Abstract

The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Highlights

  • The detectable final state emerging from the proton–proton collisions at the Large Hadron Collider (LHC) consists of particles and jets which are reconstructed with high precision for physics analyses

  • Those events accepted by Level 1 (L1) are subjected to refined jet-trigger decisions based on jet pT and multijet topology in the High Level Trigger (HLT), using jets that are reconstructed from calorimeter cell signals with algorithms similar to the ones applied in the offline precision reconstruction [13]

  • Both the topo-cluster formation and the local hadronic cell weighting” (LCW) calibration have been validated in collisions without pile-up recorded in 2010, and in the more active pile-up environments observed in 2011 and 2012 operations

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Summary

Introduction

The detectable final state emerging from the proton–proton collisions at the Large Hadron Collider (LHC) consists of particles and jets which are reconstructed with high precision for physics analyses. Calorimeter cells with insignificant signals found to not be connected to neighbouring cells with significant signals are considered noise and discarded from further jet, particle and missing transverse momentum reconstruction. The topo-clusters, while well established in deep inelastic scattering experiments such as H1 [2] at HERA and in electron–positron collider experiments such as ALEPH [3] at LEP and BaBar [4] at PEP-II, are used here in an innovative implementation as fully calibrated three-dimensional objects representing the calorimeter signals in the complex final-state environment of hadron–hadron collisions. 5. Section 6 summarises the performance of the topo-cluster signal in the reconstruction of isolated hadrons and jets produced in the proton–proton collisions at LHC.

The ATLAS experiment
The ATLAS detector
The ATLAS detector systems
The ATLAS trigger
Dataset
Pile-up in data
Effect on calorimeter noise
Monte Carlo simulations
Monte Carlo simulations of signal samples
Minimum-bias samples and pile-up modelling
Minimum-bias overlay samples for 2012
Detector simulation
Hadronic final-state reconstruction in ATLAS
Topological cluster formation and features
Topo-cluster formation
Collecting cells into topo-clusters
Treatment of negative cell signals
Cluster splitting
Cluster multiplicities in electromagnetic and hadronic showers
Cluster kinematics
Topo-cluster moments
Geometrical moments
Location
Directions
Extensions and sizes
Signal moments
Signal significance
Signal density
Signal timing
Signal composition
Local hadronic calibration and signal corrections
Topological isolation
General topo-cluster calibration strategy
Cluster classification
Single pions without PileUp
Hadronic calibration
Correction for out-of-cluster signal losses
Dead material corrections
Fully calibrated cluster kinematics
Performance of the simulation of topo-cluster kinematics and properties
Single-particle response
Effect of pile-up on topo-cluster observables
Event selection
Modelling of topo-cluster kinematics in events with pile-up
Transverse momentum flow in the presence of pile-up
Topo-cluster multiplicity in the presence of pile-up
No Jets
Modelling of the topo-cluster depth location in the presence of pile-up
Topo-clusters in jets
Jet energy scale and topo-cluster-based response in pile-up
Topo-cluster multiplicity in jets
Topo-cluster location in jets
Calibration and signal features of the leading topo-cluster
All Clusters
Pile-up dependence of leading topo-cluster signal features
Leading topo-cluster geometry and shapes
Pile-up dependence of leading topo-cluster geometry and shapes
Findings
Conclusion

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