Application of Bayesian Neural Networks in High Energy Physics Experiments

2009 ◽  
Author(s):  
Ye Xu ◽  
WeiWei Xu ◽  
YiXiong Meng ◽  
KaiEn Zhu
Keyword(s):  
Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Bayesian Neural Networks ◽  
Physics Experiments ◽  
Energy Physics

Fast triggering in high-energy physics experiments using hardware neural networks

2003 ◽  
Vol 14 (5) ◽  
pp. 1010-1027 ◽  
Author(s):  
B. Denby ◽  
P. Garda ◽  
B. Granado ◽  
C. Kiesling ◽  
J.-C. Prevotet ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Physics Experiments ◽  
Energy Physics

Development of Silicon Sensor Characterization System for Future High Energy Physics Experiments

2015 ◽  
Vol 3 (1) ◽  
pp. 41-45
Author(s):  
Preeti Kumari ◽  
◽  
Kavita Lalwani ◽  
Ranjit Dalal ◽  
Ashutosh Bhardwaj ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Physics Experiments ◽  
Silicon Sensor ◽  
Energy Physics

scholarly journals Performance of an Operating High Energy Physics Data Grid: DØSAR-Grid

2005 ◽  
Vol 20 (16) ◽  
pp. 3874-3876 ◽  
Author(s):  
B. Abbott ◽  
P. Baringer ◽  
T. Bolton ◽  
Z. Greenwood ◽  
E. Gregores ◽  
...  
Keyword(s):  
Geographical Distribution ◽  
High Energy Physics ◽  
High Energy ◽  
End Users ◽  
Southern Analysis ◽  
Data Analyses ◽  
Use Of Technology ◽  
Computing Grid ◽  
Physics Experiments ◽  
Energy Physics

The DØ experiment at Fermilab's Tevatron will record several petabytes of data over the next five years in pursuing the goals of understanding nature and searching for the origin of mass. Computing resources required to analyze these data far exceed capabilities of any one institution. Moreover, the widely scattered geographical distribution of DØ collaborators poses further serious difficulties for optimal use of human and computing resources. These difficulties will exacerbate in future high energy physics experiments, like the LHC. The computing grid has long been recognized as a solution to these problems. This technology is being made a more immediate reality to end users in DØ by developing a grid in the DØ Southern Analysis Region (DØSAR), DØSAR-Grid, using all available resources within it and a home-grown local task manager, McFarm. We will present the architecture in which the DØSAR-Grid is implemented, the use of technology and the functionality of the grid, and the experience from operating the grid in simulation, reprocessing and data analyses for a currently running HEP experiment.

scholarly journals Jet substructure classification in high-energy physics with deep neural networks

2016 ◽  
Vol 93 (9) ◽  
Author(s):  
Pierre Baldi ◽  
Kevin Bauer ◽  
Clara Eng ◽  
Peter Sadowski ◽  
Daniel Whiteson
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Jet Substructure ◽  
Energy Physics

scholarly journals Noise propagation effects in power supply distribution systems for high-energy physics experiments

2017 ◽  
Vol 12 (12) ◽  
pp. P12004-P12004 ◽  
Author(s):  
F. Arteche ◽  
C. Rivetta ◽  
M. Iglesias ◽  
I. Echeverria ◽  
A. Pradas ◽  
...  
Keyword(s):  
Power Supply ◽  
Distribution Systems ◽  
High Energy Physics ◽  
High Energy ◽  
Noise Propagation ◽  
Propagation Effects ◽  
Physics Experiments ◽  
Energy Physics
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