Interaction networks for the identification of boosted H → $$b\overline b$$ decays
Abstract
We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.
- Publication Date:
- Research Org.:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP); Kavli Foundation; NVIDIA; SuperMicro; European Research Council (ERC); Taylor W. Lawrence Research Fellowship; Mellon Mays Research Fellowship
- OSTI Identifier:
- 1643742
- Alternate Identifier(s):
- OSTI ID: 1571809
- Report Number(s):
- arXiv:1909.12285; FERMILAB-PUB-19-492-CMS-E
Journal ID: ISSN 2470-0010; PRVDAQ; 012010
- Grant/Contract Number:
- SC0011925; AC02-07CH11359; 772369
- Resource Type:
- Published Article
- Journal Name:
- Physical Review. D.
- Additional Journal Information:
- Journal Name: Physical Review. D. Journal Volume: 102 Journal Issue: 1; Journal ID: ISSN 2470-0010
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Citation Formats
Moreno, Eric A., Nguyen, Thong Q., Vlimant, Jean-Roch, Cerri, Olmo, Newman, Harvey B., Periwal, Avikar, Spiropulu, Maria, Duarte, Javier M., and Pierini, Maurizio. Interaction networks for the identification of boosted H → $b\overline b$ decays. United States: N. p., 2020.
Web. doi:10.1103/PhysRevD.102.012010.
Moreno, Eric A., Nguyen, Thong Q., Vlimant, Jean-Roch, Cerri, Olmo, Newman, Harvey B., Periwal, Avikar, Spiropulu, Maria, Duarte, Javier M., & Pierini, Maurizio. Interaction networks for the identification of boosted H → $b\overline b$ decays. United States. https://doi.org/10.1103/PhysRevD.102.012010
Moreno, Eric A., Nguyen, Thong Q., Vlimant, Jean-Roch, Cerri, Olmo, Newman, Harvey B., Periwal, Avikar, Spiropulu, Maria, Duarte, Javier M., and Pierini, Maurizio. Tue .
"Interaction networks for the identification of boosted H → $b\overline b$ decays". United States. https://doi.org/10.1103/PhysRevD.102.012010.
@article{osti_1643742,
title = {Interaction networks for the identification of boosted H → $b\overline b$ decays},
author = {Moreno, Eric A. and Nguyen, Thong Q. and Vlimant, Jean-Roch and Cerri, Olmo and Newman, Harvey B. and Periwal, Avikar and Spiropulu, Maria and Duarte, Javier M. and Pierini, Maurizio},
abstractNote = {We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.},
doi = {10.1103/PhysRevD.102.012010},
journal = {Physical Review. D.},
number = 1,
volume = 102,
place = {United States},
year = {Tue Jul 28 00:00:00 EDT 2020},
month = {Tue Jul 28 00:00:00 EDT 2020}
}
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https://doi.org/10.1103/PhysRevD.102.012010
https://doi.org/10.1103/PhysRevD.102.012010
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Cited by: 29 works
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Figures / Tables:
FIG. 1: Pictorial representation of ordinary quark and gluon jets (top left), b jets (top center), and boosted-jet topologies, emerging from high-pT W and Z bosons (top right), Higgs bosons (bottom left), and top quarks (bottom right) decaying to all-quark final states.
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Works referenced in this record:
QCD or what?
journal, January 2019
- Heimel, Theo; Kasieczka, Gregor; Plehn, Tilman
- SciPost Physics, Vol. 6, Issue 3
Maximizing the significance in Higgs boson pair analyses
journal, February 2017
- Kling, Felix; Plehn, Tilman; Schichtel, Peter
- Physical Review D, Vol. 95, Issue 3
Metric Space of Collider Events
journal, July 2019
- Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
- Physical Review Letters, Vol. 123, Issue 4
Exploring the space of jets with CMS open data
journal, February 2020
- Komiske, Patrick T.; Mastandrea, Radha; Metodiev, Eric M.
- Physical Review D, Vol. 101, Issue 3
The anti- k t jet clustering algorithm
journal, April 2008
- Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory
- Journal of High Energy Physics, Vol. 2008, Issue 04
Large Mass Hierarchy from a Small Extra Dimension
journal, October 1999
- Randall, Lisa; Sundrum, Raman
- Physical Review Letters, Vol. 83, Issue 17
Pulling out all the tops with computer vision and deep learning
journal, October 2018
- Macaluso, Sebastian; Shih, David
- Journal of High Energy Physics, Vol. 2018, Issue 10
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
journal, July 2019
- Qasim, Shah Rukh; Kieseler, Jan; Iiyama, Yutaro
- The European Physical Journal C, Vol. 79, Issue 7
Deep-learned Top Tagging with a Lorentz Layer
journal, January 2018
- Butter, Anja; Kasieczka, Gregor; Plehn, Tilman
- SciPost Physics, Vol. 5, Issue 3
Exclusion of heavy, broad resonances from precise measurements of WZ and VH final states at the LHC
journal, April 2019
- Li, You-Ying; Nicolaidou, Rosy; Paganis, Stathes
- The European Physical Journal C, Vol. 79, Issue 4
Higgs boson pair production at NNLO with top quark mass effects
journal, May 2018
- Grazzini, M.; Heinrich, G.; Jones, S.
