LEADER 00901nam1-22003011i-450- 001 990000534750403321 005 20001010 035 $a000053475 035 $aFED01000053475 035 $a(Aleph)000053475FED01 035 $a000053475 100 $a20001010d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aEinfuhrung in die technische Stromungslehre$fBruno Eck 210 $aBerlin$cverlag$d1936 215 $a2v.$d24 cm 463 \1$1001990000569490403321$12001 $a1. : Theoretische Grundlagen 463 \1$1001990000569500503321$12001 $a2. : Stromungstechnisches Praktikum 700 1$aEck,$bBruno$04688 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000534750403321 952 $a05 MF 15 3$b279-280a$fDININ 959 $aDININ 996 $aEinfuhrung in die technische Stromungslehre$9329002 997 $aUNINA DB $aING01 LEADER 00832nac0-22002771i-450- 001 990007441020403321 005 20030529 035 $a000744102 035 $aFED01000744102 035 $a(Aleph)000744102FED01 035 $a000744102 100 $a20030529d1961----km-y0itay50------ba 101 0 $aeng 105 $ay-------001yy 200 1 $aEconomic development, planning and international cooperation$fby Oscar Lange 210 $aCairo$cC.B.E. Printing Press$d1961 215 $a27 p.$d24 cm 300 $aCentral bank of Egypt 700 1$aLange,$bOskar$f<1904-1965>$048485 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007441020403321 952 $aBUSTA 20[17] 25$b68787$fFGBC 959 $aFGBC 996 $aEconomic development, planning and international cooperation$91498652 997 $aUNINA LEADER 01049nam 2200325 450 001 9910632875503321 005 20230816081449.0 035 $a(CKB)5700000000336662 035 $a(NjHacI)995700000000336662 035 $a(EXLCZ)995700000000336662 100 $a20230816d1874 uy 0 101 0 $adut 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aHet Calvinisme Oorsprong en Waarborg Onzer Constitutioneele Vrijheden $eEen Nederlandsche Gedachte /$fAbraham Kuyper 210 1$aAmsterdam :$cB. van der Land,$d[1874] 215 $a1 online resource (77 pages) 517 $aCalvinisme Oorsprong en Waarborg Onzer Constitutioneele Vrijheden 606 $aCalvinism 615 0$aCalvinism. 676 $a230.42 700 $aKuyper$b Abraham$01147169 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910632875503321 996 $aHet Calvinisme Oorsprong en Waarborg Onzer Constitutioneele Vrijheden$93420584 997 $aUNINA 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 $aThis thesis presents significant advances in the use of neural networks to study the properties of neutrinos. Machine learning tools like neural networks (NN) can be used to identify the particle types or determine their energies in detectors such as those used in the NOvA neutrino experiment, which studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falselyconstraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results. 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 606 $aParticles (Nuclear physics) 606 $aParticle accelerators 606 $aMeasurement 606 $aMeasuring instruments 606 $aMachine learning 606 $aMathematical physics 606 $aComputer simulation 606 $aParticle Physics 606 $aAccelerator Physics 606 $aMeasurement Science and Instrumentation 606 $aMachine Learning 606 $aComputational Physics and Simulations 615 0$aParticles (Nuclear physics) 615 0$aParticle accelerators. 615 0$aMeasurement. 615 0$aMeasuring instruments. 615 0$aMachine learning. 615 0$aMathematical physics. 615 0$aComputer simulation. 615 14$aParticle Physics. 615 24$aAccelerator Physics. 615 24$aMeasurement Science and Instrumentation. 615 24$aMachine Learning. 615 24$aComputational Physics and Simulations. 676 $a539.72 700 $aSutton$b Andrew T. C$01437616 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910760294903321 996 $aDomain Generalization with Machine Learning in the NOvA Experiment$93598280 997 $aUNINA