G. Comune

Studies for a common selection software environment in ATLAS: from the level-2 trigger to the offline reconstruction

The ATLAS High Level Triggers (HLT) primary function of event selection will be accomplished with a Level-2 trigger farm and an event filter (EF) farm, both running software components developed in the ATLAS offline reconstruction framework. While this approach provides a unified software framework for event selection, it poses strict requirements on offline components critical for the Level-2 trigger. A Level-2 decision in ATLAS must typically be accomplished within 10 ms and with multiple event processing in concurrent threads. To address these constraints, prototypes have been developed that incorporate elements of the ATLAS data flow, high level trigger, and offline framework software. To realize a homogeneous software environment for offline components in the HLT, the Level-2 Steering Controller was developed. With electron/gamma- and muon-selection slices it has been shown that the required performance can be reached, if the offline components used are carefully designed and optimized for the application in the HLT.

Architecture of the ATLAS High Level Trigger Event Selection Software

We present an overview of the strategy for Event Selection at the ATLAS High Level Trigger and describe the architecture and main components of the software developed for this purpose.

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

The ATLAS experiment is one of two general purpose experiments to start running at the Large Hadron Collider in 2007. The short bunch crossing period of 25 ns and the large background of soft-scattering events overlapped in each bunch crossing pose serious challenges that the ATLAS trigger must overcome in order to efficiently select interesting events. The ATLAS trigger consists of a hardware-based first-level trigger and of a software-based high-level trigger, which can be further divided into the second-level trigger and the event filter. This paper presents the current state of development of methods to be used in the high-level trigger to select events containing electrons or photons with high transverse momentum. The performance of these methods is presented, resulting from both simulation studies, timing measurements, and test beam studies.

Design, deployment and functional tests of the online event filter for the ATLAS experiment at LHC

The Event Filter (EF) selection stage is a fundamental component of the ATLAS Trigger and Data Acquisition architecture. Its primary function is the reduction of data flow and rate to values acceptable by the mass storage operations and by the subsequent offline data reconstruction and analysis steps. The computing instrument of the EF is organized as a set of independent subfarms, each connected to one output of the Event Builder (EB) switch fabric. Each subfarm comprises a number of processors analyzing several complete events in parallel. This paper describes the design of the ATLAS EF system, its deployment in the 2004 ATLAS combined test beam together with some examples of integrating selection and monitoring algorithms. Since the processing algorithms are not explicitly designed for EF but are adapted from the offline ones, special emphasis is reserved to system reliability and data security, in particular for the case of failures in the processing algorithms. Other key design elements have been system modularity and scalability. The EF shall be able to follow technology evolution and should allow for using additional processing resources possibly remotely located

Portable Gathering System for Monitoring and Online Calibration at ATLAS.

During the runtime of any experiment, a central monitoring system that detects problems as soon as they appear has an essential role. In a large experiment, like ATLAS, the online data acquisition system is distributed across the nodes of large farms, each of them running several processes that analyse a fraction of the events. In this architecture, it is necessary to have a central process that collects all the monitoring data from the different nodes, produces full statistics histograms and analyses them. In this paper we present the design of such a system, called the gatherer. It allows to collect any monitoring object, such as histograms, from the farm nodes, from any process in the

Online muon reconstruction in the ATLAS level-2 trigger system

To cope with the 40 MHz event production rate of LHC, the trigger of the ATLAS experiment selects events in three sequential steps of increasing complexity and accuracy whose final results are close to the offline reconstruction. The Level-1, implemented with custom hardware, identifies physics objects within Regions of Interests and operates with a first reduction of the event rate to 75 kHz. The higher trigger levels, Level-2 and Level-3, provide a software based event selection which further reduces the event rate to about 100 Hz. This paper presents the algorithm (/spl mu/Fast) employed at Level-2 to confirm the muon candidates flagged by the Level-1. /spl mu/Fast identifies hits of muon tracks inside the barrel region of the Muon Spectrometer and provides a precise measurement of the muon momentum at the production vertex. The algorithm must process the Level-1 muon output rate (/spl sim/20 kHz), thus particular care has been taken for its optimization. The result is a very fast track reconstruction algorithm with good physics performance which, in some cases, approaches that of the offline reconstruction: it finds muon tracks with an efficiency of about 95% and computes p/sub T/ of prompt muons with a resolution of 5.5% at 6 GeV and 4.0% at 20 GeV. The algorithm requires an overall execution time of /spl sim/1 ms on a 100 SpecInt95 machine and has been tested in the online environment of the Atlas detector test beam.

