On robust estimation of the location parameter

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb−1 of proton-proton collision data collected at √s=13 TeV between 2015 and 2017.

/pdf/electron-and-photon-performance-measurements-with-the-atlas-3lwdebxmt2.pdf

Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data

The Constrained Minimal Supersymmetric Standard Model (CMSSM) is one of the simplest and most widely-studied supersymmetric extensions to the standard model of particle physics. Nevertheless, current data do not suciently constrain the model parameters in a way completely independent of priors, statistical measures and scanning techniques. We present a new technique for scanning supersymmetric parameter spaces, optimised for frequentist prole likelihood analyses and based on Genetic Algorithms. We apply this technique to the CMSSM, taking into account existing collider and cosmological data in our global t. We compare our method to the MultiNest algorithm, an ecient Bayesian technique, paying particular attention to the best-t points and implications for particle masses at the LHC and dark matter searches. Our global best-t point lies in the focus point region. We nd many high-likelihood points in both the stau co-annihilation and focus point regions, including a previously neglected section of the co-annihilation region at large m0. We show that there are many high-likelihood points in the CMSSM parameter space commonly missed by existing scanning techniques, especially at high masses. This has a signicant inuence on the derived condence regions for parameters and observables,

likelihood analysis of the constrained MSSM with genetic algorithms

In high-energy physics, with the search for ever smaller signals in ever larger data sets, it has become essential to extract a maximum of the available information from the data. Multivariate classification methods based on machine learning techniques have become a fundamental ingredient to most analyses. Also the multivariate classifiers themselves have significantly evolved in recent years. Statisticians have found new ways to tune and to combine classifiers to further gain in performance. Integrated into the analysis framework ROOT, TMVA is a toolkit which hosts a large variety of multivariate classification algorithms. Training, testing, performance evaluation and application of all available classifiers is carried out simultaneously via user-friendly interfaces. With version 4, TMVA has been extended to multivariate regression of a real-valued target vector. Regression is invoked through the same user interfaces as classification. TMVA 4 also features more flexible data handling allowing one to arbitrarily form combined MVA methods. A generalised boosting method is the first realisation benefiting from the new framework. TMVA 4.0.1 – Toolkit for Multivariate Data Analysis with ROOT Copyright c © 2005-2009, Regents of CERN (Switzerland), DESY (Germany), MPI-Kernphysik Heidelberg (Germany), University of Bonn (Germany), and University of Victoria (Canada). BSD license: http://tmva.sourceforge.net/LICENSE. Authors: Andreas Hoecker (CERN, Switzerland) 〈andreas.hoecker@cern.ch〉, Peter Speckmayer (CERN, Switzerland) 〈peter.speckmayer@cern.ch〉, Jörg Stelzer (CERN, Switzerland) 〈joerg.stelzer@cern.ch〉, Jan Therhaag (Universität Bonn, Germany) 〈therhaag@physik.uni-bonn.de〉, Eckhard von Toerne (Universität Bonn, Germany) 〈evt@physik.uni-bonn.de〉, Helge Voss (MPI für Kernphysik Heidelberg, Germany) 〈helge.voss@cern.ch〉, Moritz Backes (Geneva University, Switzerland) moritz.backes@cern.ch, Tancredi Carli (CERN, Switzerland) 〈tancredi.carli@cern.ch〉, Or Cohen (CERN, Switzerland and Technion, Israel) 〈or.cohen@cern.ch〉, Asen Christov (Universität Freiburg, Germany) 〈christov@physik.uni-freiburg.de〉, Krzysztof Danielowski (IFJ and AGH/UJ, Krakow, Poland) 〈Krzysztof.Danielowski@cern.ch〉, Dominik Dannheim (CERN, Switzerland) 〈Dominik.Dannheim@cern.ch〉, Sophie Henrot-Versillé (LAL Orsay, France) 〈versille@lal.in2p3.fr〉, Matthew Jachowski (Stanford University, USA) 〈jachowski@stanford.edu〉, Kamil Kraszewski (IFJ and AGH/UJ, Krakow, Poland) 〈kamil.bartlomiej.kraszewski@cern.ch〉, Attila Krasznahorkay Jr. (CERN, CH, and Manchester U., UK) 〈Attila.Krasznahorkay@cern.ch〉, Maciej Kruk (IFJ and AGH/UJ, Krakow, Poland) 〈maciej.mateusz.kruk@cern.ch〉, Yair Mahalalel (Tel Aviv University, Israel) 〈yair@mahalalel.com〉, Rustem Ospanov (University of Texas, USA) 〈rustem@fnal.gov〉, Xavier Prudent (LAPP Annecy, France) 〈prudent@lapp.in2p3.fr〉, Doug Schouten (S. Fraser U., Canada) 〈dschoute@sfu.ca〉, Fredrik Tegenfeldt (Iowa University, USA) 〈fredrik.tegenfeldt@cern.ch〉, Arnaud Robert (LPNHE Paris, France) 〈arobert@lpnhe.in2p3.fr〉, Alexander Voigt (CERN, Switzerland) 〈alexander.voigt@cern.ch〉, Kai Voss (University of Victoria, Canada) 〈kai.voss@cern.ch〉, Marcin Wolter (IFJ PAN Krakow, Poland) 〈marcin.wolter@ifj.edu.pl〉, Andrzej Zemla (IFJ PAN Krakow, Poland) 〈zemla@corcoran.if.uj.edu.pl〉, and valuable contributions from many users, please see acknowledgements.

/pdf/tmva-toolkit-for-multivariate-data-analysis-with-root-users-4ek4as5qy7.pdf

TMVA - Toolkit for Multivariate Data Analysis with ROOT : Users guide

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TMVA - Toolkit for Multivariate Data Analysis with ROOT : Users guide