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Title: MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

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 network 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.

Authors:
 [1];  [2];  [3];  [4];  [3]
+ Show Author Affiliations
  1. National Inst. of Chemical Physics and Biophysics (NICPB), Tallinn (Estonia); California Institute of Technology (CalTech), Pasadena, CA (United States)
  2. Univ. of California, San Diego, La Jolla, CA (United States)
  3. California Institute of Technology (CalTech), Pasadena, CA (United States)
  4. European Organization for Nuclear Research (CERN), Geneva (Switzerland)
Publication Date:
Research Org.:
California Institute of Technology (CalTech), Pasadena, CA (United States); Univ. of California, San Diego, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); National Science Foundation (NSF); Estonian Research Council; European Research Council (ERC); European Union’s Horizon 2020
OSTI Identifier:
1851571
Grant/Contract Number:  
SC0011925; SC0019227; SC0021187; SC0021396; AC02-07CH11359; 1624356; MOBTP187; 772369; CNS-1730158; ACI-1540112; ACI-1541349; OAC-1826967
Resource Type:
Accepted Manuscript
Journal Name:
European Physical Journal. C, Particles and Fields
Additional Journal Information:
Journal Volume: 81; Journal Issue: 5; Journal ID: ISSN 1434-6044
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats

Pata, Joosep, Duarte, Javier, Vlimant, Jean-Roch, Pierini, Maurizio, and Spiropulu, Maria. MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks. United States: N. p., 2021. Web. doi:10.1140/epjc/s10052-021-09158-w.
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Pata, Joosep, Duarte, Javier, Vlimant, Jean-Roch, Pierini, Maurizio, & Spiropulu, Maria. MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks. United States. https://doi.org/10.1140/epjc/s10052-021-09158-w
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Pata, Joosep, Duarte, Javier, Vlimant, Jean-Roch, Pierini, Maurizio, and Spiropulu, Maria. Sun . "MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks". United States. https://doi.org/10.1140/epjc/s10052-021-09158-w. https://www.osti.gov/servlets/purl/1851571.
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@article{osti_1851571,
title = {MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks},
author = {Pata, Joosep and Duarte, Javier and Vlimant, Jean-Roch and Pierini, Maurizio and Spiropulu, Maria},
abstractNote = {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 network 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.},
doi = {10.1140/epjc/s10052-021-09158-w},
journal = {European Physical Journal. C, Particles and Fields},
number = 5,
volume = 81,
place = {United States},
year = {Sun May 02 00:00:00 EDT 2021},
month = {Sun May 02 00:00:00 EDT 2021}
}
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https://doi.org/10.1140/epjc/s10052-021-09158-w
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Works referenced in this record:

FPGAs-as-a-Service Toolkit (FaaST)
conference, November 2020

  • Rankin, Dylan; Krupa, Jeffrey; Harris, Philip
  • 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC)
  • DOI: 10.1109/H2RC51942.2020.00010

Metric Space of Collider Events
journal, July 2019

Similarity hashing for charged particle tracking
conference, December 2019

Learning representations of irregular particle-detector geometry with distance-weighted graph networks
journal, July 2019

Jet flavour classification using DeepJet
journal, December 2020

Electromagnetic showers beyond shower shapes
journal, January 2020

  • de Oliveira, Luke; Nachman, Benjamin; Paganini, Michela
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 951
  • DOI: 10.1016/j.nima.2019.162879

Fast inference of deep neural networks in FPGAs for particle physics
journal, July 2018

Energy flow networks: deep sets for particle jets
journal, January 2019

  • Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
  • Journal of High Energy Physics, Vol. 2019, Issue 1
  • DOI: 10.1007/JHEP01(2019)121

JEDI-net: a jet identification algorithm based on interaction networks
journal, January 2020

PYTHIA 6.4 physics and manual
journal, May 2006

GPU coprocessors as a service for deep learning inference in high energy physics
journal, April 2021

  • Krupa, Jeffrey; Lin, Kelvin; Acosta Flechas, Maria
  • Machine Learning: Science and Technology, Vol. 2, Issue 3
  • DOI: 10.1088/2632-2153/abec21

Interaction networks for the identification of boosted H → b b ¯ decays
journal, July 2020

ATLAS b-jet identification performance and efficiency measurement with $$t{\bar{t}}$$tt¯ events in pp collisions at $$\sqrt{s}=13$$s=13 TeV
journal, November 2019

How to GAN away detector effects
journal, January 2020

Measurement of the diffractive structure function
journal, January 1998

Physics requirements for the FCC-hh calorimeter system
journal, January 2019

Calorimetry with deep learning: particle simulation and reconstruction for collider physics
journal, July 2020

