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L. Hertel, J. Collado, P. Sadowski, J. Ott, and P. Baldi, SoftwareX (2020), in press. Software available at: https://github.com/sherpa-ai/sherpa, arXiv:2005.04048 [cs.LG]
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2010
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2014
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Similar

I. Hoenig, G. Samach, and D. Tucker-Smith, Phys. Rev. D 90
Original
2014
Cited alongside, same era.
P. Baldi, P. Sadowski, and D. Whiteson, Nature Commun. 5
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2014
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J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3), JHEP 02
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2014
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J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, JHEP 07
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2014
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P. Berta, M. Spousta, D. W. Miller, and R. Leitner, JHEP 06
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Then

G. Aad et al. (ATLAS), Phys. Lett. B 759
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2016
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Electron efficiency measurements with the ATLAS detector using the 2015 LHC proton-proton collision data , Tech. Rep. ATLAS-CONF-2016-024 (CERN, Geneva, 2016)
2016
Later among the works it cites.
D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, Phys. Rev. D 94
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2016
Later among the works it cites.
P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, Phys. Rev. D 93
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2016
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L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, JHEP 07
Original

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2020

Learning to Identify Electrons

Collado, Julian, Howard, Jessica N., Faucett, Taylor et al.

Understand

We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information.

A deep convolutional neural network analysis of electromagnetic and hadronic calorimeter deposits is compared to the performance of typical features, revealing a $\approx 5\%$ gap which indicates that these lower-level data do contain untapped classification power.
To reveal the nature of this unused information, we use a recently developed technique to map the deep network into a space of physically interpretable observables.
We identify two simple calorimeter observables which are not typically used for electron identification, but which mimic the decisions of the convolutional network and nearly close the performance gap.

Built on

R. Brun and F. Rademakers, Nucl. Instrum. Meth. A 389
1997
Earlier work this paper cites.
S. Agostinelli et al. (GEANT4), Nucl. Instrum. Meth. A 506
2003
Earlier work this paper cites.
L. Hertel, J. Collado, P. Sadowski, J. Ott, and P. Baldi, SoftwareX (2020), in press. Software available at: https://github.com/sherpa-ai/sherpa, arXiv:2005.04048 [cs.LG]
Original
2005
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, JHEP 0605
2006
Earlier work this paper cites.
T. Faucett, J. Thaler, and D. Whiteson, (2020), arXiv:2010.11998 [hep-ph]
Original
2010
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics , Proceedings of Machine Learning Research, Vol. 15 (PMLR, Fort Lauderdale, FL, USA, 2011) pp. 315–323
2011
Earlier work this paper cites.
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, CoRR abs/1207.0580
Original
2012
Earlier work this paper cites.
F. Pandolfi, Search for the Standard Model Higgs Boson in the H → Z Z → l + l − q q ¯ H\to ZZ\to l^{+}l^{-}q\overline{q} Decay Channel at CMS , Ph.D. thesis , Zurich, ETH, New York (2012)
2012
Earlier work this paper cites.
S. Chatrchyan et al. (CMS), JHEP 04
Original
2012
Earlier work this paper cites.
G. Aad et al. (ATLAS), Phys. Rev. D 85
Original
2013
Earlier work this paper cites.
A. J. Larkoski, G. P. Salam, and J. Thaler, arXiv.org (2013), 1305.0007
Original
2013
Earlier work this paper cites.
S. Chatrchyan et al. (CMS), Phys. Lett. B 733
Original
2014
Earlier work this paper cites.

Similar

I. Hoenig, G. Samach, and D. Tucker-Smith, Phys. Rev. D 90
Original
2014
Cited alongside, same era.
P. Baldi, P. Sadowski, and D. Whiteson, Nature Commun. 5
Original
2014
Cited alongside, same era.
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3), JHEP 02
Original
2014
Cited alongside, same era.
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, JHEP 07
Original
2014
Cited alongside, same era.
P. Berta, M. Spousta, D. W. Miller, and R. Leitner, JHEP 06
Original

Then

G. Aad et al. (ATLAS), Phys. Lett. B 759
Original
2016
Later among the works it cites.
Electron efficiency measurements with the ATLAS detector using the 2015 LHC proton-proton collision data , Tech. Rep. ATLAS-CONF-2016-024 (CERN, Geneva, 2016)
2016
Later among the works it cites.
D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, Phys. Rev. D 94
Original
2016
Later among the works it cites.
P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, Phys. Rev. D 93
Original
2016
Later among the works it cites.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, JHEP 07
Original

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2014

Cited alongside, same era.

P. Baldi and P. Sadowski, Artificial Intelligence 210C

2014

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D. P. Kingma and J. Ba, CoRR abs/1412.6980

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2014

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V. Khachatryan et al. (CMS), JINST 10

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2015

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J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, JHEP 02

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2015

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F. Chollet et al. , “Keras,” https://keras.io (2015)

2015

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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015), software available from tensorflow.org

2015

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For κ < 0 \kappa<0 , empty cells are omitted from the sum

Cited in the paper.

2016

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P. Gras, S. Höche, D. Kar, A. Larkoski, L. Lönnblad, S. Plätzer, A. Siódmok, P. Skands, G. Soyez, and J. Thaler, JHEP 07

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2017

Later among the works it cites.

L. M. Dery, B. Nachman, F. Rubbo, and A. Schwartzman, JHEP 05

Original