-
1
-
-
85011270961
-
-
ATLAS collaboration, A neural network clustering algorithm for the ATLAS silicon pixel detector, 2014 JINST 9 P09009 [] []
-
ATLAS collaboration, A neural network clustering algorithm for the ATLAS silicon pixel detector, 2014 JINST 9 P09009 [arXiv:1406.7690] [INSPIRE].
-
-
-
-
2
-
-
85011285836
-
-
ATLAS collaboration, Performance of b-jet identification in the ATLAS experiment, 2016 JINST 11 P04008 [] []
-
ATLAS collaboration, Performance of b-jet identification in the ATLAS experiment, 2016 JINST 11 P04008 [arXiv:1512.01094] [INSPIRE].
-
-
-
-
3
-
-
84961918561
-
Performance of tracking, b-tagging and jet/MET reconstruction at the CMS high level trigger
-
M. Tosi, Performance of tracking, b-tagging and jet/MET reconstruction at the CMS high level trigger, J. Phys. Conf. Ser. 664 (2015) 082055.
-
(2015)
J. Phys. Conf. Ser.
, vol.664
, pp. 082055
-
-
Tosi, M.1
-
4
-
-
85011269011
-
-
CMS collaboration, Performance of tau-lepton reconstruction and identification in CMS, 2012 JINST 7 P01001 [] []
-
CMS collaboration, Performance of tau-lepton reconstruction and identification in CMS, 2012 JINST 7 P01001 [arXiv:1109.6034] [INSPIRE].
-
-
-
-
5
-
-
84928688667
-
-
NNPDF collaboration, R.D. Ball et al., Parton distributions for the LHC Run II, JHEP 04 (2015) 040 [] []
-
NNPDF collaboration, R.D. Ball et al., Parton distributions for the LHC Run II, JHEP 04 (2015) 040 [arXiv:1410.8849] [INSPIRE].
-
-
-
-
6
-
-
80053239671
-
Multivariate discrimination and the Higgs + W/Z search
-
[arXiv:1010.3698] [INSPIRE]
-
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].
-
(2011)
JHEP
, vol.4
, pp. 069
-
-
Gallicchio, J.1
Huth, J.2
Kagan, M.3
Schwartz, M.D.4
Black, K.5
Tweedie, B.6
-
7
-
-
0004642580
-
-
T. Maggipinto et al., Role of neural networks in the search of the Higgs boson at LHC, Phys. Lett. B 409 (1997) 517 [] []
-
T. Maggipinto et al., Role of neural networks in the search of the Higgs boson at LHC, Phys. Lett. B 409 (1997) 517 [hep-ex/9705020] [INSPIRE].
-
-
-
-
8
-
-
85161728384
-
-
CMS collaboration, Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network, Phys. Rev. D 87 (2013) 072001 [] []
-
CMS collaboration, Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network, Phys. Rev. D 87 (2013) 072001 [arXiv:1301.0916] [INSPIRE].
-
-
-
-
10
-
-
84923197285
-
Jet-images: computer vision inspired techniques for jet tagging
-
[arXiv:1407.5675] [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].
-
(2015)
JHEP
, vol.2
, pp. 118
-
-
Cogan, J.1
Kagan, M.2
Strauss, E.3
Schwarztman, A.4
-
11
-
-
84938262433
-
Playing tag with ANN: boosted top identification with pattern recognition
-
[arXiv:1501.05968] [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].
-
(2015)
JHEP
, vol.7
, pp. 086
-
-
Almeida, L.G.1
Backović, M.2
Cliche, M.3
Lee, S.J.4
Perelstein, M.5
-
12
-
-
84978898035
-
Jet-images — Deep learning edition
-
[arXiv:1511.05190] [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].
-
(2016)
JHEP
, vol.7
, pp. 069
-
-
de Oliveira, L.1
Kagan, M.2
Mackey, L.3
Nachman, B.4
Schwartzman, A.5
-
13
-
-
84971602519
-
Jet substructure classification in high-energy physics with deep neural networks
-
[arXiv:1603.09349] [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].
