Muon identification in the ATLAS calorimeters

The ATLAS experiment at the Large Hadron Collider (LHC) will face the challenge of efficiently selecting interesting candidate events in pp collisions at 14 TeV centre-of-mass energy, whilst rejecting the enormous number of background events. The High-Level-Trigger (HLT second level trigger and Event Filter), which is a software based trigger will need to reduce the first level trigger output rate of kHz to Hz written out to mass storage. In this paper an overview of the steering mechanism of the HLT is given. The HLT Steering is responsible for the scheduling of algorithms and for the global event trigger decision evaluation. The concept of step-wise and seeded selection strategy implemented by the steering will be presented. Integration of large number of trigger menus and sophisticated physics selection strategies will also be discussed. The electron and photon menus will be described to demonstrate that the trigger is well adapted for the physics program at the LHC.

The High-Level-Trigger Steering of the ATLAS Experiment

This paper reviews the Nonlinear Independent Components Analysis and its applications to blind source separation. An overview of the main statistical principles that guide the search for the independent components is formulated. The uniqueness of solution and some algorithms for estimating the nonlinear independent components are discussed. Experimental results using a synthetic database are used for performance comparison. A practical application in experimental high-energy physics is also presented. Index Terms – Nonlinear ICA, Neural Networks, Blind Source Separation, Nonlinear Mixtures, Signal Detection. Introduction The basic linear Independent Component Analysis (ICA) model considers that the set of N measured signals x = [x1 , ..., xN ] is generated by a linear combination of unknown sources s = [s1 , ..., sN ]: As x  (1) where A is the N×N mixing matrix [1] (the N-dimensional vectors s and x denote, respectively, single observations of the source and measured signals, this notation will be used in the rest of the paper). Formulated this way, ICA is also referred to as Blind Source Separation (BSS) method [2] and its purpose is to estimate the original source signals s using only the observed (mixed) data x. A solution can be obtained if one can find the inverse of the mixing matrix B = A: Bx s  (2) A general principle for estimating the matrix B can be found by considering that the original source signals are statistically independent. There are many mathematical methods for calculating the coefficients of matrix B. The nonlinear decorrelation and the maximally nongaussianity are the most applied ones [3]. There are some indeterminacies in the linear ICA model, the order of extraction of the independent components can change and scalar multipliers (positive or negative) may be modifying the estimated components. Fortunately, these limitations are insignificant in most applications [1]. Several good performance algorithms have been proposed for solving the linear ICA/BSS problem such as JADE (where search for independence is performed by cumulant matrix diagonalization) [4], Nonlinear Decorrelation (explores higher-order statistics, and thus independence, through nonlinear transformations) [5] and FastICA (fixed point algorithm that uses statistical and information theoretic measures in the search for independent sources) [6]. In [7], robust solutions for the noisy ICA problem (x=As+n, where n is a random noise vector) were proposed. The linear ICA model has been applied successfully in a large number of signal processing tasks like noise removal [8, 9], passive sonar signal separation [10], telecommunications [11], feature extraction in biomedicine [12], face recognition [13, 14] and experimental particle physics [15]. Unfortunately, in problems where there is some sort of nonlinear phenomenon during the signal mixing process, linear ICA model may present poor results [16]. In a more general formulation, the nonlinear independent component analysis (NLICA) model considers that the measured signals x are formed by a nonlinear instantaneous mixing model:

Nonlinear Independent Component Analysis: Theoretical Review And Applications

http://inspirehep.net/record/1195242/files/indet-98-210.pdf

Global Pattern Recognition in the TRT for the ATLAS LVL2 Trigger 210

ATLAS is one of the four LHC experiments that will start data taking in 2007, designed to cover a wide range of physics topics. The ATLAS trigger system has to cope with a rate of 40 MHz and 23 interactions per bunch crossing. It is divided in three different levels. The first one (hardware based) provides a signature that is confirmed by the following trigger levels (software based) by running a sequence of algorithms and validating the signal step by step, looking only to the region of the space indicated by the first trigger level (seeding). In this presentation, the performance of one of these sequences that run at the event filter level (third level) and is composed of clustering at the calorimeter, track reconstruction and matching, were presented

/pdf/implementation-and-performance-of-the-seeded-reconstruction-1agrw303dj.pdf

Implementation and performance of the seeded reconstruction for the ATLAS event filter selection software

The ATLAS experiment at the Large Hadron Collider (LHC) will face the challenge of efficiently selecting interesting candidate events in pp collisions at 14 TeV center-of-mass energy, whilst rejecting the enormous number of background events. The High-Level Trigger (HLT=second level trigger and Event Filter), which is a software based trigger will need to reduce the level-1 output rate of ap75 kHz to ap200 Hz written out to mass storage. In this talk an overview of the current physics and system performance of the HLT selection for electrons and photons is given. The performance has been evaluated using Monte Carlo simulations and has been partly demonstrated in the ATLAS testbeam in 2004. The efficiency for the signal channels, the rate expected for the selection, the global data preparation and execution times will be highlighted. Furthermore, some physics examples will be discussed to demonstrate that the triggers are well adapted for the physics programme envisaged at the LHC

/pdf/overview-of-the-high-level-trigger-electron-and-photon-2a25eypuni.pdf

Overview of the high-level trigger electron and photon selection for the ATLAS experiment at the LHC

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Overview of the high-level trigger electron and photon selection for the ATLAS experiment at the LHC