Abstract
Based on the jet image approach, which treats the energy deposition in each calorimeter cell as the pixel intensity, the Convolutional neural network (CNN) method has been found to achieve a sizable improvement in jet tagging compared to the traditional jet substructure analysis. In this work, the Mask R-CNN framework is adopted to reconstruct Higgs jets in collider-like events, with the effects of pileup contamination taken into account. This automatic jet reconstruction method achieves higher efficiency of Higgs jet detection and higher accuracy of Higgs boson four-momentum reconstruction than traditional jet clustering and jet substructure tagging methods. Moreover, the Mask R-CNN trained on events containing a single Higgs jet is capable of detecting one or more Higgs jets in events of several different processes, without apparent degradation in reconstruction efficiency and accuracy. The outputs of the network also serve as new handles for the \( t\overline{t} \) background suppression, complementing to traditional jet substructure variables.
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References
G. Hanson et al., Evidence for Jet Structure in Hadron Production by e+e− Annihilation, Phys. Rev. Lett. 35 (1975) 1609 [INSPIRE].
G.C. Blazey et al., Run II jet physics, in Physics at Run II: QCD and Weak Boson Physics Workshop: Final General Meeting, (2000) [hep-ex/0005012] [INSPIRE].
S.D. Ellis, J. Huston, K. Hatakeyama, P. Loch and M. Tonnesmann, Jets in hadron-hadron collisions, Prog. Part. Nucl. Phys. 60 (2008) 484 [arXiv:0712.2447] [INSPIRE].
G.P. Salam, Towards Jetography, Eur. Phys. J. C 67 (2010) 637 [arXiv:0906.1833] [INSPIRE].
G.F. Sterman and S. Weinberg, Jets from Quantum Chromodynamics, Phys. Rev. Lett. 39 (1977) 1436 [INSPIRE].
S.D. Ellis and D.E. Soper, Successive combination jet algorithm for hadron collisions, Phys. Rev. D 48 (1993) 3160 [hep-ph/9305266] [INSPIRE].
S. Catani, Y.L. Dokshitzer, M.H. Seymour and B.R. Webber, Longitudinally invariant Kt clustering algorithms for hadron hadron collisions, Nucl. Phys. B 406 (1993) 187 [INSPIRE].
Y.L. Dokshitzer, G.D. Leder, S. Moretti and B.R. Webber, Better jet clustering algorithms, JHEP 08 (1997) 001 [hep-ph/9707323] [INSPIRE].
M. Cacciari, G.P. Salam and G. Soyez, The anti-kt jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].
A. Abdesselam et al., Boosted Objects: A Probe of Beyond the Standard Model Physics, Eur. Phys. J. C 71 (2011) 1661 [arXiv:1012.5412] [INSPIRE].
A. Altheimer et al., Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks, J. Phys. G 39 (2012) 063001 [arXiv:1201.0008] [INSPIRE].
A. Altheimer et al., Boosted Objects and Jet Substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012, Eur. Phys. J. C 74 (2014) 2792 [arXiv:1311.2708] [INSPIRE].
D. Adams et al., Towards an Understanding of the Correlations in Jet Substructure, Eur. Phys. J. C 75 (2015) 409 [arXiv:1504.00679] [INSPIRE].
R. Kogler et al., Jet Substructure at the Large Hadron Collider: Experimental Review, Rev. Mod. Phys. 91 (2019) 045003 [arXiv:1803.06991] [INSPIRE].
D.E. Kaplan, K. Rehermann, M.D. Schwartz and B. Tweedie, Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks, Phys. Rev. Lett. 101 (2008) 142001 [arXiv:0806.0848] [INSPIRE].
T. Plehn, G.P. Salam and M. Spannowsky, Fat Jets for a Light Higgs, Phys. Rev. Lett. 104 (2010) 111801 [arXiv:0910.5472] [INSPIRE].
T. Plehn, M. Spannowsky, M. Takeuchi and D. Zerwas, Stop Reconstruction with Tagged Tops, JHEP 10 (2010) 078 [arXiv:1006.2833] [INSPIRE].
D.E. Soper and M. Spannowsky, Finding top quarks with shower deconstruction, Phys. Rev. D 87 (2013) 054012 [arXiv:1211.3140] [INSPIRE].
J.M. Butterworth, A.R. Davison, M. Rubin and G.P. Salam, Jet substructure as a new Higgs search channel at the LHC, Phys. Rev. Lett. 100 (2008) 242001 [arXiv:0802.2470] [INSPIRE].
