW:KCHChamVANL001889SuttonAndrew T. C.VANV2400431437616Springer <editore>VANV108073650ITSOL20250411RICAhttps://doi.org/10.1007/978-3-031-43583-6E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN00286052BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08DLOAD e-Book 10518 08eMF10518 20250212 Domain Generalization with Machine Learning in the NOvA Experiment3598280UNICAMPANIA