Optimization of tau identification in ATLAS experiment using multivariate tools

In elementary particle physics the efficient analysis of huge amount of collected data require the use of sophisticated selection and analysis algorithms. We have implemented a Support Vector Machine (SVM) integrated with the CERN TMVA/ROOT package. SVM approach to signal and background separation is based on building a separating hyperplane defined by the support vectors. The margin between them and the hyperplane is maximized. The extensions to a non-linear separation is performed by mapping the input vectors into a high dimensional space, in which data can be linearly separated. The use of kernel functions allows to perform computations in a high dimension feature space without explicitly knowing a mapping function. Our SVM implementation is based on Platt's Sequential Minimal Optimization (SMO) algorithm and includes various kernel functions like a linear function, polynomial and Gaussian. The identification of hadronic decays of tau leptons in the ATLAS experiment using a tau1P3P package is performed using, beside the baseline cut analysis, also multivariate analysis tools: neural network, PDE_RS and our implementation of the SVM algorithm. The use and the comparison of the three algorithms is presented

OPTIMIZATION OF TAU IDENTIFICATION IN ATLAS EXPERIMENT USING MULTIVARIATE TOOLS

Elementary particle physics experiments, which search for very rare processes, require theefficient analysis and selection algorithms able to separate a signal from the overwhelmingbackground. Four learning machine algorithms have been applied to identify τ leptons inthe ATLAS experiment: projective likelihood estimator (LL), Probability Density Estimatorwith Range Searches (PDE-RS), Neural Network, and the Support Vector Machine (SVM).All four methods have similar performance, which is significantly better than the baselinecut analysis. This indicates that the achieved background rejection is close to the maximal achievable performance

Measurements of the Production, Decay and Properties of the Top Quark: A Review

With the full Tevatron Run II and early LHC data samples, the opportunity for furthering our understanding of the properties of the top quark has never been more promising. Although the current knowledge of the top quark comes largely from Tevatron measurements, the experiments at the LHC are poised to probe top-quark production and decay in unprecedented regimes. Although no current top quark measurements conclusively contradict predictions from the standard model, the precision of most measurements remains statistically limited. Additionally, some measurements, most notably the forward-backward asymmetry in top quark pair production, show tantalizing hints of beyond-the-Standard-Model dynamics. The top quark sample is growing rapidly at the LHC, with initial results now public. This review examines the current status of top quark measurements in the particular light of searching for evidence of new physics, either through direct searches for beyond the standard model phenomena or indirectly via precise measurements of standard model top quark properties

Implementation of the SVM algorithm for high energy physics data analysis

Elementary particle physics experiments, searching for very rare processes require the efficientanalysis and selection algorithms able to separate signal from the overwhelming background. Inthe last ten years a number of powerful kernel-based learning machines, like Support VectorMachines (SVM), have been developed. SVM approach to signal and background separation isbased on building a separating hyperplane defined by the support vectors. The margin betweenthem and the hyperplane is maximized. The extensions to a non-linear separation are performed bymapping the input vectors into a high dimensional space, in which data can be linearly separated.The use of kernel functions allows us to perform computations in a high dimension feature spacewithout explicitly knowing a mapping function.We have implemented an SVM algorithm and integrated it with the CERN ROOT package,which is currently a standard analysis tool used by elementary particle physicists. We also used theimplemented SVM package to identify hadronic decays of τ leptons in the ATLAS experiment atLHC accelerator. The performance of the method is compared to the likelihood estimator, whichdoes not take into account correlations between variables. The use of SVM significantly reducesthe number of background events

Machine learning at the energy and intensity frontiers of particle physics

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics

Modern machine learning in the presence of systematic uncertainties for robust and optimized multivariate data analysis in high-energy particle physics

In high energy particle physics, machine learning has already proven to be an indispensable technique to push data analysis to the limits. So far widely accepted and successfully applied in the event reconstruction at the LHC experiments, machine learning is today also increasingly often part of the final steps of an analysis and, for example, used to construct observables for the statistical inference of the physical parameters of interest. This thesis presents such a machine learning based analysis measuring the production of Standard Model Higgs bosons in the decay to two tau leptons at the CMS experiment and discusses the possibilities and challenges of machine learning at this stage of an analysis. To allow for a precise and reliable physics measurement, the application of the chosen machine learning model has to be well under control. Therefore, novel techniques are introduced to identify and control the dependence of the neural network function on features in the multidimensional input space. Further, possible improvements of machine learning based analysis strategies are studied. A novel solution is presented to maximize the expected sensitivity of the measurement to the physics of interest by incorporating information about known uncertainties in the optimization of the machine learning model, yielding an optimal statistical inference in the presence of systematic uncertainties

Prospects for Higgs Searches via VBF at the LHC with the ATLAS Detector

We report on the potential for the discovery of a Standard Model Higgs boson with the vector boson fusion mechanism in the mass range 115 with the ATLAS experiment at the LHC. Feasibility studies at hadron level followed by a fast detector simulation have been performed for H\to W^{(*)}W^{(*)}\to l^+l^-\sla{p_T}, $H\to\gamma\gamma$ and $H\to ZZ\to l^+l^-q\bar{q}$. The results obtained show a large discovery potential in the range 115. Results obtained with multivariate techniques are reported for a number of channels.Comment: 14 pages, 4 figures, contributed to 2003 Les Houches Workshop on Physics at TeV Colliders. Incorporated comments from ATLAS referee