Classify the Higgs decays with the PFN and ParticleNet at electron

[1]
G. Aad et al. (ATLAS Collaboration), Phys. Lett. B 716, 1-29 (2012)
[2]
S. Chatrchyan et al. (CMS Collaboration), Phys. Lett. B 716, 30-61 (2012)
[3]
R. Lafaye, T. Plehn, M. Rauch et al., JHEP 08, 009 (2009), arXiv:0904.3866
[4]
C. Englert, A. Freitas, M. M. Mühlleitner et al., J. Phys. G 41, 113001 (2014), arXiv:1403.7191
[5]
H. Baer, T. Barklow, K. Fujii et al., The International Linear Collider Technical Design Report - Volume 2: Physics, arXiv: 1306.6352 (2013)
[6]
M. Aicheler, P. Burrows, M. Draper et al., A Multi-TeV Linear Collider Based on CLIC Technology: CLIC Conceptual Design Report, doi: 10.5170/CERN-2012-007(2012)
[7]
A. Abada et al., Eur. Phys. J. ST 228(2), 261-623 (2019)
[8]
CEPC Study Group, CEPC Conceptual Design Report: Volume 1 - Accelerator, arXiv: 1809.00285 (2018)
[9]
CEPC Study Group, CEPC Conceptual Design Report: Volume 2 - Physics & Detector, arXiv: 1811.10545 (2018)
[10]
Y. bai et al., Chin. Phys. C 44(1), 013001 (2020), arXiv:1905.12903
[11]
F. An et al., Chin. Phys. C 43(4), 043002 (2019), arXiv:1810.09037
[12]
M. D. Schwartz, Modern Machine Learning and Particle Physics, arXiv: 2103.12226 (2021)
[13]
A. J. Larkoski, I. Moult, and B. Nachman, Phys. Rept. 841, 1 (2020), arXiv:1709.04464
[14]
R. Kogler et al., Rev. Mod. Phys 91, 045003 (2019), arXiv:1803.06991
[15]
J. Cogan, M. Kagan, E. Strauss et al., JHEP 02, 118 (2015), arXiv:1407.5675
[16]
L. G. Almeida, M. Backović, M. Cliche et al., JHEP 07, 086 (2015), arXiv:1501.05968
[17]
L. de Oliveira, M. Kagan, L. Mackey et al., JHEP 07, 069 (2016), arXiv:1511.05190
[18]
P. Baldi, K. Bauer, C. Eng et al., Phys. Rev. D 93, 094034 (2016), arXiv:1603.09349
[19]
D. Guest, J. Collado, P. Baldi et al., Phys. Rev. D 94, 112002 (2016), arXiv:1607.08633
[20]
J. Pearkes, W. Fedorko, A. Lister et al., Jet Constituents for Deep Neural Network Based Top Quark Tagging, arXiv: 1704.02124 (2017)
[21]
S. Egan, W. Fedorko, A. Lister et al., Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC, arXiv: 1711.09059 (2017)
[22]
K. Fraser and M. D. Schwartz, JHEP 10, 093 (2018), arXiv:1803.08066
[23]
G. Louppe, K. Cho, C. Becot et al., JHEP 01, 057 (2019), arXiv:1702.00748
[24]
T. Cheng, Comput. Softw. Big Sci. 2, 3 (2018), arXiv:1711.02633
[25]
I. Henrion, J. Brehmer, J. Bruna et al., Neural Message Passing for Jet Physics, Deep Learning for Physical Sciences Workshop at the 31st Conference on Neural Information Processing Systems (NIPS) (2017)
[26]
Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler, Journal of High Energy Physics 01, 121 (2019)
[27]
H. Qu and L. Gouskos, Phys. Rev. D 101(5), 056019 (2020), arXiv:1902.08570
[28]
E. M. Metodiev, B. Nachman, and J. Thaler, JHEP 10, 174 (2017), arXiv:1708.02949
[29]
P. T. Komiske, E. M. Metodiev, B. Nachman et al., Phys. Rev. D 98, 011502(R) (2018), arXiv:1801.10158
[30]
A. Andreassen, I. Feige, C. Frye et al., Eur. Phys. J. C 79, 102 (2019), arXiv:1804.09720
[31]
P. T. Komiske, E. M. Metodiev, and J. Thaler, JHEP 11, 059 (2018), arXiv:1809.01140
[32]
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh et al., Deep Sets, arXiv: 1703.06114 (2017)
[33]
K. He, X. Zhang, S. Ren et al., Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, in 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, Santiago, Chile, pg. 1026.c (2015).
[34]
Kilian, W., Ohl, T. & Reuter, J., Eur. Phys. J. C 71, 1742 (2011)
[35]
T. Sjostrand, S. Mrenna, and P. Z. Skands, JHEP 05, 026 (2006), arXiv:0603175
[36]
Xin Mo, Gang Li, Man-Qi Ruan et al., Chin. Phys. C 40(3), 033001 (2016)
[37]
Diederik P. Kingma, and Jimmy Ba, Adam: A Method for Stochastic Optimization, ICLR ArXiv: 1412.6980 (2015)
[38]
Laurens van der Maaten and Geoffrey Hinton, Journal of Machine Learning Research 9, 2579-2605 (2008)
[39]
H. Qu, Weaver, a streamlined yet flexible machine learning R&D framework for HEP, https://github.com/hqucms/weaver