- Journal of High Energy Physics, Vol. 2018, Issue 5
Exposing the QCD Splitting Function with CMS Open Data
journal, September 2017
- Larkoski, Andrew; Marzani, Simone; Thaler, Jesse
- Physical Review Letters, Vol. 119, Issue 13
Decorrelated jet substructure tagging using adversarial neural networks
journal, October 2017
- Shimmin, Chase; Sadowski, Peter; Baldi, Pierre
- Physical Review D, Vol. 96, Issue 7
Simulation assisted likelihood-free anomaly detection
journal, May 2020
- Andreassen, Anders; Nachman, Benjamin; Shih, David
- Physical Review D, Vol. 101, Issue 9
Jet performance in CMS
conference, March 2014
- Kirschenmann, Henning
- Proceedings of The European Physical Society Conference on High Energy Physics — PoS(EPS-HEP 2013)
Jet substructure classification in high-energy physics with deep neural networks
journal, May 2016
- Baldi, Pierre; Bauer, Kevin; Eng, Clara
- Physical Review D, Vol. 93, Issue 9
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
End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC
journal, March 2020
- Andrews, M.; Paulini, M.; Gleyzer, S.
- Computing and Software for Big Science, Vol. 4, Issue 1
JEDI-net: a jet identification algorithm based on interaction networks
journal, January 2020
- Moreno, Eric A.; Cerri, Olmo; Duarte, Javier M.
- The European Physical Journal C, Vol. 80, Issue 1
Stochastic gradient boosting
journal, February 2002
- Friedman, Jerome H.
- Computational Statistics & Data Analysis, Vol. 38, Issue 4
Deep-learning top taggers or the end of QCD?
journal, May 2017
- Kasieczka, Gregor; Plehn, Tilman; Russell, Michael
- Journal of High Energy Physics, Vol. 2017, Issue 5
Deep learning in color: towards automated quark/gluon jet discrimination
journal, January 2017
- Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.
- Journal of High Energy Physics, Vol. 2017, Issue 1
FastJet user manual: (for version 3.0.2)
journal, March 2012
- Cacciari, Matteo; Salam, Gavin P.; Soyez, Gregory
- The European Physical Journal C, Vol. 72, Issue 3
Energy flow polynomials: a complete linear basis for jet substructure
journal, April 2018
- Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
- Journal of High Energy Physics, Vol. 2018, Issue 4
Inclusive Search for a Highly Boosted Higgs Boson Decaying to a Bottom Quark-Antiquark Pair
journal, February 2018
- Sirunyan, A. M.; Tumasyan, A.; Adam, W.
- Physical Review Letters, Vol. 120, Issue 7
Uncovering latent jet substructure
journal, September 2019
- Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.
- Physical Review D, Vol. 100, Issue 5
Anomaly detection with density estimation
journal, April 2020
- Nachman, Benjamin; Shih, David
- Physical Review D, Vol. 101, Issue 7
Jet substructure at the Large Hadron Collider
journal, December 2019
- Kogler, Roman; Nachman, Benjamin; Schmidt, Alexander
- Reviews of Modern Physics, Vol. 91, Issue 4
Jet flavor classification in high-energy physics with deep neural networks
journal, December 2016
- Guest, Daniel; Collado, Julian; Baldi, Pierre
- Physical Review D, Vol. 94, Issue 11
Probing stop pair production at the LHC with graph neural networks
journal, August 2019
- Abdughani, Murat; Ren, Jie; Wu, Lei
- Journal of High Energy Physics, Vol. 2019, Issue 8
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning
journal, November 2019
- Larkoski, Andrew J.; Moult, Ian; Nachman, Benjamin
- Physics Reports
Recursive Neural Networks in Quark/Gluon Tagging
journal, June 2018
- Cheng, Taoli
- Computing and Software for Big Science, Vol. 2, Issue 1
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
Long Short-Term Memory
journal, November 1997
- Hochreiter, Sepp; Schmidhuber, Jürgen
- Neural Computation, Vol. 9, Issue 8
Extending the search for new resonances with machine learning
journal, January 2019
- Collins, Jack H.; Howe, Kiel; Nachman, Benjamin
- Physical Review D, Vol. 99, Issue 1
Mass agnostic jet taggers
journal, January 2020
- Bradshaw, Layne; Mishra, Rashmish K.; Mitridate, Andrea
- SciPost Physics, Vol. 8, Issue 1
Learning to classify from impure samples with high-dimensional data
journal, July 2018
- Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin
- Physical Review D, Vol. 98, Issue 1
Deep Learning and Its Application to LHC Physics
journal, October 2018
- Guest, Dan; Cranmer, Kyle; Whiteson, Daniel
- Annual Review of Nuclear and Particle Science, Vol. 68, Issue 1
Measurement and interpretation of differential cross sections for Higgs boson production at
journal, May 2019
- Sirunyan, A. M.; Tumasyan, A.; Adam, W.
