References
241
533. Y. Freund, Boosting a weak learning algorithm by majority. Inf. Comput. 121, 256 (1995).
https://doi.org/10.1006/inco.1995.1136
534. S.S. Haykin, Neural Networks and Learning Machines (Pearson, 2008)
535. A. Hoecker et al., TMVA—Toolkit for multivariate data analysis, arXiv:physics/0703039
536. P. Baldi, P. Sadowski, D. Whiteson, Searching for exotic particles in high-energy physics
with deep learning. Nat. Commun. 5, 4308 (2014). https://doi.org/10.1038/ncomms5308.
arXiv:1402.4735
537. L.G. Almeida, M. Backovi´ c, M. Cliche, S.J. Lee, M. Perelstein, Playing tag with ANN:
boosted top identification with pattern recognition. JHEP 07, 086 (2015). https://doi.org/10.
1007/JHEP07(2015)086. arXiv:1501.05968
538. L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, A. Schwartzman, Jet-images—
deep learning edition. JHEP 07, 069 (2016). https://doi.org/10.1007/JHEP07(2016)069.
arXiv:1511.05190
539. P. Baldi, K. Bauer, C. Eng, P. Sadowski, D. Whiteson, Jet substructure classification in highenergy physics with deep neural networks. Phys. Rev. D 93, 094034 (2016). https://doi.org/
10.1103/PhysRevD.93.094034. arXiv:1603.09349
540. J. Cogan, M. Kagan, E. Strauss, A. Schwarztman, Jet-images: computer vision inspired
techniques for jet tagging. JHEP 02, 118 (2015). https://doi.org/10.1007/JHEP02(2015)118.
arXiv:1407.5675
541. G. Louppe, K. Cho, C. Becot, K. Cranmer, QCD-aware recursive neural networks for jet physics. JHEP 01, 057 (2019). https://doi.org/10.1007/JHEP01(2019)057.
arXiv:1702.00748
542. CMS Collaboration, Performance of the DeepJet b tagging algorithm using 41.9 fb −1 of
data from proton-proton collisions at 13 TeV with phase 1 CMS detector. CMS Detector
Performance Summary (November 2018)
543. D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, D. Whiteson, Jet flavor classification
in high-energy physics with deep neural networks. Phys. Rev. D 94, 112002 (2016). https://
doi.org/10.1103/PhysRevD.94.112002. arXiv:1607.08633
544. ATLAS Collaboration, Identification of jets containing b-hadrons with recurrent neural networks at the ATLAS experiment. ATLAS Physics Note. ATL-PHYS-PUB-2017-003 (2017),
https://cds.cern.ch/record/2255226
545. J. Arjona Martínez, O. Cerri, M. Pierini, M. Spiropulu, J.-R. Vlimant, Pileup mitigation at
the Large Hadron Collider with graph neural networks. Eur. Phys. J. Plus 134, 333 (2019).
https://doi.org/10.1140/epjp/i2019-12710-3. arXiv:1810.07988
546. H. Qu, L. Gouskos, ParticleNet: Jet tagging via particle clouds. Phys. Rev. D 101, 056019
(2020). https://doi.org/10.1103/PhysRevD.101.056019. arXiv:1902.08570
547. A. Butter et al., The machine learning landscape of top taggers. SciPost Phys. 7, 014 (2019).
https://doi.org/10.21468/SciPostPhys.7.1.014. arXiv:1902.09914
548. J. Pearkes, W. Fedorko, A. Lister, C. Gay, Jet constituents for deep neural network based top
quark tagging, arXiv:1704.02124
549. C. Chen, New approach to identifying boosted hadronically-decaying particle using jet substructure in its center-of-mass frame. Phys. Rev. D 85, 034007 (2012). https://doi.org/10.
1103/PhysRevD.85.034007. arXiv:1112.2567
550. C. Chen, Identifying the boosted hadronically decaying top quark using jet substructure in its
center-of-mass frame. Phys. Rev. D 87, 074007 (2013). https://doi.org/10.1103/PhysRevD.
87.074007. arXiv:1303.3521
551. ATLAS Collaboration, Jet mass and substructure of inclusive jets in
√
s = 7 TeV pp
collisions with the ATLAS experiment. JHEP 05, 128 (2012). https://doi.org/10.1007/
JHEP05(2012)128. arXiv:1203.4606
552. C.M.S. Collaboration, Studies of jet mass in dijet and W /Z +jet events. JHEP 05, 090 (2013).
https://doi.org/10.1007/JHEP05(2013)090. arXiv:1303.4811
553. ATLAS Collaboration, Measurement of the soft-drop jet mass in pp collisions at
√
s = 13
TeV with the ATLAS detector. Phys. Rev. Lett. 121, 092001 (2018). https://doi.org/10.1103/
PhysRevLett.121.092001. arXiv:1711.08341
241
533. Y. Freund, Boosting a weak learning algorithm by majority. Inf. Comput. 121, 256 (1995).
https://doi.org/10.1006/inco.1995.1136
534. S.S. Haykin, Neural Networks and Learning Machines (Pearson, 2008)
535. A. Hoecker et al., TMVA—Toolkit for multivariate data analysis, arXiv:physics/0703039
536. P. Baldi, P. Sadowski, D. Whiteson, Searching for exotic particles in high-energy physics
with deep learning. Nat. Commun. 5, 4308 (2014). https://doi.org/10.1038/ncomms5308.
