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