- Physics Letters B, Vol. 792
Boosted Higgs shapes
journal, October 2014
- Schlaffer, Matthias; Spannowsky, Michael; Takeuchi, Michihisa
- The European Physical Journal C, Vol. 74, Issue 10
Jet Substructure as a New Higgs-Search Channel at the Large Hadron Collider
journal, June 2008
- Butterworth, Jonathan M.; Davison, Adam R.; Rubin, Mathieu
- Physical Review Letters, Vol. 100, Issue 24
Boosting H → b b ¯ $$ H\to b\overline{b} $$ with machine learning
journal, October 2018
- Lin, Joshua; Freytsis, Marat; Moult, Ian
- Journal of High Energy Physics, Vol. 2018, Issue 10
Constraining Light-Quark Yukawa Couplings from Higgs Distributions
journal, March 2017
- Bishara, Fady; Haisch, Ulrich; Monni, Pier Francesco
- Physical Review Letters, Vol. 118, Issue 12
Looking Inside Jets: An Introduction to Jet Substructure and Boosted-object Phenomenology
book, January 2019
- Marzani, Simone; Soyez, Gregory; Spannowsky, Michael
- Lecture Notes in Physics, Vol. 958
Soft drop
journal, May 2014
- Larkoski, Andrew J.; Marzani, Simone; Soyez, Gregory
- Journal of High Energy Physics, Vol. 2014, Issue 5
A multi-dimensional search for new heavy resonances decaying to boosted $$\text{ W }{}{}$$W $$\text{ W }{}{}$$W , $$\text{ W }{}{}$$W $$\text{ Z }{}{}$$Z , or $$\text{ Z }{}{}$$Z $$\text{ Z }{}{}$$Z boson pairs in the dijet final state at 13 $$\text {Te}\text {V}$$Te
journal, March 2020
- Sirunyan, A. M.; Tumasyan, A.; Adam, W.
- The European Physical Journal C, Vol. 80, Issue 3
Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
journal, May 2016
- Dolen, James; Harris, Philip; Marzani, Simone
- Journal of High Energy Physics, Vol. 2016, Issue 5
Very boosted Higgs in gluon fusion
journal, May 2014
- Grojean, C.; Salvioni, E.; Schlaffer, M.
- Journal of High Energy Physics, Vol. 2014, Issue 5
Usefulness of effective field theory for boosted Higgs production
journal, April 2015
- Dawson, S.; Lewis, I. M.; Zeng, Mao
- Physical Review D, Vol. 91, Issue 7
Energy correlation functions for jet substructure
journal, June 2013
- Larkoski, Andrew J.; Salam, Gavin P.; Thaler, Jesse
- Journal of High Energy Physics, Vol. 2013, Issue 6
(Machine) learning to do more with less
journal, February 2018
- Cohen, Timothy; Freytsis, Marat; Ostdiek, Bryan
- Journal of High Energy Physics, Vol. 2018, Issue 2
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
journal, June 1989
- Williams, Ronald J.; Zipser, David
- Neural Computation, Vol. 1, Issue 2
The average number of sub-jets in a hadron collider jet
journal, June 1994
- Seymour, Michael H.
- Nuclear Physics B, Vol. 421, Issue 3
Classification without labels: learning from mixed samples in high energy physics
journal, October 2017
- Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse
- Journal of High Energy Physics, Vol. 2017, Issue 10
New angles on energy correlation functions
journal, December 2016
- Moult, Ian; Necib, Lina; Thaler, Jesse
- Journal of High Energy Physics, Vol. 2016, Issue 12
The Machine Learning landscape of top taggers
journal, January 2019
- Kasieczka, Gregor; Plehn, Tilman; Butter, Anja
- SciPost Physics, Vol. 7, Issue 1
Jet tagging via particle clouds
journal, March 2020
- Qu, Huilin; Gouskos, Loukas
- Physical Review D, Vol. 101, Issue 5
Jet-images — deep learning edition
journal, July 2016
- de Oliveira, Luke; Kagan, Michael; Mackey, Lester
- Journal of High Energy Physics, Vol. 2016, Issue 7
scattering at the CERN LHC
journal, May 2002
- Butterworth, J. M.; Cox, B. E.; Forshaw, J. R.