J. Gallicchio and M.D. Schwartz, Quark and Gluon Tagging at the LHC, Phys. Rev. Lett. 107 (2011) 172001 [arXiv:1106.3076] [INSPIRE].
J. Gallicchio and M.D. Schwartz, Quark and Gluon Jet Substructure, JHEP 04 (2013) 090 [arXiv:1211.7038] [INSPIRE].
D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE].
S.D. Ellis, C.K. Vermilion and J.R. Walsh, Techniques for improved heavy particle searches with jet substructure, Phys. Rev. D 80 (2009) 051501 [arXiv:0903.5081] [INSPIRE].
A.J. Larkoski, I. Moult and B. Nachman, Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning, Phys. Rept. 841 (2020) 1 [arXiv:1709.04464] [INSPIRE].
D. Guest, K. Cranmer and D. Whiteson, Deep Learning and its Application to LHC Physics, Ann. Rev. Nucl. Part. Sci. 68 (2018) 161 [arXiv:1806.11484] [INSPIRE].
K. Albertsson et al., Machine Learning in High Energy Physics Community White Paper, J. Phys. Conf. Ser. 1085 (2018) 022008 [arXiv:1807.02876] [INSPIRE].
A. Radovic et al., Machine learning at the energy and intensity frontiers of particle physics, Nature 560 (2018) 41 [INSPIRE].
M. Feickert and B. Nachman, A Living Review of Machine Learning for Particle Physics, arXiv:2102.02770 [INSPIRE].
A. Andreassen, I. Feige, C. Frye and M.D. Schwartz, JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics, Eur. Phys. J. C 79 (2019) 102 [arXiv:1804.09720] [INSPIRE].
G. Louppe, K. Cho, C. Becot and K. Cranmer, QCD-Aware Recursive Neural Networks for Jet Physics, JHEP 01 (2019) 057 [arXiv:1702.00748] [INSPIRE].
T. Cheng, Recursive Neural Networks in Quark/Gluon Tagging, Comput. Softw. Big Sci. 2 (2018) 3 [arXiv:1711.02633] [INSPIRE].
E.A. Moreno et al., JEDI-net: a jet identification algorithm based on interaction networks, Eur. Phys. J. C 80 (2020) 58 [arXiv:1908.05318] [INSPIRE].
J. Guo, J. Li and T. Li, The Boosted Higgs Jet Reconstruction via Graph Neural Network, arXiv:2010.05464 [INSPIRE].
F.A. Dreyer and H. Qu, Jet tagging in the Lund plane with graph networks, JHEP 03 (2021) 052 [arXiv:2012.08526] [INSPIRE].
P.T. Komiske, E.M. Metodiev and J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121 [arXiv:1810.05165] [INSPIRE].
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101 (2020) 056019 [arXiv:1902.08570] [INSPIRE].
M.J. Dolan and A. Ore, Equivariant Energy Flow Networks for Jet Tagging, arXiv:2012.00964 [INSPIRE].
J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging, JHEP 02 (2015) 118 [arXiv:1407.5675] [INSPIRE].
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images — deep learning edition, JHEP 07 (2016) 069 [arXiv:1511.05190] [INSPIRE].
P. Baldi, K. Bauer, C. Eng, P. Sadowski and D. Whiteson, Jet Substructure Classification in High-Energy Physics with Deep Neural Networks, Phys. Rev. D 93 (2016) 094034 [arXiv:1603.09349] [INSPIRE].
L.G. Almeida, M. Backović, M. Cliche, S.J. Lee and M. Perelstein, Playing Tag with ANN: Boosted Top Identification with Pattern Recognition, JHEP 07 (2015) 086 [arXiv:1501.05968] [INSPIRE].
G. Kasieczka, T. Plehn, M. Russell and T. Schell, Deep-learning Top Taggers or The End of QCD?, JHEP 05 (2017) 006 [arXiv:1701.08784] [INSPIRE].
S. Macaluso and D. Shih, Pulling Out All the Tops with Computer Vision and Deep Learning, JHEP 10 (2018) 121 [arXiv:1803.00107] [INSPIRE].
A. Butter et al., The Machine Learning Landscape of Top Taggers, SciPost Phys. 7 (2019) 014 [arXiv:1902.09914] [INSPIRE].
CMS collaboration, Machine learning-based identification of highly Lorentz-boosted hadronically decaying particles at the CMS experiment, Tech. Rep. CMS-PAS-JME-18-002, CERN, Geneva (2019).
J. Lin, M. Freytsis, I. Moult and B. Nachman, Boosting H → \( b\overline{b} \) with Machine Learning, JHEP 10 (2018) 101 [arXiv:1807.10768] [INSPIRE].