Dynamic Graph CNN for Learning on Point Clouds
journal, October 2019

  • Wang, Yue; Sun, Yongbin; Liu, Ziwei
  • ACM Transactions on Graphics, Vol. 38, Issue 5
  • DOI: 10.1145/3326362

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
journal, January 2021

Measurement of the diffractive cross section in deep inelastic scattering using ZEUS 1994 data
journal, January 1999

  • Breitweg et al., J.
  • The European Physical Journal C, Vol. 6, Issue 1
  • DOI: 10.1007/PL00021606

Performance of the DELPHI detector
journal, August 1996

  • Abreu, P.; Adam, W.; Adye, T.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 378, Issue 1-2
  • DOI: 10.1016/0168-9002(96)00463-9

Graph neural networks in particle physics
journal, January 2021

  • Shlomi, Jonathan; Battaglia, Peter; Vlimant, Jean-Roch
  • Machine Learning: Science and Technology, Vol. 2, Issue 2
  • DOI: 10.1088/2632-2153/abbf9a

ABCNet: an attention-based method for particle tagging
journal, June 2020

A brief introduction to PYTHIA 8.1
journal, June 2008

  • Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter
  • Computer Physics Communications, Vol. 178, Issue 11
  • DOI: 10.1016/j.cpc.2008.01.036

How to GAN LHC events
journal, January 2019

Study of jet Energy Resolution at cdf
journal, October 2001

  • Bocci, A.; Kuhlmann, S.; Lami, S.
  • International Journal of Modern Physics A, Vol. 16, Issue supp01a
  • DOI: 10.1142/S0217751X01006632

DELPHES 3: a modular framework for fast simulation of a generic collider experiment
journal, February 2014

  • de Favereau, J.; Delaere, C.; Demin, P.
  • Journal of High Energy Physics, Vol. 2014, Issue 2
  • DOI: 10.1007/JHEP02(2014)057

Jet tagging via particle clouds
journal, March 2020

Supervised jet clustering with graph neural networks for Lorentz boosted bosons
journal, October 2020

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing
journal, October 2019

  • Duarte, Javier; Harris, Philip; Hauck, Scott
  • Computing and Software for Big Science, Vol. 3, Issue 1
  • DOI: 10.1007/s41781-019-0027-2

FCC-ee: The Lepton Collider: Future Circular Collider Conceptual Design Report Volume 2
journal, June 2019

  • Abada, A.; Abbrescia, M.; AbdusSalam, S. S.
  • The European Physical Journal Special Topics, Vol. 228, Issue 2
  • DOI: 10.1140/epjst/e2019-900045-4

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journal, January 2009

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
journal, May 2018

Identification of boosted Higgs bosons decaying into b-quark pairs with the ATLAS detector at 13 $$\text {TeV}$$TeV
journal, October 2019

Measurement of σ ( p p ¯ → Z ) · B ( Z → τ τ ) in p p ¯ collisions at s = 1.96     TeV
journal, May 2007

FCC-hh: The Hadron Collider: Future Circular Collider Conceptual Design Report Volume 3
journal, July 2019

  • Abada, A.; Abbrescia, M.; AbdusSalam, S. S.
  • The European Physical Journal Special Topics, Vol. 228, Issue 4
  • DOI: 10.1140/epjst/e2019-900087-0

HepSim: A Repository with Predictions for High-Energy Physics Experiments
journal, January 2015

Jet reconstruction and performance using particle flow with the ATLAS Detector
journal, July 2017

Challenges of Particle Flow reconstruction in the CMS High-Granularity Calorimeter at the High-Luminosity LHC
journal, November 2017

Pileup mitigation at the Large Hadron Collider with graph neural networks
journal, July 2019

  • Arjona Martínez, J.; Cerri, O.; Spiropulu, M.
  • The European Physical Journal Plus, Vol. 134, Issue 7
  • DOI: 10.1140/epjp/i2019-12710-3

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
conference, July 2017

  • Charles, R. Qi; Su, Hao; Kaichun, Mo
  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • DOI: 10.1109/CVPR.2017.16

Performance of the ALEPH detector at LEP
journal, June 1995

  • Buskulic, D.; Casper, D.; De Bonis, I.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 360, Issue 3
  • DOI: 10.1016/0168-9002(95)00138-7

Particle-flow reconstruction and global event description with the CMS detector
journal, October 2017

Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data
journal, September 2020

Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data
journal, September 2020

Graph Neural Networks for Particle Tracking and Reconstruction
text, January 2020

Simulated particle-level dataset of ttbar with PU200 using Pythia8+Delphes3 for machine learned particle flow (MLPF)
dataset, January 2021

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