Interaction networks for the identification of boosted $H\to b\overline{b}$ decays

S. Marzani, G. Soyez, and M. Spannowsky, Looking Inside Jets: an Introduction to Jet Substructure and Boosted-Object Phenomenology , Lect. Notes Phys., Vol. 958 (Springer, New York, 2019) arXiv:1901.10342

Original

1901

Earlier work this paper cites.

G. Kasieczka et al. , The machine learning landscape of top taggers, SciPost Phys. 7

Original

1902

Earlier work this paper cites.

H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101

Original

1902

Earlier work this paper cites.

J. H. Collins, K. Howe, and B. Nachman, Extending the search for new resonances with machine learning, Phys. Rev. D 99

Original

1902

Earlier work this paper cites.

P. T. Komiske, E. M. Metodiev, and J. Thaler, Metric space of collider events, Phys. Rev. Lett. 123

Original

1902

Earlier work this paper cites.

S. R. Qasim, J. Kieseler, Y. Iiyama, and M. Pierini, Learning representations of irregular particle-detector geometry with distance-weighted graph networks, Eur. Phys. J. C 79

Original

1902

Earlier work this paper cites.

Y.-Y. Li, R. Nicolaidou, and S. Paganis, Exclusion of heavy, broad resonances from precise measurements of W ​ Z WZ and V ​ H VH final states at the LHC, Eur. Phys. J. C 79

Original

1904

Earlier work this paper cites.

B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure, Phys. Rev. D 100

Original

1904

Earlier work this paper cites.

CMS Collaboration, A multi-dimensional search for new heavy resonances decaying to boosted W ​ W WW , W ​ Z WZ , or Z ​ Z ZZ boson pairs in the dijet final state at 13 TeV, Eur. Phys. J. C 80

Original

1906

Earlier work this paper cites.

ATLAS Collaboration, Identification of boosted Higgs bosons decaying into b b -quark pairs with the ATLAS detector at 13 TeV, Eur. Phys. J. C 79

Original

1906

Earlier work this paper cites.

E. A. Moreno, O. Cerri, J. M. Duarte, H. B. Newman, T. Q. Nguyen, A. Periwal, M. Pierini, A. Serikova, M. Spiropulu, and J.-R. Vlimant, JEDI-net: a jet identification algorithm based on interaction networks, Eur. Phys. J. C 80

Original

1908

Earlier work this paper cites.

P. T. Komiske, R. Mastandrea, E. M. Metodiev, P. Naik, and J. Thaler, Exploring the space of jets with CMS open data, Phys. Rev. D 101

Original

1908

Earlier work this paper cites.

L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass agnostic jet taggers, SciPost Phys. 8

Original

1908

Earlier work this paper cites.

R. J. Williams and D. Zipser, A learning algorithm for continually running fully recurrent neural networks, Neural Comput. 1

1989

Earlier work this paper cites.

M. H. Seymour, Tagging a heavy Higgs boson, in ECFA Large Hadron Collider Workshop, Aachen, Germany, 1990, Proceedings (1991) p. 557

1991

Earlier work this paper cites.

Y. LeCun and Y. Bengio, Convolutional networks for images, speech, and time series, in The Handbook of Brain Theory and Neural Networks , Vol. 3361, edited by M. A. Arbib (MIT Press, Cambridge, Massachusetts, 1995) p. 255

1995

Earlier work this paper cites.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, Face recognition: a convolutional neural-network approach, IEEE Trans. Neural Networks 8

1997

Earlier work this paper cites.

S. Hochreiter and J. Schmidhuber, Long short–term memory, Neural Comput. 9

1997

Earlier work this paper cites.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE 11

1998

Earlier work this paper cites.

L. Randall and R. Sundrum, Large mass hierarchy from a small extra dimension, Phys. Rev. Lett. 83

1999

Earlier work this paper cites.

B. Nachman and D. Shih, Anomaly detection with density estimation, Phys. Rev. D 101

Original

2001

Earlier work this paper cites.

