Bachelor Thesis Open Access

Tau Lepton Reconstruction at FCC-ee

Bogner, Lars

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  <dc:creator>Bogner, Lars</dc:creator>
  <dc:date>2024-07-22</dc:date>
  <dc:description>The next generation of e+e--colliders, such as the proposed Future Circular Collider electron-positron (FCC-ee) at CERN operating as a Higgs and electroweak factory, necessitates new implementations while enabling innovative approaches in τ lepton reconstruction. In the FCC dataset, the τ lepton plays a crucial role in many physics topics. Focusing on the IDEA detector concept at the FCC-ee, machine learning (ML) approaches for jet flavor tagging are studied. Additionally, explicit reconstruction algorithms are employed for tagging the decay mode of τ leptons from its decay products on the jet level.

In the study about using ML for identifying the flavor of Z boson decay jets, the existing, state-of-the-art Particle Transformer Network shows very good performance with very low false positive rates in quark jet suppression against Z → τ+τ- signal samples. A more simplistic feed-forward neural network is compared against the transformer model and shows worse performance with an increased false positive rate of two orders of magnitude.


Using an explicit reconstruction approach for the τ decay mode identification, the combination with the reconstruction of intermediate states, like the neutral π0 meson or the ρ(770) meson is proposed. The performance of this method is evaluated in dependence on multiple cuts on the jet properties, aiming at optimizing the purity of the identified decay
modes and investigating the efficiency-purity-relation. The proposed techniques promise good performance in τ reconstruction at FCC-ee but also further the need for a full simulation of the Innovative Detector for Electron-positron Accelerator (IDEA) detector to find final conclusions on the limitations of τ lepton reconstruction, as the current fast simulation shows limitations for the reconstruction of τ leptons. Additionally, alternative approaches to the tasks at hand are proposed.</dc:description>
  <dc:identifier>https://publish.etp.kit.edu/record/22243</dc:identifier>
  <dc:identifier>oai:publish.etp.kit.edu:22243</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:https://publish.etp.kit.edu/communities/etp</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:subject>FCC-ee</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>explicit reconstruction</dc:subject>
  <dc:title>Tau Lepton Reconstruction at FCC-ee</dc:title>
  <dc:type>info:eu-repo/semantics/bachelorThesis</dc:type>
  <dc:type>thesis-bachelor-thesis</dc:type>
</oai_dc:dc>
Publication date:
July 22, 2024
Keyword(s):
FCC-ee machine learning explicit reconstruction
Awarding University:
Karlsruhe Institute of Technology (KIT)
Report Number:
ETP-BACHELOR-KA/2024-10
Communities:

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