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

ATLAS is one of the four major Large Hadron Collider (LHC) experiments that will start data taking in 2007. It is designed to cover a wide range of physics topics. The ATLAS trigger system has to be able to reduce an initial 40 MHz event rate, corresponding to an average of 23 proton-proton inelastic interactions per every 25 ns bunch crossing, to 200 Hz admissible by the Data Acquisition System. The ATLAS trigger is divided in three different levels. The first one provides a signal describing an event signature using dedicated custom hardware. This signature must be confirmed by the High Level Trigger (HLT) which using commercial computing farms performs an event reconstruction by running a sequence of algorithms. The validity of a signature is checked after every algorithm execution. A main characteristic of the ATLAS HLT is that only the data in a certain window around the position flagged by the first level trigger are analyzed. In this work, the performance of one sequence that runs at the Event Filter level (third level) is demonstrated. The goal of this sequence is to reconstruct and identify high transverse momentum electrons by performing cluster reconstruction at the electromagnetic calorimeter, track reconstruction at the Inner Detector, and cluster track matching.

Muon Event Filter Software for the ATLAS Experiment at LHC

At LHC the 40 MHz bunch crossing rate dictates a high selectivity of the ATLAS Trigger system, which has to keep the full physics potential of the experiment in spite of a limited storage capability. The Level-1 trigger, implemented in a custom hardware, will reduce the initial rate S. Armstrong a , K. Assamagan a , A. dos Anjos a , J.T.M. Baines c , C.P. Bee d , M. Biglietti e , M. Bellomo w , J.A. Bogaerts f , V. Boisvert f , M. Bosman g , B. Caron h , P. Casado g , G. Cataldi i , G. Carlino dd D. Cavalli j , M. Cervetto k , G. Comune l , F. Conventi dd , P. Conde Muino f , A. De Santo m , M. Diaz Gomez n , M. Dosil g , N. Ellis f , D. Emeliyanov c , B. Epp o , S. Falciano p , A. Farilla q , S. George m , V. Ghete o , S. Gonzlez r , M. Grothe f , S. Kabana l , A. Khomich s , G. Kilvington m , N. Konstantinidis t , A. Kootz u , A. Lowe m , L. Luminari p , T. Maeno f , J. Masik v , A. Di Mattia p , C. Meessen d , A.G. Mello b , G. Merino g , R. Moore h , P. Morettini k , A. Negri w , N. Nikitin x , A. Nisati p , C. Padilla f , N. Panikashvili y , F. Parodi k , V. Perez Reale l , J.L. Pinfold h , P. Pinto f , M. Primavera i , Z. Qian d , S. Resconi j , S. Rosati f , C. Sanchez g , C. Santamarina f , D.A. Scannicchio w , C. Schiavi k , E. Segura g , J.M. de Seixas b , S. Sivoklokov x , R. Soluk h , E. Stefanidis t , S. Sushkov g , M. Sutton t , S. Tapprogge z , E. Thomas l , F. Touchard d , B. Venda Pinto aa , A. Ventura i , V. Vercesi w , P. Werner f , S. Wheeler h,bb , , F.J. Wickens c , W. Wiedenmann r , M. Wielers cc , G. Zobernig r . a Brookhaven National Laboratory (BNL), Upton, New York, USA, b Universidade Federal do Rio de Janeiro, COPPE-EE, Rio de Janeiro, Brazil, c Appleton Laboratory, Chilton, Didcot, UK, d Centre de Physique des Particules de Marseille, IN2P3-CNRS-Université d’AixMarseille 2, France, e University of Michigan, Ann Arbor, Michigan, USA, f CERN, Geneva, Switzerland, g Institut de Fsica d’Altes Energies (IFAE), Universidad Autnoma de Barcelona, Barcelona, Spain, h University of Alberta, Edmonton, Canada, i Dipartimento di Fisica dell’Università di Lecce e I.N.F.N., Lecce, Italy, j Dipartimento di Fisica dell’Università di Milano e I.N.F.N., Milan, Italy, k Dipartimento di Fisica dell’Università di Genova e I.N.F.N., Genoa, Italy, l Laboratory for High Energy Physics, University of Bern, Switzerland, m Department of Physics, Royal Holloway, University of London, Egham, UK, n Section de Physique, Université de Genve, Switzerland, o Institut fr Experimentalphysik der