LEADER 02061nam0 22004693i 450 001 VAN00286052 005 20250404104616.862 017 70$2N$a9783031435836 100 $a20250203d2023 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 181 $ai$b e 182 $ab 183 $acr 200 1 $aDomain Generalization with Machine Learning in the NOvA Experiment$eDoctoral Thesis accepted by the University of Virginia, USA$fAndrew T. C. Sutton 210 $aCham$cSpringer$d2023 215 $axi, 170 p.$cill.$d24 cm 410 1$1001VAN00104193$12001 $aSpringer theses$erecognizing outstanding Ph.D. research$1210 $aBerlin$cSpringer$d2010- 606 $a00A79 (77-XX)$xPhysics [MSC 2020]$3VANC023182$2MF 606 $a68-XX$xComputer science [MSC 2020]$3VANC019670$2MF 606 $a68Txx$xArtificial intelligence [MSC 2020]$3VANC021266$2MF 606 $a81V35$xNuclear physics [MSC 2020]$3VANC023270$2MF 610 $a3-flavor analysis$9KW:K 610 $aAdversarial domain generalization$9KW:K 610 $aEvent reconstruction$9KW:K 610 $aMachine learning in HEP$9KW:K 610 $aNOvA experiment$9KW:K 610 $aNeutrino Oscillation$9KW:K 610 $aParticle identification$9KW:K 610 $aPhysics beyond the Standard Model$9KW:K 620 $aCH$dCham$3VANL001889 700 1$aSutton$bAndrew T. C.$3VANV240043$01437616 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20250411$gRICA 856 4 $uhttps://doi.org/10.1007/978-3-031-43583-6$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN00286052 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08DLOAD e-Book 10518 $e08eMF10518 20250212 996 $aDomain Generalization with Machine Learning in the NOvA Experiment$93598280 997 $aUNICAMPANIA