02005nam0 22004693i 450 VAN0028279820250722113916.669N978303065380420241111d2021 |0itac50 baengCH|||| |||||i e bcrSearch for tt̄H Production in the H → bb̅ Decay ChannelUsing Deep Learning Techniques with the CMS ExperimentDoctoral Thesis accepted by RWTH Aachen University, Aachen, GermanyMarcel RiegerChamSpringer2021xiii, 217 p.ill.24 cm001VAN001041932001 Springer thesesrecognizing outstanding Ph.D. research210 BerlinSpringer2010-81TxxQuantum field theory; related classical field theories [MSC 2020]VANC027580MF81V35Nuclear physics [MSC 2020]VANC023270MFBb ChannelKW:KCMS ExperimentKW:KHiggs BosonKW:KMachine learningKW:KMulticlass ClassificationKW:KMulticlassificationKW:KNeural networksKW:KTTH ProductionKW:KTth SearchKW:KTthbb SearchKW:KCHChamVANL001889RiegerMarcelVANV235782848798Springer <editore>VANV108073650ITSOL20250725RICAhttp://doi.org/10.1007/978-3-030-65380-4E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN00282798BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08DLOAD e-Book 9695 08eMF9695 20241127 Search for tt̄H Production in the H → bb̅ Decay Channel4286323UNICAMPANIA