J. H. Friedman, Greedy function approximation: a gradient boosting machine, Ann. Stat. , 1189 (2001)

2001

Earlier work this paper cites.

J. M. Butterworth, B. E. Cox, and J. R. Forshaw, W ​ W WW scattering at the CERN LHC, Phys. Rev. D 65

2002

Earlier work this paper cites.

J. H. Friedman, Stochastic gradient boosting, Computational Statistics and Data Analysis 38

2002

Earlier work this paper cites.

X. Ju et al. , Graph neural networks for particle reconstruction in high energy physics detectors, in Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems ( ml4physicalsciences.github.io , Vancouver, 2019) arXiv:2003.11603

Original

2003

Earlier work this paper cites.

CMS Collaboration, Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques, J. Instrum. 15

Original

2004

Earlier work this paper cites.

CMS Collaboration, CMS physics: technical design report Volume 1: Detector performance and software , CMS Technical Design Report CERN-LHCC-2006-001 (2006)

2006

Earlier work this paper cites.

Y. Yao, L. Rosasco, and A. Caponnetto, On early stopping in gradient descent learning, Constr. Approx. 26

2007

Earlier work this paper cites.

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

Original

2008

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems 25 (Curran Associates, Inc., Red Hook, New York, 2012) p. 1097

2012

Earlier work this paper cites.

M. Cacciari, G. P. Salam, and G. Soyez, FastJet user manual, Eur. Phys. J. C 72

Original

2012

Earlier work this paper cites.

CMS Collaboration and H. Kirschenmann, Jet performance in CMS, Proc. Sci. EPS-HEP2013, 433 (2013)

2013

Earlier work this paper cites.

A. Graves, A.-R. Mohamed, and G. Hinton, Speech recognition with deep recurrent neural networks, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, New York, 2013) p. 6645

2013

Earlier work this paper cites.

R. D. Ball, V. Bertone, S. Carrazza, C. S. Deans, L. Del Debbio, S. Forte, A. Guffanti, N. P. Hartland, J. I. Latorre, J. Rojo, and M. Ubiali (NNPDF), Parton distributions with LHC data, Nucl. Phys. B 867

Original

2013

Earlier work this paper cites.

M. Schlaffer, M. Spannowsky, M. Takeuchi, A. Weiler, and C. Wymant, Boosted Higgs shapes, Eur. Phys. J. C 74

Original

2014

Earlier work this paper cites.

M. Niepert, M. Ahmed, and K. Kutzkov, Learning convolutional neural networks for graphs, in Proceedings of the 33rd International Conference on Machine Learning , Proceedings of Machine Learning Research, edited by M. F. Balcan and K. Q. Weinberger (PMLR, New York, 2016) p. 2014

2014

Cited alongside, same era.

J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, Spectral networks and locally connected networks on graphs, in 2nd International Conference on Learning Representations, ICLR 2014, Conference Track Proceedings (ICLR, Banf, 2014) arXiv:1312.6203

Original

2014

Cited alongside, same era.

CERN Open Data Portal, http://opendata.cern.ch (2014)

2014

Cited alongside, same era.

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, in Deep Learning and Representation Learning Workshop at the 28th Conference on Neural Information Processing Systems (dlworkshop.org, Montreal, 2014) arXiv:1412.3555

Original

2014

Cited alongside, same era.

C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Søgaard, Decorrelated jet substructure tagging using adversarial neural networks, Phys. Rev. D 96

Original

2017

Later among the works it cites.

Open Neural Network Exchange Collaboration, ONNX , https://onnx.ai/ (2017)

2017

Later among the works it cites.

CMS Collaboration, Performance of deep tagging algorithms for boosted double quark jet topology in proton-proton collisions at 13 TeV with the Phase-0 CMS detector , CMS Detector Performance Note CMS-DP-2018-046 (2018)

2018

Later among the works it cites.

A. M. Sirunyan et al. (CMS), Inclusive search for a highly boosted Higgs boson decaying to a bottom quark-antiquark pair, Phys. Rev. Lett. 120

Original

2018

Later among the works it cites.