Y.-L. Chung, S.-C. Hsu and B. Nachman, Disentangling Boosted Higgs Boson Production Modes with Machine Learning, arXiv:2009.05930 [INSPIRE].
P.T. Komiske, E.M. Metodiev and M.D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination, JHEP 01 (2017) 110 [arXiv:1612.01551] [INSPIRE].
ATLAS collaboration, Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector, Tech. Rep. ATL-PHYS-PUB-2017-017, CERN, Geneva (2017).
J. Guo, J. Li, T. Li, F. Xu and W. Zhang, Deep learning for R-parity violating supersymmetry searches at the LHC, Phys. Rev. D 98 (2018) 076017 [arXiv:1805.10730] [INSPIRE].
K. He, G. Gkioxari, P. Dollár and R. Girshick, Mask r-cnn, in ICCV (2017) 2980 [arXiv:1703.06870].
J. Redmon, S. Divvala, R. Girshick and A. Farhadi, You Only Look Once: Unified, Real-Time Object Detection (2016) 779 [arXiv:1506.02640v5].
J. Redmon and A. Farhadi, YOLOv3: An Incremental Improvement (2018) [arXiv:1804.02767].
J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE].
T. Sjöstrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE].
G. Qiu, J. Duan, M. Chen and J. Guan, Tone mapping for hdr image using optimization a new closed form solution, in International Conference on Pattern Recognition, IEEE Computer Society 1 (2006) 996.
R. Girshick, J. Donahue, T. Darrell and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation (2014) 580 [arXiv:1311.2524v5].
R. Girshick, Fast r-cnn, in ICCV (2015) 1440 [arXiv:1504.08083].
S. Ren, K. He, R. Girshick and J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, in NIPS (2015) 91 [arXiv:1506.01497].
A. Krizhevsky, I. Sutskever and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM 60 (2017) 84.
K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, in 3rd International Conference on Learning Representations, ICLR 2015, Conference Track Proceedings, Y. Bengio and Y. LeCun eds., San Diego, CA, U.S.A. (2015) arXiv:1409.1556 [INSPIRE].
K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) 770 arXiv:1512.03385 [INSPIRE].
J. Long, E. Shelhamer and T. Darrell, Fully convolutional networks for semantic segmentation, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015) 3431, DOI.
T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie, Feature pyramid networks for object detection, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017) 936, DOI.
M. Jaderberg, K. Simonyan, A. Zisserman and K. Kavukcuoglu, Spatial transformer networks, CoRR (2015) [arXiv:1506.02025].
M. Cacciari and G.P. Salam, Pileup subtraction using jet areas, Phys. Lett. B 659 (2008) 119 [arXiv:0707.1378] [INSPIRE].
B.P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu and G. McGregor, Boosted decision trees, an alternative to artificial neural networks, Nucl. Instrum. Meth. A 543 (2005) 577 [physics/0408124] [INSPIRE].
A. Hocker et al., TMVA — Toolkit for Multivariate Data Analysis, physics/0703039 [INSPIRE].
ATLAS collaboration, Identification and energy calibration of hadronically decaying τ leptons with the ATLAS experiment in pp collisions at \( \sqrt{s} \) = 8 TeV, Eur. Phys. J. C 75 (2015) 303 [arXiv:1412.7086] [INSPIRE].
J.-H. Kim, Rest Frame Subjet Algorithm With SISCone Jet For Fully Hadronic Decaying Higgs Search, Phys. Rev. D 83 (2011) 011502 [arXiv:1011.1493] [INSPIRE].
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness, JHEP 03 (2011) 015 [arXiv:1011.2268] [INSPIRE].
I.M. Chakravarty, R.G. Laha and J.D. Roy, Handbook of methods of applied statistics, McGraw-Hill, New York, U.S.A (1967).
J. Gallicchio, J. Huth, M. Kagan, M.D. Schwartz, K. Black and B. Tweedie, Multivariate discrimination and the Higgs +W/Z search, JHEP 04 (2011) 069 [arXiv:1010.3698] [INSPIRE].
X. Ju and B. Nachman, Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons, Phys. Rev. D 102 (2020) 075014 [arXiv:2008.06064] [INSPIRE].
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Li, J., Li, T. & Xu, FZ. Reconstructing boosted Higgs jets from event image segmentation. J. High Energ. Phys. 2021, 156 (2021). https://doi.org/10.1007/JHEP04(2021)156
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DOI: https://doi.org/10.1007/JHEP04(2021)156