V Shigaev | AMiner学者主页

1Performance of the Scintillator System Prototype of the NUCLEON Space Experiment

L.G. Tkatchev, V.F. Boreiko, V. Grebenyuk, I. Chupin, E. Kajfasz,Z. Krumstein, Dmitri Podorozhny,M. Incurvati,A. Sadovski,V. Shigaev

The NUCLEON space experiment has been proposed to perform direct measurements of CR energy spectrum and composition up to E ∼ 10 eV. The NUCLEON detector consists of layers of different detectors: scintillator detectors with WLS fibers and silicon pad and microstrip ones. The results of beam and space qualification tests of the scintillator detectors are presented.

2003International Cosmic Ray Conference

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2Performance of the Scintillator System Prototype of the NUCLEON Space Experiment

L G Tkatchev,V F Boreiko,V M Grebenyuk, I Chupin,A M Kalinin,Z Krumstein, D Podorozhny,A A Nozdrin,A Sadovski,V Shigaev

2003引用：23

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3ATLAS detector and physics performance

A Airapetian,V Dodonov,L Micu,D Axen,V B Vinogradov, D Akerman,B Szeless,P Chochula,C Geichgimbel,P Schacht,Z Tomiak,S P Baranov,

1999引用：91

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4Separation of Gluon Jets and B-Jets with a Low Pt Muon Using ATLAS Tile Calorimeter Simulation Data and Artificial Neural Networks Technique

A Astvatsaturov,J Budagov,V Shigaev,M Bosman,M Nessi,D Pantea

The possibility to enhance the capability of ATLAS Tile Calorimeter to identify low pT muons inside b-jets by the use of artificial neural networks technique is investigated in a systematic way on the basis of 2000 simulated jets.

1997Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors a...（1997）

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5ATLAS calorimeter performance Technical Design Report

A Airapetian,Fredrick I Olness,V Dodonov,L Micu,D Axen,V B Vinogradov,F Bosi,O I Dahl,Jan Godlewski,D Kisielewska,J P Richer,P Schacht,

1996引用：396

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6ATLAS Computing Technical Proposal

A. Airapetian,V. Grabsky,H. Hakopian,A. Vartapetian,F. Fares, S. Montesano,M. E. Sevior,G. N. Taylor, K. Trabelsi,D. Alexiev,I.J. Donnelly,K. E. Varvell,

1996引用：7

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7In Atlas Hadron Calorimeter Using Artificial Neural Networks Technique

J. Budagov,I. Chirikov-Zorin, D. Pantea, A. Paplevka,V. Shigaev, S. Sushkov

Advantages of artiicial neural networks techniques in handling data from highly granulated ATLAS hadron calorimeter are shown in application to isolated == separation task in the range 3 < p T < 5 GeV at pseudorapidity = 0:3. Such low p T muons have a signiicant probability to be absorbed in the calorimeter and therefore they cannot be reliably registered by the muon detector. JETNET program was used to investigate the performance of neural network classiiers in solving low p T == separation task. Data sets for training and analysis were obtained with ATLAS simulation programs.

1995

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8Program for the SHP Processor Data Filtration in the HPD Minimum Guidance System