A5046977309- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Dark Matter and Cosmic Phenomena
- Computational Physics and Python Applications
- Neutrino Physics Research
- Cosmology and Gravitation Theories
- Radiation Detection and Scintillator Technologies
- Distributed and Parallel Computing Systems
- Astrophysics and Cosmic Phenomena
- Medical Imaging Techniques and Applications
- advanced mathematical theories
- Black Holes and Theoretical Physics
- Atomic and Subatomic Physics Research
- Advanced Data Storage Technologies
- Neural Networks and Applications
- Muon and positron interactions and applications
- Nuclear Physics and Applications
- Advanced Clustering Algorithms Research
- Structural Analysis of Composite Materials
- Scientific Computing and Data Management
- Algorithms and Data Compression
- Agricultural and Food Sciences
- Digital Radiography and Breast Imaging
Brookhaven National Laboratory
2014-2025
University of California, Santa Cruz
2023-2024
Rutherford Appleton Laboratory
2011-2024
Istanbul University
2024
A. Alikhanyan National Laboratory
2024
Atlas Scientific (United States)
2024
University of Geneva
2024
The University of Adelaide
2016-2023
Istituto Nazionale di Fisica Nucleare, Gruppo Collegato di Udine
2023
Istituto Nazionale di Fisica Nucleare, Sezione di Trieste
2023
This paper presents the latest results from Ringer algorithm, which is based on artificial neural networks for electron identification at online filtering system of ATLAS particle detector, in context LHC experiment CERN. The algorithm performs topological feature extraction using calorimetry information (energy measurements). extracted presented to a network classifier. Studies showed that achieves high detection efficiency, while keeping false alarm rate low. Optimizations, guided by...
The ATLAS experiment is one of the multi-purpose experiments at Large Hadron Collider (LHC) CERN, constructed to study elementary particle interactions in collisions high-energy proton beams. Twelve different sub detectors as well common experimental infrastructure are controlled and monitored by Detector Control System (DCS) using a highly distributed system 140 server machines running industrial SCADA product PVSS. Higher level control layers allow for automatic procedures, efficient error...
The ATLAS detector is undergoing intense commissioning effort with cosmic rays preparing for the first LHC collisions late 2009. Combined runs all of subsystems are being taken in order to evaluate performance. This an unique opportunity also trigger system be studied different operation modes, such as event rates and configuration. starts a hardware based which tries identify regions where interesting physics objects may found (eg: large transverse energy depositions calorimeter system). An...
Cosmic rays with kinetic energy larger than 10/sup 20/ eV have been detected by two experiments, AGASA and HIRES. The nature origin of these particles are not known. Acceleration mechanisms that can produce at energies could be due to yet unknown sources energy. extreme cosmic (EECR) rare reaching earth a rate few per square kilometer year. rarity events implies large detector arrays required making construction cost one the main issues. We exploring possibility detect EECR using bi-static...
The ATLAS trigger will need to achieve a 10 -7 rejection factor against proton-proton collisions, and still be able efficiently select interesting events.After first hardware-implemented processing level, the final event selection is done by high-level (HLT), implemented on software.With more than 100 contributors around 250 different packages, thorough validation of HLT software essential.This paper describes existing infrastructure used for validating software.
A neural classifier is developed for passive sonar signals. For achieving data compaction and high performance on the identification of ship classes, processing performed preprocessed in frequency domain. Preprocessing comprises averaged spectral analysis over contiguous acquisition windows, background noise estimation wavelet transformation. The overall discrimination efficiency achieved was better than 94%, considering four classes ships.
A passive radio detection system is proposed for the and study of ultra high energy cosmic rays (UHECR) showers meteors. TV FM signals reflected by ionization clouds produced meteors are clearly detectable. This technique known as meteor scatter well established. UHECRs produces in principle similar trails. station operating at BNL continuously recording from a group three antennas tuned to low end commercial VHF broadcast frequencies. An offline analysis, here described, allows event...
The ATLAS detector operated very successfully at the LHC Run 1 data taking period collecting a large number of events used for different physics analyses, such as ones leading to discovery Higgs boson well search beyond Standard Model physics. In main channels related finding Higgs, calorimeter system played major role by measuring energy photons, electrons, jets, taus and neutrinos, via missing transverse measurement. trigger selects from huge amount produced every second, those few that...
For the hadronic calorimeter of ATLAS, TileCal, neural processing is used to establish an efficient methodology for online particle identification in beam tests prototypes. Although purity usually very good a selected type, background from wrong-type particles cannot be avoided and routinely identified offline analysis. The proposed system trained identify electrons, pions, muons at different energy levels it achieves more than 90% efficiency terms identification. being implemented by...
Neural networks are applied to a particle discrimination problem in high-energy physics. Information from specific detector that measures the energy of incoming particles (a calorimeter) is used feed input nodes discriminator for identification electrons, pions and muons. During training phase, neural was capable identify impurities original data sample obtained beams this capability cross checked with classical method. Having such removed, achieved efficiencies 99.6% (pions), 99.5% (muons)...
The present work describes a neural particle classifier system based on topological mapping of the segmented information provided by high-energy calorimeter, detector that measures energy incoming particles. achieved classification efficiencies are above 97.50% for higher beams, even when experimental data exhibit unavoidable contamination due to beam generation process, what could jeopardize performance. Some deterioration in performance lower range is also discussed. reduction...
A particle discrimination problem in high-energy physics is addressed by optimal linear filtering and neural processing on experimental data acquired from a highly segmented calorimeter, which detector that measures the energy of incoming particles. It shown both approaches are able to identify impurities typically appear sample achieve efficiencies higher than 98%.
Um sistema classificador neuronal é desenvolvido para identificar três classes de partículas em física experimental altas energias. O usa a extração componentes principais discriminação combinar compacticidade e alta eficiência classificação, identificando, inclusive, contaminação presente nos dados experimentais. Mais 97% dos eventos analisados são corretamente classificados.
For the hadronic calorimeter of ATLAS detector, TileTransfer has been developed as a Web system to facilitate transferring data that are produced during calibration testbeam periods. It automatically searches, stages and provides link download selected stored at remote file center. The an interface with Run Info Database, which contains description all test beam runs. In order optimize transmission, is connected central repository stores information latest accesses. Once client host connects...