280
References
1279. ATLAS Collaboration, G. Aad et al., Search for pair production of scalar leptoquarks decaying into first- or second-generation leptons and top quarks in proton-proton collisions at
√
s = 13 TeV with the ATLAS detector, arXiv:2010.02098
1280. P. Jackson, C. Rogan, Recursive jigsaw reconstruction: HEP event analysis in the presence
of kinematic and combinatoric ambiguities. Phys. Rev. D 96, 112007 (2017). https://doi.org/
10.1103/PhysRevD.96.112007. arXiv:1705.10733
1281. P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, D. Whiteson, Parameterized neural networks
for high-energy physics. Eur. Phys. J. C 76, 235 (2016). https://doi.org/10.1140/epjc/s10052016-4099-4. arXiv:1601.07913
1282. ATLAS Collaboration, Search for pairs of scalar leptoquarks decaying into quarks and electrons or muons in
√
s = 13 TeV pp collisions with the ATLAS detector, arXiv:2006.05872
1283. N. Craig, P. Draper, K. Kong, Y. Ng, D. Whiteson, The unexplored landscape of two-body
resonances. Acta Phys. Polon. B 50, 837 (2019). https://doi.org/10.5506/APhysPolB.50.
837. arXiv:1610.09392
1284. J.H. Kim, K. Kong, B. Nachman, D. Whiteson, The motivation and status of two-body
resonance decays after the LHC Run 2 and beyond. JHEP 04, 030 (2020). https://doi.org/
10.1007/JHEP04(2020)030. arXiv:1907.06659
1285. K. Agashe, M. Ekhterachian, D. Kim, D. Sathyan, LHC signals for KK graviton from an
extended warped extra dimension, arXiv:2008.06480
1286. R. Baier, Y.L. Dokshitzer, A.H. Mueller, S. Peigne, D. Schiff, Radiative energy loss and p T
broadening of high-energy partons in nuclei. Nucl. Phys. B 484, 265 (1997). https://doi.org/
10.1016/S0550-3213(96)00581-0. arXiv:hep-ph/9608322
1287. ALICE Collaboration, Exploration of jet substructure using iterative declustering in pp and
Pb-Pb collisions at LHC energies. Phys. Lett. B 802, 135227 (2020). https://doi.org/10.1016/
j.physletb.2020.135227. arXiv:1905.02512
1288. CMS Collaboration, Measurement of the groomed jet mass in PbPb and pp collisions
at
√ s NN = 5.02 TeV. JHEP 10, 161 (2018). https://doi.org/10.1007/JHEP10(2018)161.
arXiv:1805.05145
1289. ALICE Collaboration, First measurement of jet mass in Pb-Pb and p-Pb collisions at
the LHC. Phys. Lett. B 776, 249 (2018). https://doi.org/10.1016/j.physletb.2017.11.044.
arXiv:1702.00804
1290. H.A. Andrews et al., Novel tools and observables for jet physics in heavy-ion collisions. J.
Phys. G 47, 065102 (2020). https://doi.org/10.1088/1361-6471/ab7cbc. arXiv:1808.03689
1291. V. Vaidya, Effective field theory for jet substructure in heavy ion collisions, arXiv:2010.00028
1292. K. Datta, A.J. Larkoski, Novel jet observables from machine learning. JHEP 03, 086 (2018).
https://doi.org/10.1007/JHEP03(2018)086. arXiv:1710.01305
1293. K. Datta, A. Larkoski, B. Nachman, Automating the construction of jet observables with
machine learning. Phys. Rev. D 100, 095016 (2019). https://doi.org/10.1103/PhysRevD.
