Classification of the low-energy neutron spectra behind the shield of the accelerator of the institute of high-energy physics

1987 ◽  
Vol 62 (3) ◽  
pp. 213-217 ◽  
Author(s):  
E. A. Belogorlov ◽  
G. I. Britvich ◽  
G. I. Krupnyi ◽  
V. N. Lebedev ◽  
V. S. Lukanin ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Low Energy ◽  
Neutron Spectra ◽  
Energy Neutron ◽  
Energy Physics

scholarly journals End-to-End Event Classification of High-Energy Physics Data

2018 ◽  
Vol 1085 ◽  
pp. 042022 ◽  
Author(s):  
M Andrews ◽  
M Paulini ◽  
S Gleyzer ◽  
B Poczos
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Event Classification ◽  
End To End ◽  
Energy Physics

scholarly journals LITTLE MIRACLES OF SUPERSYMMETRIC EVOLUTION OF GAUGE COUPLINGS

1996 ◽  
Vol 11 (32) ◽  
pp. 5761-5784 ◽  
Author(s):  
M. SHIFMAN
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Low Energy ◽  
Spontaneous Breaking ◽  
Grand Unification ◽  
Exact Results ◽  
Gauge Groups ◽  
The Face ◽  
Gauge Couplings ◽  
Energy Physics

The invention of supersymmetry, almost exactly 25 years ago,1 changed the face of high energy physics. The idea that the observed low energy gauge groups appear due to the process of spontaneous breaking of a single unifying group G is also quite popular. The synthesis of these two elements results in supersymmetric grand unification. I present (perturbatively) exact results regarding the supersymmetric evolution of the gauge couplings from the scale of their unification to lower scales. In particular, it is shown how the heavy mass thresholds can be properly taken into account to all orders.

scholarly journals Use of a Low Energy Proton Accelerator for Calibrating a Large NaI(Tl) Array in a High Energy Physics Experiment

1979 ◽  
Vol 26 (1) ◽  
pp. 1535-1538 ◽  
Author(s):  
G. I. Kirkbride ◽  
J. G. O'Reilly ◽  
J. C. Tompkins ◽  
D. G. Aschman ◽  
B. L. Beron ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Low Energy ◽  
Proton Accelerator ◽  
Energy Proton ◽  
Physics Experiment ◽  
Energy Physics

scholarly journals Past, present and future low energy antiproton facilities at CERN

2014 ◽  
Vol 30 ◽  
pp. 1460261 ◽  
Author(s):  
W. Bartmann ◽  
P. Belochitskii ◽  
H. Breuker ◽  
F. Butin ◽  
C. Carli ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Low Energy ◽  
Electron Cooler ◽  
Antiproton Decelerator ◽  
Slow Extraction ◽  
Fast Extraction ◽  
Physics Experiments ◽  
Energy Physics

Low energy antiprotons are available for physics experiments at CERN since the 1980s and have been used by a large variety of experiments. The Low Energy Antiproton Ring LEAR has been constructed as a complementary use of antiprotons available at that time for high energy physics and delivered beam to experiments mainly using slow extraction. After completion of LEAR exploitation, the Antiproton Decelerator (AD) was constructed (adaptation of the existing Antiproton Collector, AC) to allow for a simpler low energy antiproton scheme (only one accelerator operated with Antiprotons) with fast extraction well suited for trap experiments. The Extra Low ENergy Antiproton ring ELENA is a small synchrotron presently constructed to further decelerate antiprotons from the AD in a controlled manner, and to reduce emittances with the help of an electron cooler to improve the capture efficiencies of existing experiments and allow for additional ones.

scholarly journals Can Low-Energy Electrons Affect High-Energy Physics Accelerators?

2004 ◽  
Vol 93 (1) ◽  
Author(s):  
R. Cimino ◽  
I. R. Collins ◽  
M. A. Furman ◽  
M. Pivi ◽  
F. Ruggiero ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Low Energy ◽  
Low Energy Electrons ◽  
Energy Physics

scholarly journals AN EMPIRICAL STUDY OF BOOSTED NEURAL NETWORK FOR PARTICLE CLASSIFICATION IN HIGH ENERGY COLLISIONS

2007 ◽  
Vol 22 (06) ◽  
pp. 1201-1211
Author(s):  
MEILING YU ◽  
LIANSHOU LIU
Keyword(s):  
Neural Network ◽  
High Energy Physics ◽  
High Energy ◽  
Two Samples ◽  
High Energy Collisions ◽  
Boosting Technique ◽  
Single Neural Network ◽  
Energy Physics ◽  
Particle Classification

The possible application of boosted neural network to particle classification in high energy physics is discussed. A two-dimensional toy model, where the boundary between signal and background is irregular but not overlapping, is constructed to show how boosting technique works with neural network. It is found that boosted neural network not only decreases the error rate of classification significantly but also increases the efficiency and signal–background ratio. Besides, boosted neural network can avoid the disadvantage aspects of single neural network design. The boosted neural network is also applied to the classification of quark- and gluon-jet samples from Monte Carlo e+e- collisions, where the two samples show significant overlapping. The performance of boosting technique for the two different boundary cases — with and without overlapping is discussed.

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