-
(2016)
Phys. Rev.
, vol.500
, pp. 094034
-
-
Baldi, P.1
Bauer, K.2
Eng, C.3
Sadowski, P.4
Whiteson, D.5
-
14
-
-
85002406555
-
Jet flavor classification in high-energy physics with deep neural networks
-
[arXiv:1607.08633] [INSPIRE]
-
D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban and D. Whiteson, Jet flavor classification in high-energy physics with deep neural networks, Phys. Rev. D 94 (2016) 112002 [arXiv:1607.08633] [INSPIRE].
-
(2016)
Phys. Rev.
, vol.500
, pp. 112002
-
-
Guest, D.1
Collado, J.2
Baldi, P.3
Hsu, S.-C.4
Urban, G.5
Whiteson, D.6
-
15
-
-
85011316042
-
-
J. Barnard, E.N. Dawe, M.J. Dolan and N. Rajcic, Parton shower uncertainties in jet substructure analyses with deep neural networks, []
-
J. Barnard, E.N. Dawe, M.J. Dolan and N. Rajcic, Parton shower uncertainties in jet substructure analyses with deep neural networks, arXiv:1609.00607 [INSPIRE].
-
-
-
-
16
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks, in the proceedings of Neural Information Processing Systems (NIPS 2012)
-
Lake Tahoe: U.S.A
-
A. Krizhevsky, I. Sutskever and G.E. Hinton, Imagenet classification with deep convolutional neural networks, in the proceedings of Neural Information Processing Systems (NIPS 2012), December 3-8, Lake Tahoe, U.S.A. (2012).
-
(2012)
December
, vol.3-8
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
17
-
-
23544442655
-
How to tell quark jets from gluon jets
-
[INSPIRE]
-
J. Pumplin, How to tell quark jets from gluon jets, Phys. Rev. D 44 (1991) 2025 [INSPIRE].
-
(1991)
Phys. Rev.
, vol.500
, pp. 2025
-
-
Pumplin, J.1
-
19
-
-
85148311856
-
-
OPAL collaboration, A Study of differences between quark and gluon jets using vertex tagging of quark jets, Z. Phys. C 58 (1993) 387
-
OPAL collaboration, A Study of differences between quark and gluon jets using vertex tagging of quark jets, Z. Phys. C 58 (1993) 387.
-
-
-
-
20
-
-
0000252637
-
-
OPAL collaboration, A direct observation of quark-gluon jet differences at LEP, Phys. Lett. B 265 (1991) 462
-
OPAL collaboration, A direct observation of quark-gluon jet differences at LEP, Phys. Lett. B 265 (1991) 462.
-
-
-
-
21
-
-
85011269030
-
-
D. Ferreira de Lima, P. Petrov, D. Soper and M. Spannowsky, Quark-gluon tagging with shower deconstruction: unearthing dark matter and Higgs couplings, []
-
D. Ferreira de Lima, P. Petrov, D. Soper and M. Spannowsky, Quark-gluon tagging with shower deconstruction: unearthing dark matter and Higgs couplings, arXiv:1607.06031 [INSPIRE].
-
-
-
-
22
-
-
84920145134
-
Gaining (mutual) information about quark/gluon discrimination
-
[arXiv:1408.3122] [INSPIRE]
-
A.J. Larkoski, J. Thaler and W.J. Waalewijn, Gaining (mutual) information about quark/gluon discrimination, JHEP 11 (2014) 129 [arXiv:1408.3122] [INSPIRE].
-
(2014)
JHEP
, vol.11
, pp. 129
-
-
Larkoski, A.J.1
Thaler, J.2
Waalewijn, W.J.3
-
23
-
-
84928667310
-
Associated jet and subjet rates in light-quark and gluon jet discrimination
-
[arXiv:1501.04794] [INSPIRE]
-
B. Bhattacherjee, S. Mukhopadhyay, M.M. Nojiri, Y. Sakaki and B.R. Webber, Associated jet and subjet rates in light-quark and gluon jet discrimination, JHEP 04 (2015) 131 [arXiv:1501.04794] [INSPIRE].