- Physical Review D, Vol. 65, Issue 9
Anomaly Detection for Resonant New Physics with Machine Learning
journal, December 2018
- Collins, Jack; Howe, Kiel; Nachman, Benjamin
- Physical Review Letters, Vol. 121, Issue 24
Weakly supervised classification in high energy physics
journal, May 2017
- Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco
- Journal of High Energy Physics, Vol. 2017, Issue 5
On Early Stopping in Gradient Descent Learning
journal, April 2007
- Yao, Yuan; Rosasco, Lorenzo; Caponnetto, Andrea
- Constructive Approximation, Vol. 26, Issue 2
Parton distributions with LHC data
journal, February 2013
- Ball, Richard D.; Bertone, Valerio; Carrazza, Stefano
- Nuclear Physics B, Vol. 867, Issue 2
Searches for new particles using cone and cluster jet algorithms: a comparative study
journal, March 1994
- Seymour, Michael H.
- Zeitschrift für Physik C Particles and Fields, Vol. 62, Issue 1
Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
journal, May 2018
- Sirunyan, A. M.; Tumasyan, A.; Adam, W.
- Journal of Instrumentation, Vol. 13, Issue 05
Identification of boosted Higgs bosons decaying into b-quark pairs with the ATLAS detector at 13 $$\text {TeV}$$TeV
journal, October 2019
- Aad, G.; Abbott, B.; Abbott, D. C.
- The European Physical Journal C, Vol. 79, Issue 10
Novel jet observables from machine learning
journal, March 2018
- Datta, Kaustuv; Larkoski, Andrew J.
- Journal of High Energy Physics, Vol. 2018, Issue 3
Searching for new physics with deep autoencoders
journal, April 2020
- Farina, Marco; Nakai, Yuichiro; Shih, David
- Physical Review D, Vol. 101, Issue 7
Modeling BSM effects on the Higgs transverse-momentum spectrum in an EFT approach
journal, March 2017
- Grazzini, Massimiliano; Ilnicka, Agnieszka; Spira, Michael
- Journal of High Energy Physics, Vol. 2017, Issue 3
Jet substructure studies with CMS open data
journal, October 2017
- Tripathee, Aashish; Xue, Wei; Larkoski, Andrew
- Physical Review D, Vol. 96, Issue 7
An introduction to PYTHIA 8.2
journal, June 2015
- Sjöstrand, Torbjörn; Ask, Stefan; Christiansen, Jesper R.
- Computer Physics Communications, Vol. 191
Gradient-based learning applied to document recognition
journal, January 1998
- Lecun, Y.; Bottou, L.; Bengio, Y.
- Proceedings of the IEEE, Vol. 86, Issue 11
Face recognition: a convolutional neural-network approach
journal, January 1997
- Lawrence, S.; Giles, C. L.
- IEEE Transactions on Neural Networks, Vol. 8, Issue 1
Pileup per particle identification
journal, October 2014
- Bertolini, Daniele; Harris, Philip; Low, Matthew
- Journal of High Energy Physics, Vol. 2014, Issue 10
Geant4—a simulation toolkit
journal, July 2003
- 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
Towards an understanding of jet substructure
journal, September 2013
- Dasgupta, Mrinal; Fregoso, Alessandro; Marzani, Simone
- Journal of High Energy Physics, Vol. 2013, Issue 9
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
QCD-aware recursive neural networks for jet physics
journal, January 2019
- Louppe, Gilles; Cho, Kyunghyun; Becot, Cyril
- Journal of High Energy Physics, Vol. 2019, Issue 1
Particle-flow reconstruction and global event description with the CMS detector
journal, October 2017
- Sirunyan, A. M.; Tumasyan, A.; Adam, W.
- Journal of Instrumentation, Vol. 12, Issue 10
Event generator tunes obtained from underlying event and multiparton scattering measurements
journal, March 2016
- Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.
- The European Physical Journal C, Vol. 76, Issue 3
Geometric Deep Learning: Going beyond Euclidean data
journal, July 2017
- Bronstein, Michael M.; Bruna, Joan; LeCun, Yann
- IEEE Signal Processing Magazine, Vol. 34, Issue 4
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