16,435 research outputs found
THE ATLAS EXPERIMENT ON-LINE MONITORING AND FILTERING AS AN EXAMPLE OF REAL-TIME APPLICATION
The ATLAS detector, recording LHC particles’ interactions, produces events with rate of40 MHz and size of 1.6 MB. The processes with new and interesting physics phenomena arevery rare, thus an efficient on-line filtering system (trigger) is necessary. The asynchronouspart of that system relays on few thousands of computing nodes running the filtering software.Applying refined filtering criteria results in increase of processing times what may lead tolack of processing resources installed on CERN site. We propose extension to this part ofthe system based on submission of the real-time filtering tasks into the Grid
Polish grid infrastructure for science and research
Structure, functionality, parameters and organization of the computing Grid
in Poland is described, mainly from the perspective of high-energy particle
physics community, currently its largest consumer and developer. It represents
distributed Tier-2 in the worldwide Grid infrastructure. It also provides
services and resources for data-intensive applications in other sciences.Comment: Proceeedings of IEEE Eurocon 2007, Warsaw, Poland, 9-12 Sep. 2007,
p.44
CERN openlab Whitepaper on Future IT Challenges in Scientific Research
This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
This paper focuses on an examination of an applicability of Recurrent Neural
Network models for detecting anomalous behavior of the CERN superconducting
magnets. In order to conduct the experiments, the authors designed and
implemented an adaptive signal quantization algorithm and a custom GRU-based
detector and developed a method for the detector parameters selection. Three
different datasets were used for testing the detector. Two artificially
generated datasets were used to assess the raw performance of the system
whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets
was intended for real-life experiments and model training. Several different
setups of the developed anomaly detection system were evaluated and compared
with state-of-the-art OC-SVM reference model operating on the same data. The
OC-SVM model was equipped with a rich set of feature extractors accounting for
a range of the input signal properties. It was determined in the course of the
experiments that the detector, along with its supporting design methodology,
reaches F1 equal or very close to 1 for almost all test sets. Due to the
profile of the data, the best_length setup of the detector turned out to
perform the best among all five tested configuration schemes of the detection
system. The quantization parameters have the biggest impact on the overall
performance of the detector with the best values of input/output grid equal to
16 and 8, respectively. The proposed solution of the detection significantly
outperformed OC-SVM-based detector in most of the cases, with much more stable
performance across all the datasets.Comment: Related to arXiv:1702.0083
Commissioning of the ATLAS Reconstruction Software with First Data
Looking towards first LHC collisions, the ATLAS detector is being commissioned using all types of physics data available: cosmic rays, beam-halo and beam-gas events produced during LHC single beam operations. In addition to putting in place the trigger and data acquisition chains, commissioning of the full software chain is a primary goal. This is interesting not only to ensure that the reconstruction, monitoring and simulation chains are ready to deal with LHC physics data, but also to understand the detector performance in view of achieving the physics requirements. Cosmic rays have allowed us to study the ATLAS detector in terms of efficiencies, resolutions, channel integrity, and alignment and calibrations. They have also allowed us to test and optimize the muon combined performance algorithms
Assess program: Interactive data management systems for airborne research
Two data systems were developed for use in airborne research. Both have distributed intelligence and are programmed for interactive support among computers and with human operators. The C-141 system (ADAMS) performs flight planning and telescope control functions in addition to its primary role of data acquisition; the CV-990 system (ADDAS) performs data management functions in support of many research experiments operating concurrently. Each system is arranged for maximum reliability in the first priority function, precision data acquisition
Adapting a HEP Application for Running on the Grid
The goal of the EU IST int.eu.grid project is to build middleware facilities which enable the execution of real-time and interactive applications on the Grid. Within this research, relevant support for the HEP application is provided by Virtual Organization, monitoring system, and real-time dispatcher (RTD). These facilities realize the pilot jobs idea that allows to allocate grid resources in advance and to analyze events in real time. In the paper we present HEP Virtual Organization, the details of monitoring, and RTD. We present the way of running the HEP application using the above facilities to fit into the real-time application requirements
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