-
(2015)
JHEP
, vol.4
, pp. 131
-
-
Bhattacherjee, B.1
Mukhopadhyay, S.2
Nojiri, M.M.3
Sakaki, Y.4
Webber, B.R.5
-
24
-
-
84928250592
-
Quark and gluon jet substructure
-
[arXiv:1211.7038] [INSPIRE]
-
J. Gallicchio and M.D. Schwartz, Quark and gluon jet substructure, JHEP 04 (2013) 090 [arXiv:1211.7038] [INSPIRE].
-
(2013)
JHEP
, vol.4
, pp. 090
-
-
Gallicchio, J.1
Schwartz, M.D.2
-
25
-
-
80054766696
-
Quark and gluon tagging at the LHC
-
[arXiv:1106.3076] [INSPIRE]
-
J. Gallicchio and M.D. Schwartz, Quark and gluon tagging at the LHC, Phys. Rev. Lett. 107 (2011) 172001 [arXiv:1106.3076] [INSPIRE].
-
(2011)
Phys. Rev. Lett.
, vol.107
, pp. 172001
-
-
Gallicchio, J.1
Schwartz, M.D.2
-
26
-
-
85011273190
-
-
J.R. Andersen et al., Les Houches 2015: physics at TeV colliders standard model working group report, []
-
J.R. Andersen et al., Les Houches 2015: physics at TeV colliders standard model working group report, arXiv:1605.04692 [INSPIRE].
-
-
-
-
27
-
-
85029801742
-
-
ATLAS collaboration, Light-quark and gluon jet discrimination in pp collisions at221AsTeV with the ATLAS detector, Eur. Phys. J. C 74 (2014) 3023 [] []
-
ATLAS collaboration, Light-quark and gluon jet discrimination in pp collisions at s=7 TeV with the ATLAS detector, Eur. Phys. J. C 74 (2014) 3023 [arXiv:1405.6583] [INSPIRE].
-
-
-
-
28
-
-
84978818158
-
Factorization for groomed jet substructure beyond the next-to-leading logarithm
-
[arXiv:1603.09338] [INSPIRE]
-
C. Frye, A.J. Larkoski, M.D. Schwartz and K. Yan, Factorization for groomed jet substructure beyond the next-to-leading logarithm, JHEP 07 (2016) 064 [arXiv:1603.09338] [INSPIRE].
-
(2016)
JHEP
, vol.7
, pp. 064
-
-
Frye, C.1
Larkoski, A.J.2
Schwartz, M.D.3
Yan, K.4
-
29
-
-
56449095373
-
-
th International Conference on Machine Learning, July 5-9, Helsinki, Finland (2008)
-
th International Conference on Machine Learning, July 5-9, Helsinki, Finland (2008).
-
-
-
-
30
-
-
84903779279
-
Searching for exotic particles in high-energy physics with deep learning
-
[arXiv:1402.4735] [INSPIRE]
-
P. Baldi, P. Sadowski and D. Whiteson, Searching for exotic particles in high-energy physics with deep learning, Nature Commun. 5 (2014) 4308 [arXiv:1402.4735] [INSPIRE].
-
(2014)
Nature Commun.
, vol.5
, pp. 4308
-
-
Baldi, P.1
Sadowski, P.2
Whiteson, D.3
-
31
-
-
84936124348
-
Rotation-invariant convolutional neural networks for galaxy morphology prediction
-
[arXiv:1503.07077]
-
S. Dieleman, K.W. Willet and J. Dambre, Rotation-invariant convolutional neural networks for galaxy morphology prediction, Mon. Roy. Astron. Soc. 450 (2015) 1441 [arXiv:1503.07077].
-
(2015)
Mon. Roy. Astron. Soc.