Leopald-Franzens Universitt, Innsbruck, Austria, p Dipartimento di Fisica dell’Università di Roma ’La Sapienza’ e I.N.F.N., Rome, Italy, q Dipartimento di Fisica dell’Università di Roma ’Roma Tre’ e I.N.F.N., Rome, Italy, r Department of Physics, University of Wisconsin, Madison, Wisconsin, USA, s Lehrstuhl fr Informatik V, Universitt Mannheim, Mannheim, Germany, t Department of Physics and Astronomy, University College London, London, UK, u Fachbereich Physik, Bergische Universitt Wuppertal, Germany, v Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic, w Dipartimento di Fisica Nucleare e Teorica dell’Università di Pavia e INFN, Pavia, Italy, x Institute of Nuclear Physics, Moscow State University, Moscow, Russia, y Department of Physics, Technion, Haifa, Israel, z Institut fr Physik, Universitt Mainz, Mainz, Germany, aa CFNUL Universidade de Lisboa, Faculdade de Cincias, Lisbon, Portugal, bb University of California at Irvine, Irvine, USA, dd Dipartimento di Fisica dell’Università degli studi di Napoli “Federico II” e I.N.F.N., Napoli, Italy to 75 kHz and is followed by the software based Level-2 and Event Filter, usually referred as High Level Triggers (HLT), which further reduce the rate to about 100 Hz. In this paper an overview of the implementation of the offline muon recostruction algortihms MOORE (Muon Object Oriented REconstruction) and MuId (Muon Identification) as Event Filter in the ATLAS online framework is given. The MOORE algorithm performs the reconstruction inside the standalone Muon Spectrometer, thus providing a precise measurement of the muon track parameters outside the calorimeters; MuId combines the measurements of all ATLAS sub-detectors in order to identify muons and provides the best estimate of their momentum at the production vertex. In the HLT implementation the muon reconstruction can be executed in ”full scan mode”, performing pattern recognition in the whole muon spectrometer, or in the ”seeded mode”, taking advantage of the results of the earlier trigger levels. An estimate of the execution time will be presented along with the performances in terms of efficiency, momentum resolution and rejection power of muons coming from hadron decays and of fake muon tracks, due to accidental hit correlations in the high background environment of the experiment. THE ATLAS HIGH LEVEL TRIGGER The LHC bunch crossing frequency will be 40 MHz and will have to be reduced to the order of 100 Hz by the ATLAS Trigger and Data Acquisition (TDAQ) systems in order to achieve the foreseen storage capability and meet the physics requirements of the experiments. The Level-1 trigger (LVL1) [1], implemented in a custom hardware, will make the first level of event selection, reducing the initial event rate to less than 75 kHz in less then 2.5 μs. Operation at up to about 100 kHz is possible with somewhat increased dead time. The result of LVL1 contains informations about the type of trigger and the position of possible particle candidates that causes the event to be accepted. The second (LVL2) and third level, called Event Filter (EF), are software based systems and are referred togheter as High Level Triggers (HLT). The HLT must reduce the event rate further down to O(100) Hz. Each selected event will have a total size of 1.5 Mbyte giving a required storage capability of a few hundred Mbyte/s. The LVL2 and the Event Filter differ in several important respects. The LVL2 is composed of a combination of high rejection power with fast, limited precision algorithms using modest computing power; the Event Filter instead has a modest rejection power with slower, high precision algorithms using more A T L -D A Q -C O N F20 05 -0 08

Implementation and Performance of the Seeded Reconstruction for the ATLAS Event Filter Selection Software

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

A Level-2 trigger algorithm for the identification of muons in the ATLAS Muon Spectrometer

