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Tremblay,$d2007. 215 $a1 online resource 225 1 $aClassiques des sciences sociales 410 0$aClassiques des sciences sociales. 517 $aL'amour et la haine 606 $aSociology$xHistory 615 0$aSociology$xHistory. 676 $a301.09 700 $aJanet$b Pierre$0163042 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910131228703321 996 $aL'amour et la haine$93909728 997 $aUNINA LEADER 01748nas 22004691- 450 001 996336096103316 005 20240130213016.0 035 $a(CKB)963017986515 035 $a(CONSER)---76200510- 035 $a(EXLCZ)99963017986515 100 $a20751027b19552003 --- a 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 04$aThe Australian science teachers' journal 210 $aMelbourne$cAustralian Science Teachers' Association 215 $a1 online resource 300 $aRefereed/Peer-reviewed 311 08$aPrint version: The Australian science teachers' journal. 0045-0855 (DLC) 76200510 (OCoLC)1737884 531 0 $aAust. sci. teach. j. 606 $aScience$xStudy and teaching$zAustralia$vPeriodicals 606 $aSciences$xÉtude et enseignement$zAustralie$vPériodiques 606 $aScience$xStudy and teaching$2fast$3(OCoLC)fst01108387 606 $aEnseignement des sciences$2rasuqam 607 $aAustralia$2fast$1https://id.oclc.org/worldcat/entity/E39QbtfRv8PPH7gCqhkJ8DK8bM 607 $aAustralie$2rasuqam 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 608 $aRessource Internet (Descripteur de forme)$2rasuqam 608 $aPériodique électronique (Descripteur de forme)$2rasuqam 615 0$aScience$xStudy and teaching 615 6$aSciences$xÉtude et enseignement 615 7$aScience$xStudy and teaching. 615 7$aEnseignement des sciences. 676 $a507 712 02$aAustralian Science Teachers' Association. 906 $aJOURNAL 912 $a996336096103316 920 $aexl_impl conversion 996 $aThe Australian science teachers' journal$92337058 997 $aUNISA LEADER 03910nam 22007095 450 001 9910760294903321 005 20251009094728.0 010 $a3-031-43583-4 024 7 $a10.1007/978-3-031-43583-6 035 $a(MiAaPQ)EBC30876556 035 $a(Au-PeEL)EBL30876556 035 $a(DE-He213)978-3-031-43583-6 035 $a(CKB)28804789100041 035 $a(EXLCZ)9928804789100041 100 $a20231108d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDomain Generalization with Machine Learning in the NOvA Experiment /$fby Andrew T.C. Sutton 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (174 pages) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 311 08$aPrint version: Sutton, Andrew T. C. Domain Generalization with Machine Learning in the NOvA Experiment Cham : Springer International Publishing AG,c2023 9783031435829 327 $aChapter 1: Neutrinos: A Desperate Remedy -- Chapter 2. A Review of Neutrino Physics -- Chapter 3. The NOvA Experiment -- Chapter 4. Event Reconstruction -- Chapter 5. The 3-Flavor Analysis -- Chapter 6. A Long Short-Term Memory Neural Network -- Chapter 7. Domain Generalization by Adversarial Training -- Chapter 8. Conclusion. 330 $aThis thesis presents significant advances in the use of neural networks to study the properties of neutrinos. Machine learning tools like neural networks (NN) can be used to identify the particle types or determine their energies in detectors such as those used in the NOvA neutrino experiment, which studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falselyconstraining the phase space. 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