, vol.450
, pp. 1441
-
-
Dieleman, S.1
Willet, K.W.2
Dambre, J.3
-
34
-
-
85011262525
-
-
th International Conference on Artificial Intelligence and Statistics, April 11-13, Ft. Lauderdale, U.S.A. (2011)
-
th International Conference on Artificial Intelligence and Statistics, April 11-13, Ft. Lauderdale, U.S.A. (2011).
-
-
-
-
35
-
-
0024880831
-
Multilayer feedforward networks are universal approximators
-
K. Hornik, M. Stinchcombe and H. White, Multilayer feedforward networks are universal approximators, Neural Netw. 2 (1989) 359.
-
(1989)
Neural Netw.
, vol.2
, pp. 359
-
-
Hornik, K.1
Stinchcombe, M.2
White, H.3
-
36
-
-
84910651844
-
Deep learning in neural networks: an overview
-
[arXiv:1404.7828]
-
J. Schmidhuber, Deep learning in neural networks: an overview, Neural Netw. 61 (2015) 85 [arXiv:1404.7828].
-
(2015)
Neural Netw.
, vol.61
, pp. 85
-
-
Schmidhuber, J.1
-
37
-
-
84937522268
-
Going deeper with convolutions, in the proceedings of the
-
C. Szegedy et al., Going deeper with convolutions, in the proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, Boston U.S.A. (2015), arXiv:1409.4842.
-
(2015)
IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, Boston U.S.A. (2015), arXiv
, pp. 4842
-
-
Szegedy, C.1
-
38
-
-
84904163933
-
Dropout: a simple way to prevent neural networks from overfitting
-
N. Srivastava et al., Dropout: a simple way to prevent neural networks from overfitting, JMLR 15 (2014) 1929.
-
(2014)
JMLR
, vol.15
, pp. 1929
-
-
Srivastava, N.1
-
39
-
-
84928671738
-
-
T. Sjöstrand, et al., An Introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [] []
-
T. Sjöstrand, et al., An Introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012] [INSPIRE].
-
-
-
-
40
-
-
57049125048
-
HERWIG++ physics and manual
-
[arXiv:0803.0883] [INSPIRE]
-
M. Bahr et al., HERWIG++ physics and manual, Eur. Phys. J. C 58 (2008) 639 [arXiv:0803.0883] [INSPIRE].
-
(2008)
Eur. Phys. J.
, vol.100
, pp. 639
-
-
Bahr, M.1
-
41
-
-
84963876705
-
-
J. Bellm et al., HERWIG 7.0/HERWIG++ 3.0 release note, Eur. Phys. J. C 76 (2016) 196 [] []
-
J. Bellm et al., HERWIG 7.0/HERWIG++ 3.0 release note, Eur. Phys. J. C 76 (2016) 196 [arXiv:1512.01178] [INSPIRE].
-
-
-
-
42
-
-
84858019473
-
FastJet user manual
-
[arXiv:1111.6097] [INSPIRE]
-
M. Cacciari, G.P. Salam and G. Soyez, FastJet user manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].
-
(2012)
Eur. Phys. J.
, vol.100
, pp. 1896
-
-
Cacciari, M.1
Salam, G.P.2
Soyez, G.3
-
43
-
-
84856393274
-
t jet clustering algorithm
-
[arXiv:0802.1189] [INSPIRE]
-
t jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].
-
(2008)
JHEP
, vol.4
, pp. 063
-
-
Cacciari, M.1
Salam, G.P.2
Soyez, G.3
-
44
-
-
53549092486
-
Top tagging: a method for identifying boosted hadronically decaying top quarks
-
[arXiv:0806.0848] [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].
-
(2008)
Phys. Rev. Lett.
, vol.101
, pp. 142001
-
-
Kaplan, D.E.1
Rehermann, K.2
Schwartz, M.D.3
Tweedie, B.4
-
45
-
-
56749163377
-
A determination of parton distributions with faithful uncertainty estimation
-
NNPDF collaboration, R.D. Ball et al., A determination of parton distributions with faithful uncertainty estimation, Nucl. Phys. B 809 (2009) 1
-
(2009)
Nucl. Phys.