The ATLAS Level-2 trigger provides a software-based event selection after the initial Level-1 hardware trigger. For the muon events, the selection is decomposed in a number of broad steps: first, the Muon Spectrometer data are processed to give physics quantities associated to the muon track (standalone feature extraction) then, other detector data are used to refine the extracted features. The “μFast” algorithm performs the standalone feature extraction, providing a first reduction of the muon event * S. Armstrong, A. dos Anjos, J.T.M. Baines, C.P. Bee, M. Biglietti, J.A. Bogaerts, V. Boisvert, M. Bosman, B. Caron, P. Casado, G. Cataldi, D. Cavalli, M. Cervetto, G. Comune, P. Conde Muino, A. De Santo, A. Di Mattia, M. Diaz Gomez, M. Dosil, N. Ellis, D. Emeliyanov, B. Epp, S. Falciano, A. Farilla, S. George, V. Ghete, S. González, M. Grothe, S. Kabana, A. Khomich, G. Kilvington, N. Kostantinidis, A. Kootz, A. Lowe, C. Luci, L. Luminari, T. Maeno, F. Marzano, J. Masik, C. Meessen, A.G. Mello, G. Merino, R. Moore, P. Morettini, A. Negri, N. Nikitin, A. Nisati, C. Padilla, N. Panikashvili, F. Parodi, E. Pasqualucci, V. Perez Reale, J.L.Pinfold, P. Pinto, Z. Qian, S. Resconi, S. Rosati, C. Sanchez, C. Santamarina, D.A. Scannicchio, C. Schiavi, E. Segura, J.M. de Seixas, S. Sivoklokov, R. Soluk, E. Stefanidis, S. Sushkov, M. Sutton, S Tapprogge, E. Thomas, F. Touchard, B. Venda Pinto, V. Vercesi, P. Werner, S. Wheeler, F.J. Wickens, W. Wiedenmann, M. Wielers, G. Zobernig. Brookhaven National Laboratory (BNL), Upton, NewYork, USA, Universidade Federal do Rio de Janeiro, COPPE/EE, Rio de Janeiro, Barzil. Rutherford Appleton Laboratory, Chilton, Didcot, UK, Centre de Physique des Particules de Marseille, IN2P3-CNRS-Univesité d’AixMarseille 2, France, University of Michigan, Ann Arbor, Michigan, USA, CERN, Geneva, Switzerland, Institut de Física d’Altes Energies (IFAE), Universidad Autónoma de Barcelona, Barcelona, Spain, University of Alberta, Edmonton, Canada, Dipartimento di Fisica dell’Università di Lecce e I.N.F.N., Lecce, Italy, Dipartimento di Fisica dell’Università di Milano e I.N.F.N., Milan, Italy, Dipartimento di Fisica dell’Università di Genova e I.N.F.N., Genoa, Italy, Laboratory for High Energy Physics, University of Bern, Switzerland, Department of Physics, Royal Holloway, University of London, Egham, UK, Dipartimento di Fisica dell’Università di Roma ‘La Sapienza’ e I.N.F.N., Rome, Italy, Section de Physique, Université de Genève, Switzerland, Institut für Experimentalphysik der Leopald-Franzens Universität, Innsbruck, Austria, Dipartimento di Fisica dell’Università di Roma ‘Roma Tre’ e I.N.F.N., Rome, Italy, Department of Physics, University of Wisconsin, Madison, Wisconsin, USA, Lehrstuhl für Informatik V, Universität Mannheim, Mannheim, Germany, Department of Physics and Astronomy, University College London, London, UK, Fachbereich Physik, Bergische Universität Wuppertal, Germany, Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic, Dipartimento di Fisica Nucleare e Teorica dell’Università di Pavia e I.N.F.N., Pavia, Italy, Institute of Nuclear Physics, Moscow State University, Moscow, Russia, Department of Physics, Technion, Haifa, Israel, Institut für Physik, Universität Mainz, Mainz, Germany, CFNUL – Universidade de Lisboa, Facultade de Ciências, Lisbon, Portugal, University of California at Irvine, Inrvine, USA, University of Victoria, Victoria, Canada. rate from Level-1. It confirms muon track candidates with a precise measurement of the muon momentum. The algorithm is designed to be both conceptually simple and fast so as to be readily implemented in the demanding online environment in which the Level-2 selection code will run. Nevertheless its physics performance approaches, in some cases, that of the offline reconstruction algorithms. This paper describes the implemented algorithm together with the software techniques employed to increase its timing performances.