Machine learning algorithms for the Belle II experiment and their validation on Belle data

[1]  T. Aushev,et al.  Evidence for B- → τ- ν(τ) with a hadronic tagging method using the full data sample of Belle. , 2012, Physical review letters.

[2]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[3]  R. Koenker,et al.  Regression Quantiles , 2007 .

[4]  K. Cranmer,et al.  RECAST — extending the impact of existing analyses , 2010, 1010.2506.

[5]  K. Arinstein,et al.  Measurement of the branching fraction of B → τντ decays with the semileptonic tagging method , 2015 .

[6]  Razvan Pascanu,et al.  A simple neural network module for relational reasoning , 2017, NIPS.

[7]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[8]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[9]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[10]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[11]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[12]  Matthew D. Schwartz,et al.  Quantum Field Theory and the Standard Model , 2013 .

[13]  A. Dell'Acqua,et al.  Geant4 - A simulation toolkit , 2003 .

[14]  Johannes Grygier Development and Evaluation of a new Calorimeter Based Variable for Missing Energy Analyses at the Belle Experiment , 2013 .

[15]  Gregory Schott,et al.  Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods , 2013 .

[16]  Martin Wiebusch,et al.  Status of the two-Higgs-doublet model of type II , 2013, 1305.1649.

[17]  Gilles Louppe,et al.  Learning to Pivot with Adversarial Networks , 2016, NIPS.

[18]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[19]  Michael Feindt,et al.  A hierarchical NeuroBayes-based algorithm for full reconstruction of B mesons at B factories , 2011, 1102.3876.

[20]  SPlot: A Statistical tool to unfold data distributions , 2004, physics/0402083.

[21]  J. Friedman Stochastic gradient boosting , 2002 .

[22]  R. Kulasiri,et al.  Search for B− → µ −ν¯µ Decays at the Belle Experiment , 2017 .

[23]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[24]  Mike Williams,et al.  uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers , 2013, 1305.7248.

[25]  Stephen Wolfram,et al.  Observables for the Analysis of Event Shapes in e+ e- Annihilation and Other Processes , 1978 .

[26]  D. Lange,et al.  The EvtGen particle decay simulation package , 2001 .

[27]  M. Feindt,et al.  The NeuroBayes neural network package , 2006 .

[28]  Fabian Moser,et al.  RAVE—a Detector-independent vertex reconstruction toolkit , 2007 .

[29]  G. Amdhal,et al.  Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).

[30]  Benjamin Lipp,et al.  sPlot-based Training of Multivariate Classifiers in the Belle II Analysis Software Framework , 2015 .

[31]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[32]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[34]  P. Baldi,et al.  Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.

[35]  Thomas Kuhr,et al.  The Full Event Interpretation , 2018, Computing and Software for Big Science.

[36]  D Martschei,et al.  Advanced event reweighting using multivariate analysis , 2012 .

[37]  Michael Feindt,et al.  An algorithm for quantifying dependence in multivariate data sets , 2012, 1207.0981.

[38]  R. Rigby,et al.  Generalized additive models for location, scale and shape , 2005 .

[39]  C. Schwanda SuperKEKB machine and Belle II detector status , 2010 .

[40]  W. Hulsbergen,et al.  Decay chain fitting with a Kalman filter , 2005, physics/0503191.

[41]  Matthias Steinhauser,et al.  Weak radiative decays of the B meson and bounds on MH±\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{H^\pm }$$\end , 2017, The European Physical Journal C.

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

[43]  Marina Artuso,et al.  Search for the Decays B_{s} , 2017 .

[44]  D. G. Hitlin,et al.  Search for B^+→ℓ^+ν_ℓ recoiling against B^-→D^0ℓ^-ν̅ X , 2010 .

[45]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[46]  Thomas Keck,et al.  FastBDT: A Speed-Optimized Multivariate Classification Algorithm for the Belle II Experiment , 2017, Computing and Software for Big Science.

[47]  A. Bondar,et al.  The Belle detector , 1998 .

[48]  T. Aushev,et al.  Search for $\boldsymbol{B\to h\nu\bar{\nu}}$ decays with semileptonic tagging at Belle , 2017, 1702.03224.

[49]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[50]  K. Arinstein,et al.  Search for B þ → l þ ν l γ decays with hadronic tagging using the full Belle data sample , 2015 .

[51]  Christian Pulvermacher,et al.  Analysis Software and Full Event Interpretation for the Belle II Experiment , 2016 .

[52]  R. Frühwirth Application of Kalman filtering to track and vertex fitting , 1987 .

[53]  Vladimir Gligorov,et al.  New approaches for boosting to uniformity , 2014, 1410.4140.

[54]  K. Arinstein,et al.  Belle II Technical Design Report , 2010, 1011.0352.

[55]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[56]  DorisYangsoo Kim The software library of the Belle II experiment , 2016 .

[57]  Ryan P. Adams,et al.  Gradient-based Hyperparameter Optimization through Reversible Learning , 2015, ICML.

[58]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[59]  Edward I. Shibata Hadronic structure in τ− → π− π− π+ ντ decays , 2003 .

[60]  R. Itoh BASF - BELLE AnalysiS Framework , 1997 .

[61]  Bastian Kronenbitter,et al.  Measurement of the branching fraction of $B^{+} \to \tau^{+}\nu_{\tau}$ decays at the Belle experiment , 2016 .

[62]  P. Harris,et al.  Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure , 2016, 1603.00027.

[63]  Dumitru Erhan,et al.  Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[64]  E. Parzen On Estimation of a Probability Density Function and Mode , 1962 .

[65]  F. Tegenfeldt,et al.  TMVA - Toolkit for multivariate data analysis , 2012 .

[66]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[67]  Vladimir Vapnik,et al.  Principles of Risk Minimization for Learning Theory , 1991, NIPS.

[68]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[69]  Bjarne Stugu,et al.  Evidence of B-->tau nu decays with hadronic B tags , 2012, 1207.0698.

[70]  Dennis Weyland,et al.  Continuum Suppression with Deep Learning techniques for the Belle II Experiment , 2017 .

[71]  M. Huschle Measurement of the branching ratio of $B \to D^{(*)} \tau \nu_{\tau}$ relative to $B \to D^{(*)} l \nu_{l}$ decays with hadronic tagging at Belle , 2016 .

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