JUNIPR: a framework for unsupervised machine learning in particle physics

[1]  T. Hussain,et al.  Measurement of D0, D+, D*+ and D s + production in Pb-Pb collisions at sNN−−−√=5.02 TeV , 2018, 1804.09083.

[2]  R. K. Elayavalli,et al.  Probing heavy ion collisions using quark and gluon jet substructure , 2018, Proceedings of International Conference on Hard and Electromagnetic Probes of High-Energy Nuclear Collisions — PoS(HardProbes2018).

[3]  D. Shih,et al.  Pulling out all the tops with computer vision and deep learning , 2018, Journal of High Energy Physics.

[4]  Y. Wang,et al.  Jet properties in PbPb and pp collisions at sNN=5.02$$ \sqrt{s_{\mathrm{N}\;\mathrm{N}}}=5.02 $$ TeV , 2018 .

[5]  E. Metodiev,et al.  Jet Topics: Disentangling Quarks and Gluons at Colliders. , 2018, Physical review letters.

[6]  Patrick T. Komiske,et al.  Learning to Classify from Impure Samples , 2018 .

[7]  Michela Paganini,et al.  CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks , 2017, ArXiv.

[8]  Patrick T. Komiske,et al.  Energy flow polynomials: a complete linear basis for jet substructure , 2017, 1712.07124.

[9]  Hui Luo,et al.  Quark jet versus gluon jet: deep neural networks with high-level features , 2017, 1712.03634.

[10]  S. Hsu,et al.  Isolating color-singlet boson jets at the LHC using telescoping jet substructure , 2017, 1711.11041.

[11]  Shannon Egan,et al.  Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC , 2017, ArXiv.

[12]  Prabhat,et al.  Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC , 2017, Journal of Physics: Conference Series.

[13]  Taoli Cheng,et al.  Recursive Neural Networks in Quark/Gluon Tagging , 2017, Computing and Software for Big Science.

[14]  A. Larkoski,et al.  Novel jet observables from machine learning , 2017, 1710.01305.

[15]  B. Nachman,et al.  Classification without labels: learning from mixed samples in high energy physics , 2017, Journal of High Energy Physics.

[16]  Gregor Kasieczka,et al.  Deep-learned Top Tagging with a Lorentz Layer , 2017, SciPost Physics.

[17]  Patrick T. Komiske,et al.  Pileup Mitigation with Machine Learning (PUMML) , 2017, Journal of High Energy Physics.

[18]  Tatsumi Nitta,et al.  Identification of Hadronically-Decaying W Boson Top Quarks Using High-Level Features as Input to Boosted Decision Trees and Deep Neural Networks in ATLAS at #sqrt{s} = 13 TeV , 2017 .

[19]  M. Freytsis,et al.  (Machine) learning to do more with less , 2017, Journal of High Energy Physics.

[20]  Zihao Jiang,et al.  Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment , 2017 .

[21]  Benjamin Nachman,et al.  Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters. , 2017, Physical review letters.

[22]  A. Larkoski,et al.  How much information is in a jet? , 2017, Journal of High Energy Physics.

[23]  Wojciech Fedorko,et al.  Jet Constituents for Deep Neural Network Based Top Quark Tagging , 2017, ArXiv.

[24]  Kyunghyun Cho,et al.  QCD-aware recursive neural networks for jet physics , 2017, Journal of High Energy Physics.

[25]  G. Kasieczka,et al.  Deep-learning top taggers or the end of QCD? , 2017, 1701.08784.

[26]  Luke de Oliveira,et al.  Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis , 2017, Computing and Software for Big Science.

[27]  Patrick T. Komiske,et al.  Deep learning in color: towards automated quark/gluon jet discrimination , 2016, Journal of High Energy Physics.

[28]  Quoc V. Le,et al.  Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.

[29]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[30]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  P. Baldi,et al.  Jet flavor classification in high-energy physics with deep neural networks , 2016, 1607.08633.

[32]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[33]  Aniello De Santo,et al.  Performance of $b$-Jet Identification in the ATLAS Experiment , 2016 .