100.095016. arXiv:1902.07180
1294. J. Collado, J.N. Howard, T. Faucett, T. Tong, P. Baldi, D. Whiteson, Learning to identify
electrons, arXiv:2011.01984
1295. T. Faucett, J. Thaler, D. Whiteson, Mapping machine-learned physics into a human-readable
space, arXiv:2010.11998
1296. F.C.C. Collaboration, FCC physics opportunities: future circular collider conceptual design
report volume 1. Eur. Phys. J. C 79, 474 (2019). https://doi.org/10.1140/epjc/s10052-0196904-3
1297. FCC Collaboration, HE-LHC: the high-energy Large Hadron Collider: future circular collider conceptual design report volume 4.Eur. Phys. J. ST 228, 1109 (2019). https://doi.org/
10.1140/epjst/e2019-900088-6
1298. F.C.C. Collaboration, FCC-hh: the hadron collider: future circular collider conceptual design
report volume 3. Eur. Phys. J. ST 228, 755 (2019). https://doi.org/10.1140/epjst/e2019900087-0
References
1279. ATLAS Collaboration, G. Aad et al., Search for pair production of scalar leptoquarks decaying into first- or second-generation leptons and top quarks in proton-proton collisions at
√
s = 13 TeV with the ATLAS detector, arXiv:2010.02098
1280. P. Jackson, C. Rogan, Recursive jigsaw reconstruction: HEP event analysis in the presence
of kinematic and combinatoric ambiguities. Phys. Rev. D 96, 112007 (2017). https://doi.org/
10.1103/PhysRevD.96.112007. arXiv:1705.10733
1281. P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, D. Whiteson, Parameterized neural networks
for high-energy physics. Eur. Phys. J. C 76, 235 (2016). https://doi.org/10.1140/epjc/s10052016-4099-4. arXiv:1601.07913
1282. ATLAS Collaboration, Search for pairs of scalar leptoquarks decaying into quarks and electrons or muons in
√
s = 13 TeV pp collisions with the ATLAS detector, arXiv:2006.05872
1283. N. Craig, P. Draper, K. Kong, Y. Ng, D. Whiteson, The unexplored landscape of two-body
resonances. Acta Phys. Polon. B 50, 837 (2019). https://doi.org/10.5506/APhysPolB.50.
837. arXiv:1610.09392
1284. J.H. Kim, K. Kong, B. Nachman, D. Whiteson, The motivation and status of two-body
resonance decays after the LHC Run 2 and beyond. JHEP 04, 030 (2020). https://doi.org/
10.1007/JHEP04(2020)030. arXiv:1907.06659
1285. K. Agashe, M. Ekhterachian, D. Kim, D. Sathyan, LHC signals for KK graviton from an
extended warped extra dimension, arXiv:2008.06480
1286. R. Baier, Y.L. Dokshitzer, A.H. Mueller, S. Peigne, D. Schiff, Radiative energy loss and p T
broadening of high-energy partons in nuclei. Nucl. Phys. B 484, 265 (1997). https://doi.org/
10.1016/S0550-3213(96)00581-0. arXiv:hep-ph/9608322
1287. ALICE Collaboration, Exploration of jet substructure using iterative declustering in pp and
Pb-Pb collisions at LHC energies. Phys. Lett. B 802, 135227 (2020). https://doi.org/10.1016/
j.physletb.2020.135227. arXiv:1905.02512
1288. CMS Collaboration, Measurement of the groomed jet mass in PbPb and pp collisions
at
√ s NN = 5.02 TeV. JHEP 10, 161 (2018). https://doi.org/10.1007/JHEP10(2018)161.
arXiv:1805.05145
1289. ALICE Collaboration, First measurement of jet mass in Pb-Pb and p-Pb collisions at
the LHC. Phys. Lett. B 776, 249 (2018). https://doi.org/10.1016/j.physletb.2017.11.044.
arXiv:1702.00804
1290. H.A. Andrews et al., Novel tools and observables for jet physics in heavy-ion collisions. J.
Phys. G 47, 065102 (2020). https://doi.org/10.1088/1361-6471/ab7cbc. arXiv:1808.03689
1291. V. Vaidya, Effective field theory for jet substructure in heavy ion collisions, arXiv:2010.00028
1292. K. Datta, A.J. Larkoski, Novel jet observables from machine learning. JHEP 03, 086 (2018).
https://doi.org/10.1007/JHEP03(2018)086. arXiv:1710.01305
1293. K. Datta, A. Larkoski, B. Nachman, Automating the construction of jet observables with
machine learning. Phys. Rev. D 100, 095016 (2019). https://doi.org/10.1103/PhysRevD.
100.095016. arXiv:1902.07180
1294. J. Collado, J.N. Howard, T. Faucett, T. Tong, P. Baldi, D. Whiteson, Learning to identify
electrons, arXiv:2011.01984
1295. T. Faucett, J. Thaler, D. Whiteson, Mapping machine-learned physics into a human-readable
space, arXiv:2010.11998
1296. F.C.C. Collaboration, FCC physics opportunities: future circular collider conceptual design
report volume 1. Eur. Phys. J. C 79, 474 (2019). https://doi.org/10.1140/epjc/s10052-0196904-3
1297. FCC Collaboration, HE-LHC: the high-energy Large Hadron Collider: future circular collider conceptual design report volume 4.Eur. Phys. J. ST 228, 1109 (2019). https://doi.org/
10.1140/epjst/e2019-900088-6
1298. F.C.C. Collaboration, FCC-hh: the hadron collider: future circular collider conceptual design
report volume 3. Eur. Phys. J. ST 228, 755 (2019). https://doi.org/10.1140/epjst/e2019900087-0