Abstract
After a detailed exposition of the various reconstruction techniques in Chap. 5, further selections and quality criteria are applied to define a set of baseline and candidate reconstructed objects that will be used in the analysis and the kinematic variables to discriminate between signal and background and maximize the sensitivity for a discovery of new physics. These reconstructed objects include jets, reclustered large radius jets, b-tagged jets, leptons and missing transverse momentum. Additionally, the procedure for removing energy overlaps among the reconstructed objects will be detailed.
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- 1.
The term “lepton” exclusively refers to electron or muon in this thesis.
- 2.
The term “lepton” exclusively refers to electron or muon in this thesis.
- 3.
Unless otherwise specified, “jets” will always refer to the candidate, overlap-removed (see Sect. 6.2.4), small-R jets.
- 4.
At the 77% working point, the corresponding rejection factors against jets originating from c-quarks, τ-leptons, and light quarks & gluons are 6, 22, and 134, respectively [82].
- 5.
The optimization did sometimes favor the 85% working point, but 77% was not significantly worse. On top of this, there are some benefits using a lower efficiency working point for background estimation due to the enhanced purity of the flavor composition.
- 6.
The muon and electron definition choices were optimized in the previous version of the analysis [79].
- 7.
If you have a very boosted top quark, you often have a real electron close to a real b-jet. This is why both the electron and b-tagged jet would be kept.
- 8.
These jets usually have very few matching ID tracks.
- 9.
Unless otherwise specified, “large-R” jets will always refer to the candidate, re-clustered, trimmed jets.
- 10.
The JES uncertainties are used to describe the mass uncertainty on the re-clustered jets. In the signal regions, less than 2% of these re-clustered jets were formed from a single small-R jet, so the mass of the re-clustered jet originates from the \({p_{\text{T}, i}^{\text{jet}}}\) and separation between small-R jets.
- 11.
Trimming for re-clustered jets means to remove subjets where \({\;pT ^{\text{subjet}}} < {f_{\text{cut}}} {p_{\text{T}, i}^{\text{jet}}} \). For this analysis, subjets with pT < 10% of the re-clustered pTjet were removed.
References
ATLAS Collaboration, Optimisation of the ATLAS b-tagging performance for the 2016 LHC run. ATL-PHYS-PUB-2016-012. https://cds.cern.ch/record/2160731
ATLAS Collaboration, Performance of jet substructure techniques for large-R jets in proton–proton collisions at \(\sqrt {s} = 7\) TeV using the ATLAS detector. J. High Energy Phys. 9, 76 (2013). https://doi.org/10.1007/JHEP09(2013)076. arXiv: 1306.4945 [hep-ex]
ATLAS Collaboration, Tagging and suppression of pileup jets with the ATLAS detector (2014). ATLAS-CONF-2014-018. https://cds.cern.ch/record/1700870
ATLAS Collaboration, Electron and photon energy calibration with the ATLAS detector using LHC Run 1 data. Eur. Phys. J. C 74, 3071 (2014). https://doi.org/10.1140/epjc/s10052-014-3071-4. arXiv: 1407.5063 [hep-ex]
ATLAS Collaboration, Expected performance of missing transverse momentum reconstruction for the ATLAS detector at \(\sqrt {s} = 13\) TeV (2015). ATL-PHYS-PUB-2015-023. https://cds.cern.ch/record/2037700
ATLAS Collaboration, Selection of jets produced in 13 TeV proton-proton collisions with the ATLAS detector (2015). ATLAS-CONF-2015029. https://cds.cern.ch/record/2037702