[34]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Atlas Collaboration,et al.  Performance of b-jet identification in the ATLAS experiment , 2015, 1512.01094.

[36]  Luke de Oliveira,et al.  Jet-images — deep learning edition , 2015, Journal of High Energy Physics.

[37]  Lester W. Mackey,et al.  Fuzzy jets , 2015, 1509.02216.

[38]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[39]  Peter Skands,et al.  An introduction to PYTHIA 8.2 , 2014, Comput. Phys. Commun..

[40]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[41]  Sahal Yacoob,et al.  A neural network clustering algorithm for the ATLAS silicon pixel detector , 2014 .

[42]  J. Cogan,et al.  Jet-images: computer vision inspired techniques for jet tagging , 2014, 1407.5675.

[43]  A. collaboration,et al.  A neural network clustering algorithm for the ATLAS silicon pixel detector , 2014, Journal of Instrumentation.

[44]  Navdeep Jaitly,et al.  Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.

[45]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[46]  M. Schwartz,et al.  Hard-Soft-Collinear Factorization to All Orders , 2014, 1403.6472.

[47]  D. Soper,et al.  Finding physics signals with event deconstruction , 2014, 1402.1189.

[48]  M. Schwartz,et al.  An on-shell approach to factorization , 2013, 1306.6341.

[49]  M. Schwartz,et al.  Jet sampling: improving event reconstruction through multiple interpretations , 2013, 1304.2394.

[50]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[51]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[52]  David Krohn,et al.  Nondeterministic approach to tree-based jet substructure. , 2012, Physical review letters.

[53]  M. Cacciari,et al.  FastJet user manual , 2011, 1111.6097.

[54]  C. Collaboration,et al.  Performance of tau-lepton reconstruction and identification in CMS , 2011, 1109.6034.

[55]  Lukás Burget,et al.  Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[56]  D. Soper,et al.  Finding physics signals with shower deconstruction , 2011, 1102.3480.

[57]  J. Thaler,et al.  Identifying boosted objects with N-subjettiness , 2010, 1011.2268.

[58]  K. Black,et al.  Multivariate discrimination and the Higgs+W/Z search , 2010, 1010.3698.

[59]  M. Schwartz,et al.  Seeing in color: jet superstructure. , 2010, Physical review letters.

[60]  Geoffrey E. Hinton,et al.  A Scalable Hierarchical Distributed Language Model , 2008, NIPS.

[61]  M. Cacciari,et al.  The anti-$k_t$ jet clustering algorithm , 2008, 0802.1189.

[62]  S. Mrenna,et al.  PYTHIA 6.4 Physics and Manual , 2006, hep-ph/0603175.

[63]  T. Wengler,et al.  Hadronization Corrections to Jet Cross Sections in Deep-Inelastic Scattering , 1998, hep-ph/9907280.

[64]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[65]  M. Seymour,et al.  Longitudinally-invariant k ⊥ -clustering algorithms for hadron-hadron collisions , 1993 .

[66]  Ellis,et al.  Successive combination jet algorithm for hadron collisions. , 1993, Physical review. D, Particles and fields.

[67]  D. Soper,et al.  Soft Gluons and Factorization , 1988 .

[68]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[69]  S. Coleman,et al.  Singularities in the physical region , 1965 .

[70]  V. M. Ghete,et al.  Performance of $\tau$-lepton reconstruction and identification in CMS , 2012 .

[71]  Matthew D. Schwartz,et al.  Q-jets : A Non-Deterministic Approach to Tree-Based Jet Substructure , 2012 .

[72]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[73]  Yoshua Bengio,et al.  Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.

[74]  A. Doyle,et al.  Monte Carlo Generators for HERA Physics , 1999 .

[75]  Preprint typeset in JHEP style.- PAPER VERSION Cavendish–HEP–97/06 , 1997 .

[76]  D. Soper,et al.  Factorization for short distance hadron-hadron scattering , 1985 .

[77]  Manuel Robbins,et al.  Seeing in Color , 1983 .

[78]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .