MLPF: Efficient machine-learned particle-flow reconstruction using graph
neural networks
- URL: http://arxiv.org/abs/2101.08578v2
- Date: Wed, 10 Mar 2021 15:18:59 GMT
- Title: MLPF: Efficient machine-learned particle-flow reconstruction using graph
neural networks
- Authors: Joosep Pata, Javier Duarte, Jean-Roch Vlimant, Maurizio Pierini, Maria
Spiropulu
- Abstract summary: In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a particle-level view of the event.
We introduce a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, scalable, and graph neural networks.
We report the physics and computational performance of the algorithm on a Monte Carlo dataset of top quark-antiquark pairs produced in proton-proton collisions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In general-purpose particle detectors, the particle-flow algorithm may be
used to reconstruct a comprehensive particle-level view of the event by
combining information from the calorimeters and the trackers, significantly
improving the detector resolution for jets and the missing transverse momentum.
In view of the planned high-luminosity upgrade of the CERN Large Hadron
Collider (LHC), it is necessary to revisit existing reconstruction algorithms
and ensure that both the physics and computational performance are sufficient
in an environment with many simultaneous proton-proton interactions (pileup).
Machine learning may offer a prospect for computationally efficient event
reconstruction that is well-suited to heterogeneous computing platforms, while
significantly improving the reconstruction quality over rule-based algorithms
for granular detectors. We introduce MLPF, a novel, end-to-end trainable,
machine-learned particle-flow algorithm based on parallelizable,
computationally efficient, and scalable graph neural networks optimized using a
multi-task objective on simulated events. We report the physics and
computational performance of the MLPF algorithm on a Monte Carlo dataset of top
quark-antiquark pairs produced in proton-proton collisions in conditions
similar to those expected for the high-luminosity LHC. The MLPF algorithm
improves the physics response with respect to a rule-based benchmark algorithm
and demonstrates computationally scalable particle-flow reconstruction in a
high-pileup environment.
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