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@MASTERSTHESIS{Mbius:641213,
      author       = {Möbius, Hagen},
      othercontributors = {Issever, Cigdem and Pani, Priscilla},
      title        = {{P}erformance of a soft secondary vertex tagger
                      usingproton-proton collisions collected at 13.6 {T}e{V}
                      withthe {ATLAS} detector},
      school       = {Humboldt-Universit zu Berlin},
      type         = {Masterarbeit},
      reportid     = {PUBDB-2025-04944},
      pages        = {143},
      year         = {2025},
      note         = {Masterarbeit, Humboldt-Universit zu Berlin, 2025},
      abstract     = {This thesis evaluates a b-tagging algorithm optimized to
                      identify low pT (soft) b-hadrons in theATLAS experiment at
                      the LHC. The algorithm, called the NewVrtSecInclusiveTool,
                      reconstructssoft secondary vertices (SSVs), which can be
                      connected to the decay of a soft b-hadron. Theanalysis
                      evaluates the properties of these soft secondary vertices
                      and compares them with theproperties of b-hadrons using a
                      dileptonic t¯t sample from the Monte Carlo campaign
                      denotedas mc23a which corresponds to the ATLAS data taking
                      in 2022. An acceptance definition isintroduced to
                      specifically test the identification ability of the
                      algorithm outside of jets. Furthermore,a ΔR matching
                      procedure is developed to assess if a soft secondary vertex
                      can be associated tothe decay of a b-hadron. This procedure
                      divides the SSVs into true SSVs and fake SSVs. Acomparison
                      of these objects is done and an explanation for the origin
                      of the fake SSVs is given.The b-hadrons and SSVs in
                      acceptance, the matched and fake SSVs and the matched
                      b-hadronsare used to develop an efficiency definition for
                      the algorithm. Moreover, the average number offake SSVs nF
                      is introduced to assess how often the algorithm makes a
                      wrong tagging decision.The efficiency and the average number
                      of fake SSVs are then analysed as a function of
                      b-hadronproperties and event variables. Additionally, they
                      are used to evaluate the 3 working points ofthe algorithm.
                      The overall efficiency is in the order of 0.2 to 0.25
                      depending on the working point.The overall average number of
                      fake SSVs is between 0.01 and 0.04 depending on the
                      workingpoint. Furthermore, regions which are enhanced in
                      matched and fake SSVs are constructed and afurther splitting
                      of these regions is discussed in an effort to enable a
                      calibration of the algorithmin the future. Finally, Monte
                      Carlo to data comparisons are performed using data from 2022
                      and2023 corresponding to the Monte Carlo campaigns denoted
                      as mc23a and mc23d respective},
      cin          = {ATLAS / FHTestBeam / $Z_ET$ / $Z_ATUP$ / $Z_ATLAS$},
      cid          = {I:(DE-H253)ATLAS-20120731 / I:(DE-H253)FHTestBeam-20150203
                      / $I:(DE-H253)Z_ET-20210408$ / $I:(DE-H253)Z_ATUP-20210408$
                      / $I:(DE-H253)Z_ATLAS-20210408$},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611)},
      pid          = {G:(DE-HGF)POF4-611},
      experiment   = {EXP:(DE-H253)LHC-Exp-ATLAS-20150101},
      typ          = {PUB:(DE-HGF)19},
      url          = {https://bib-pubdb1.desy.de/record/641213},
}