Calibrating two jets at once

Gambhir, Rikab; Nachman, Benjamin

doi:10.1103/PhysRevD.110.076015

Title: Calibrating two jets at once

Journal Article · 15 October 2024 · Physical Review. D.

DOI: https://doi.org/10.1103/PhysRevD.110.076015 · OSTI ID:2474660

;

Jet-energy calibration is an important aspect of many measurements and searches at the LHC. Currently, these calibrations are performed on a per-jet basis, i.e., agnostic to the properties of other jets in the same event. In this work, we propose taking advantage of the correlations induced by momentum conservation between jets in order to improve their jet-energy calibration. By fitting the $p_{T}$ asymmetry of dijet events in simulation, while remaining agnostic to the $p_{T}$ spectra themselves, we are able to obtain correlation-improved maximum likelihood estimates. This approach is demonstrated with simulated jets from the CMS detector, yielding a 3%–5% relative improvement in the jet-energy resolution, corresponding to a quadrature improvement of approximately 35%. Published by the American Physical Society 2024

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: US Department of Energy; USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)

Grant/Contract Number:: AC02-05CH11231; SC0012567

OSTI ID:: 2474660

Journal Information:: Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 7 Vol. 110; ISSN PRVDAQ; ISSN 2470-0010

Publisher:: American Physical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

References (38)

Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks Araz, Jack Y.; Spannowsky, Michael Journal of High Energy Physics, Vol. 2021, Issue 4 https://doi.org/10.1007/JHEP04(2021)296	journal	April 2021
Pileup Mitigation with Machine Learning (PUMML) Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin Journal of High Energy Physics, Vol. 2017, Issue 12 https://doi.org/10.1007/JHEP12(2017)051	journal	December 2017
A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution Sirunyan, A. M.; Tumasyan, A.; Adam, W. Computing and Software for Big Science, Vol. 4, Issue 1 https://doi.org/10.1007/s41781-020-00041-z	journal	October 2020
Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline Holmberg, Daniel; Golubovic, Dejan; Kirschenmann, Henning Computing and Software for Big Science, Vol. 7, Issue 1 https://doi.org/10.1007/s41781-023-00103-y	journal	August 2023
Geant4—a simulation toolkit Agostinelli, S.; Allison, J.; Amako, K. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3 https://doi.org/10.1016/S0168-9002(03)01368-8	journal	July 2003
Restoring original signals from pile-up using deep learning Kim, C. H.; Ahn, S.; Chae, K. Y. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1055 https://doi.org/10.1016/j.nima.2023.168492	journal	October 2023
Dispelling the N3 myth for the kt jet-finder Cacciari, Matteo; Salam, Gavin P. Physics Letters B, Vol. 641, Issue 1 https://doi.org/10.1016/j.physletb.2006.08.037	journal	September 2006
Measurement of colour flow with the jet pull angle in t t ¯ events using the ATLAS detector at s = 8 TeV Aad, G.; Abbott, B.; Abdallah, J. Physics Letters B, Vol. 750 https://doi.org/10.1016/j.physletb.2015.09.051	journal	November 2015
PYTHIA 6.4 physics and manual Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter Journal of High Energy Physics, Vol. 2006, Issue 05 https://doi.org/10.1088/1126-6708/2006/05/026	journal	May 2006
The anti- k _t jet clustering algorithm Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory Journal of High Energy Physics, Vol. 2008, Issue 04 https://doi.org/10.1088/1126-6708/2008/04/063	journal	April 2008
Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A. Journal of Instrumentation, Vol. 12, Issue 02 https://doi.org/10.1088/1748-0221/12/02/P02014	journal	February 2017
Parametrizing the detector response with neural networks Cheong, S.; Cukierman, A.; Nachman, B. Journal of Instrumentation, Vol. 15, Issue 01 https://doi.org/10.1088/1748-0221/15/01/P01030	journal	January 2020
The CMS experiment at the CERN LHC Collaboration, The CMS; Chatrchyan, S.; Hmayakyan, G. Journal of Instrumentation, Vol. 3, Issue 08 https://doi.org/10.1088/1748-0221/3/08/S08004	journal	August 2008
Determination of jet energy calibration and transverse momentum resolution in CMS collaboration, The CMS Journal of Instrumentation, Vol. 6, Issue 11 https://doi.org/10.1088/1748-0221/6/11/P11002	journal	November 2011
Pile-up mitigation using attention Maier, B.; Narayanan, S. M.; de Castro, G. Machine Learning: Science and Technology, Vol. 3, Issue 2 https://doi.org/10.1088/2632-2153/ac7198	journal	June 2022
Machine-learning-based jet momentum reconstruction in heavy-ion collisions Haake, Rüdiger; Loizides, Constantin Physical Review C, Vol. 99, Issue 6 https://doi.org/10.1103/PhysRevC.99.064904	journal	June 2019
Jet grooming through reinforcement learning Carrazza, Stefano; Dreyer, Frédéric A. Physical Review D, Vol. 100, Issue 1 https://doi.org/10.1103/PhysRevD.100.014014	journal	July 2019
Bias and priors in machine learning calibrations for high energy physics Gambhir, Rikab; Nachman, Benjamin; Thaler, Jesse Physical Review D, Vol. 106, Issue 3 https://doi.org/10.1103/PhysRevD.106.036011	journal	August 2022
Measurement of color flow in t t ¯ events from p p ¯ collisions at s = 1.96 TeV Abazov, V. M.; Abbott, B.; Acharya, B. S. Physical Review D, Vol. 83, Issue 9 https://doi.org/10.1103/PhysRevD.83.092002	journal	May 2011
Seeing in Color: Jet Superstructure Gallicchio, Jason; Schwartz, Matthew D. Physical Review Letters, Vol. 105, Issue 2 https://doi.org/10.1103/PhysRevLett.105.022001	journal	July 2010
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics Gambhir, Rikab; Nachman, Benjamin; Thaler, Jesse Physical Review Letters, Vol. 129, Issue 8 https://doi.org/10.1103/PhysRevLett.129.082001	journal	August 2022
Regression Shrinkage and Selection Via the Lasso Tibshirani, Robert Journal of the Royal Statistical Society: Series B (Methodological), Vol. 58, Issue 1 https://doi.org/10.1111/j.2517-6161.1996.tb02080.x	journal	January 1996
FastJet user manual: (for version 3.0.2) Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory The European Physical Journal C, Vol. 72, Issue 3 https://doi.org/10.1140/epjc/s10052-012-1896-2	journal	March 2012
Measurement of colour flow using jet-pull observables in $$t\bar{t}$$ t t ¯ events with the ATLAS experiment at $$\sqrt{s} = 13\,\hbox {TeV}$$ s = 13 TeV Aaboud, M.; Aad, G.; Abbott, B. The European Physical Journal C, Vol. 78, Issue 10 https://doi.org/10.1140/epjc/s10052-018-6290-2	journal	October 2018
Measurement of the top quark mass in the $$t\bar{t}\rightarrow $$ t t ¯ → lepton+jets channel from $$\sqrt{s}=8$$ s = 8 TeV ATLAS data and combination with previous results Aaboud, M.; Aad, G.; Abbott, B. The European Physical Journal C, Vol. 79, Issue 4 https://doi.org/10.1140/epjc/s10052-019-6757-9	journal	March 2019
Determination of jet calibration and energy resolution in proton–proton collisions at $$\sqrt{s} = 8~\hbox {TeV}$$ using the ATLAS detector Aaboud, M.; Aad, G.; Abbott, B. The European Physical Journal C, Vol. 80, Issue 12 https://doi.org/10.1140/epjc/s10052-020-08477-8	journal	December 2020
Jet energy scale and resolution measured in proton–proton collisions at $$\sqrt{s}=13$$ TeV with the ATLAS detector Aad, G.; Abbott, B.; Abbott, D. C. The European Physical Journal C, Vol. 81, Issue 8 https://doi.org/10.1140/epjc/s10052-021-09402-3	journal	August 2021
Tagging the Higgs boson decay to bottom quarks with colour-sensitive observables and the Lund jet plane Cavallini, Luca; Coccaro, Andrea; Khosa, Charanjit K. The European Physical Journal C, Vol. 82, Issue 5 https://doi.org/10.1140/epjc/s10052-022-10447-1	journal	May 2022
Semi-supervised graph neural networks for pileup noise removal Li, Tianchun; Liu, Shikun; Feng, Yongbin The European Physical Journal C, Vol. 83, Issue 1 https://doi.org/10.1140/epjc/s10052-022-11083-5	journal	January 2023
New techniques for jet calibration with the ATLAS detector Aad, G.; Abbott, B.; Abeling, K. The European Physical Journal C, Vol. 83, Issue 8 https://doi.org/10.1140/epjc/s10052-023-11837-9	journal	August 2023
Measurement of the top quark mass using a profile likelihood approach with the lepton + jets final states in proton–proton collisions at $$\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V} $$ Tumasyan, A.; Adam, W.; Andrejkovic, J. W. The European Physical Journal C, Vol. 83, Issue 10 https://doi.org/10.1140/epjc/s10052-023-12050-4	journal	October 2023
Pileup mitigation at the Large Hadron Collider with graph neural networks Arjona Martínez, J.; Cerri, O.; Spiropulu, M. The European Physical Journal Plus, Vol. 134, Issue 7 https://doi.org/10.1140/epjp/i2019-12710-3	journal	July 2019
Towards an automated data cleaning with deep learning in CRESST Angloher, G.; Banik, S.; Bartolot, D. The European Physical Journal Plus, Vol. 138, Issue 1 https://doi.org/10.1140/epjp/s13360-023-03674-2	journal	January 2023
Understanding Event-Generation Networks via Uncertainties Bellagente, Marco; Haussmann, Manuel; Luchmann, Michel SciPost Physics, Vol. 13, Issue 1 https://doi.org/10.21468/SciPostPhys.13.1.003	journal	July 2022
How to GAN Higher Jet Resolution Baldi, Pierre; Blecher, Lukas; Butter, Anja SciPost Physics, Vol. 13, Issue 3 https://doi.org/10.21468/SciPostPhys.13.3.064	journal	September 2022
Deep-learning jets with uncertainties and more Bollweg, Sven; Haussmann, Manuel; Kasieczka, Gregor SciPost Physics, Vol. 8, Issue 1 https://doi.org/10.21468/SciPostPhys.8.1.006	journal	January 2020
Per-object systematics using deep-learned calibration Kasieczka, Gregor; Luchmann, Michel; Otterpohl, Florian SciPost Physics, Vol. 9, Issue 6 https://doi.org/10.21468/SciPostPhys.9.6.089	journal	January 2020
Machine Learning based jet momentum reconstruction in Pb-Pb collisions measured with the ALICE detector Haake, Ruediger Proceedings of European Physical Society Conference on High Energy Physics — PoS(EPS-HEP2019) https://doi.org/10.22323/1.364.0312	conference	October 2020