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We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider.
Handwritten digit recognition with a back-propagation network
Yann LeCun et al · 1990
Earlier work this paper cites.
Longitudinally invariant K t K_{t} clustering algorithms for hadron hadron collisions
S. Catani et al · 1993
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
The CMS high level trigger
W. Adam et al · 2006
Earlier work this paper cites.
The anti- k t k_{t} jet clustering algorithm
Matteo Cacciari, Gavin P. Salam, and Gregory Soyez · 2008
Earlier work this paper cites.
Rectified linear units improve restricted Boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa et al · 2011
Earlier work this paper cites.
FastJet user manual
Matteo Cacciari, Gavin P. Salam, and Gregory Soyez · 2012
Earlier work this paper cites.
Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree
V V Gligorov and M Williams · 2013
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
KyungHyun Cho et al · 2014
Earlier work this paper cites.
DELPHES 3, A modular framework for fast simulation of a generic collider experiment
J. de Favereau et al · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Searching for exotic particles in high-energy physics with deep learning
P. Baldi, P. Sadowski, and D. Whiteson · 2014
Earlier work this paper cites.
An introduction to PYTHIA 8.2
Torbjörn Sjöstrand et al · 2015
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