arXiv:1402.4735
537. L.G. Almeida, M. Backovi´ c, M. Cliche, S.J. Lee, M. Perelstein, Playing tag with ANN:
boosted top identification with pattern recognition. JHEP 07, 086 (2015). https://doi.org/10.
1007/JHEP07(2015)086. arXiv:1501.05968
538. L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, A. Schwartzman, Jet-images—
deep learning edition. JHEP 07, 069 (2016). https://doi.org/10.1007/JHEP07(2016)069.
arXiv:1511.05190
539. P. Baldi, K. Bauer, C. Eng, P. Sadowski, D. Whiteson, Jet substructure classification in highenergy physics with deep neural networks. Phys. Rev. D 93, 094034 (2016). https://doi.org/
10.1103/PhysRevD.93.094034. arXiv:1603.09349
540. J. Cogan, M. Kagan, E. Strauss, A. Schwarztman, Jet-images: computer vision inspired
techniques for jet tagging. JHEP 02, 118 (2015). https://doi.org/10.1007/JHEP02(2015)118.
arXiv:1407.5675
541. G. Louppe, K. Cho, C. Becot, K. Cranmer, QCD-aware recursive neural networks for jet physics. JHEP 01, 057 (2019). https://doi.org/10.1007/JHEP01(2019)057.
arXiv:1702.00748
542. CMS Collaboration, Performance of the DeepJet b tagging algorithm using 41.9 fb −1 of
data from proton-proton collisions at 13 TeV with phase 1 CMS detector. CMS Detector
Performance Summary (November 2018)
543. D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, D. Whiteson, Jet flavor classification
in high-energy physics with deep neural networks. Phys. Rev. D 94, 112002 (2016). https://
doi.org/10.1103/PhysRevD.94.112002. arXiv:1607.08633
544. ATLAS Collaboration, Identification of jets containing b-hadrons with recurrent neural networks at the ATLAS experiment. ATLAS Physics Note. ATL-PHYS-PUB-2017-003 (2017),
https://cds.cern.ch/record/2255226
545. J. Arjona Martínez, O. Cerri, M. Pierini, M. Spiropulu, J.-R. Vlimant, Pileup mitigation at
the Large Hadron Collider with graph neural networks. Eur. Phys. J. Plus 134, 333 (2019).
https://doi.org/10.1140/epjp/i2019-12710-3. arXiv:1810.07988
546. H. Qu, L. Gouskos, ParticleNet: Jet tagging via particle clouds. Phys. Rev. D 101, 056019
(2020). https://doi.org/10.1103/PhysRevD.101.056019. arXiv:1902.08570
547. A. Butter et al., The machine learning landscape of top taggers. SciPost Phys. 7, 014 (2019).
https://doi.org/10.21468/SciPostPhys.7.1.014. arXiv:1902.09914
548. J. Pearkes, W. Fedorko, A. Lister, C. Gay, Jet constituents for deep neural network based top
quark tagging, arXiv:1704.02124
549. C. Chen, New approach to identifying boosted hadronically-decaying particle using jet substructure in its center-of-mass frame. Phys. Rev. D 85, 034007 (2012). https://doi.org/10.
1103/PhysRevD.85.034007. arXiv:1112.2567
550. C. Chen, Identifying the boosted hadronically decaying top quark using jet substructure in its
center-of-mass frame. Phys. Rev. D 87, 074007 (2013). https://doi.org/10.1103/PhysRevD.
87.074007. arXiv:1303.3521
551. ATLAS Collaboration, Jet mass and substructure of inclusive jets in
√
s = 7 TeV pp
collisions with the ATLAS experiment. JHEP 05, 128 (2012). https://doi.org/10.1007/
JHEP05(2012)128. arXiv:1203.4606
552. C.M.S. Collaboration, Studies of jet mass in dijet and W /Z +jet events. JHEP 05, 090 (2013).
https://doi.org/10.1007/JHEP05(2013)090. arXiv:1303.4811
553. ATLAS Collaboration, Measurement of the soft-drop jet mass in pp collisions at
√
s = 13
TeV with the ATLAS detector. Phys. Rev. Lett. 121, 092001 (2018). https://doi.org/10.1103/
PhysRevLett.121.092001. arXiv:1711.08341