, vol.809
, pp. 1
-
-
Ball, R.D.1
-
46
-
-
64649106278
-
A determination of parton distributions with faithful uncertainty estimation
-
[arXiv:0808.1231] [INSPIRE]
-
[Erratum ibid. B 816 (2009) 293] [arXiv:0808.1231] [INSPIRE]
-
(2009)
Nucl. Phys.
, vol.816
, pp. 293
-
-
Ball, R.D.1
-
47
-
-
84945900998
-
-
P.Y. Simard, D. Steinkraus and J.C. Platt, Best practices for convolutional neural networks applied to visual document analysis, ICDAR 3 (2003)
-
P.Y. Simard, D. Steinkraus and J.C. Platt, Best practices for convolutional neural networks applied to visual document analysis, ICDAR 3 (2003).
-
-
-
-
49
-
-
85011262535
-
-
th Python in science conference, June 28-July 3, Austin, U.S.A. (2010)
-
th Python in science conference, June 28-July 3, Austin, U.S.A. (2010).
-
-
-
-
50
-
-
84973911419
-
Delving deep into rectifiers: surpassing human-level performance on imagenet classification, in the proceedings of the IEEE International Conference on Computer Vision
-
Santiago, Chile
-
K. He, X. Zhang, S. Ren and J. Sun, Delving deep into rectifiers: surpassing human-level performance on imagenet classification, in the proceedings of the IEEE International Conference on Computer Vision, December 11-18, Santiago, Chile (2015).
-
(2015)
December
, vol.11-18
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
51
-
-
85011316278
-
-
D. Kingma and J. Ba, Adam: a method for stochastic optimization
-
D. Kingma and J. Ba, Adam: a method for stochastic optimization, arXiv:1412.6980.
-
-
-
-
52
-
-
84869201485
-
Practical bayesian optimization of machine learning algorithms, in the proceedings of Neural Information Processing Systems (NIPS
-
J. Snoek, H. Larochelle and R.P. Adams, Practical bayesian optimization of machine learning algorithms, in the proceedings of Neural Information Processing Systems (NIPS 2012), December 3-8, Lake Tahoe, U.S.A. (2012), arXiv:1206.2944.
-
(2012)
December 3-8, Lake Tahoe, U.S.A. (2012), arXiv
, vol.1206
, pp. 2944
-
-
Snoek, J.1
Larochelle, H.2
Adams, R.P.3
-
53
-
-
85034428200
-
Taus and MET
-
CMS Collaboration, Particle-flow event reconstruction in CMS and Performance for Jets, Taus and MET, CMS-PAS-PFT-09-001 (2009).
-
(2009)
CMS-PAS-PFT-09-001
-
-
-
54
-
-
80555140075
-
Scikit-learn: machine learning in Python
-
F. Pedregosa et al., Scikit-learn: machine learning in Python, JMLR 12 (2012) 2825.
-
(2012)
JMLR
, vol.12
, pp. 2825
-
-
Pedregosa, F.1
-
55
-
-
84880382537
-
Energy correlation functions for jet substructure
-
[arXiv:1305.0007] [INSPIRE]
-
A.J. Larkoski, G.P. Salam and J. Thaler, Energy correlation functions for jet substructure, JHEP 06 (2013) 108 [arXiv:1305.0007] [INSPIRE].
-
(2013)
JHEP
, vol.6
, pp. 108
-
-
Larkoski, A.J.1
Salam, G.P.2
Thaler, J.3
-
56
-
-
85011286472
-
London
-
H. Küçük, Measurement of the inclusive-jet cross-section in proton-proton collisions and study of quark-gluon jet discrimination with the ATLAS experiment at the LHC, Dissertation, University College London, London. U.K. (2016).
-
(2016)
U.K
-
-
|