ATLAS Collaboration, Performance of missing transverse momentum reconstruction with the ATLAS detector in the first proton-proton collisions at \(\sqrt {s}= 13\) TeV (2015). ATL-PHYS-PUB-2015-027. https://cds.cern.ch/record/2037904
ATLAS Collaboration, Vertex reconstruction performance of the ATLAS detector at \(\sqrt {s} = 13\) TeV (2015). ATL-PHYS-PUB-2015-026. https://cds.cern.ch/record/2037717
ATLAS Collaboration, Electron efficiency measurements with the ATLAS detector using the 2015 LHC proton-proton collision data (2016). ATLAS-CONF-2016-024. https://cds.cern.ch/record/2157687
ATLAS Collaboration, Performance of b-jet identification in the ATLAS experiment. J. Instrum. 11, P04008 (2016). https://doi.org/10.1088/1748-0221/11/04/P04008. arXiv: 1512.01094 [hep-ex]
ATLAS Collaboration, Muon reconstruction performance of the ATLAS detector in proton-proton collision data at \(\sqrt {s} = 13\) TeV. Eur. Phys. J. C 76, 292 (2016). https://doi.org/10.1140/epjc/s10052-016-4120-y. arXiv: 1603.05598 [hep-ex]
ATLAS Collaboration, Performance of pile-up mitigation techniques for jets in pp collisions at \(\sqrt {s} = 8\) TeV using the ATLAS detector. Eur. Phys. J. C 76, 581 (2016). https://doi.org/10.1140/epjc/s10052-016-4395-z. arXiv: 1510.03823 [hep-ex]
ATLAS Collaboration, Search for pair production of gluinos decaying via stop and sbottom in events with b-jets and large missing transverse momentum in pp collisions at \(\sqrt {s}= 13\) TeV with the ATLAS detector. Phys. Rev. D 94, 032003 (2016). https://doi.org/10.1103/PhysRevD.94.032003. arXiv: 1605.09318 [hep-ex]
ATLAS Collaboration, Search for supersymmetry in final states with missing transverse momentum and multiple b-jets in proton–proton collisions at \(\sqrt {s} = 13\) TeV with the ATLAS detector (2017). arXiv: 1711.01901 [hep-ex]
ATLAS Collaboration, Performance of top quark and W Boson tagging in run 2 with ATLAS (2017). ATLAS-CONF-2017-064. https://cds.cern.ch/record/2281054
ATLAS Collaboration, Jet reclustering and close-by effects in ATLAS Run 2 (2017). ATLAS-CONF-2017-062. https://cds.cern.ch/record/2275649
ATLAS Collaboration, Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1. Eur. Phys. J. C 77, 490 (2017). https://doi.org/10.1140/epjc/s10052-017-5004-5. arXiv: 1603.02934 [hep-ex]
ATLAS Collaboration, Jet energy scale measurements and their systematic uncertainties in proton-proton collisions at \(\sqrt {s} = 13\) TeV with the ATLAS detector. Phys. Rev. D96(7), 072002 (2017). arXiv: 1703.09665 [hep-ex]
M. Cacciari, G.P. Salam, G. Soyez, The anti-k t jet clustering algorithm. J. High Energy Phys. 4, 63 (2008). https://doi.org/10.1088/1126-6708/2008/04/063. arXiv: 0802.1189 [hep-ph]
M. Cacciari, G.P. Salam, G. Soyez, FastJet user manual. Eur. Phys. J. C 72, 1896 (2012). https://doi.org/10.1140/epjc/s10052-012-1896-2. arXiv: 1111.6097 [hep-ph]
D. Krohn, J. Thaler, L.-T. Wang, Jet trimming. J. High Energy Phys. 2, 84 (2010). https://doi.org/10.1007/JHEP02(2010)084. arXiv: 0912.1342 [hep-ph]
B. Nachman et al., Jets from jets: re-clustering as a tool for large radius jet reconstruction and grooming at the LHC. J. High Energy Phys. 2, 75 (2015). https://doi.org/10.1007/JHEP02(2015)075. arXiv: 1407.2922 [hep-ph]
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Stark, G. (2020). Boosted Object Reconstruction. In: The Search for Supersymmetry in Hadronic Final States Using Boosted Object Reconstruction. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-34548-8_6
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