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Heterogeneous Graph Neural Network for identifying hadronically decayed tau leptons at the High Luminosity LHC

Journal Article · · Journal of Instrumentation
 [1];  [2];  [1];  [2];  [2];  [3]
  1. Univ. of California, Berkeley, CA (United States)
  2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  3. Univ. of Minnesota, Minneapolis, MN (United States)
+ Show Author Affiliations
Here, we present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algorithm is used. Instead, the algorithm represents jets as heterogeneous graphs with tracks and energy clusters as nodes and trains a Graph Neural Network to identify tau jets from other jets. Different attributed graph representations and different GNN architectures are explored. We propose to use differential track and energy cluster information as node features and a heterogeneous sequentially-biased encoding for the inputs to final graph-level classification.
Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
2323302
Journal Information:
Journal of Instrumentation, Journal Name: Journal of Instrumentation Vol. 18; ISSN 1748-0221
Publisher:
Institute of Physics (IOP)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

47 OTHER INSTRUMENTATION
LHC
calibration and fitting methods
cluster finding
graph neural network
heterogeneous graph
pattern recognition
performance of high energy physics detectors
tau lepton