THE MAIN INJECTOR FACILITY: A NEW TOOL FOR HIGH ENERGY PHYSICS AT FERMILAB

2001 ◽  
Vol 16 (supp01c) ◽  
pp. 1187-1189 ◽  
Author(s):  
C. M. BHAT
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Accelerator Complex ◽  
Fixed Target ◽  
The Future ◽  
Proton Injector ◽  
Energy Physics

The Main Injector(MI) and the Recycler Ring (RR) are newly built synchrotrons in the Fermilab Accelerator complex. Many new features have been incorporated in their design and are tested. MI has already served as a 150 GeV proton injector to the Tevatron during the final stages of 1998-1999 800 GeV fixed target HEP experiments. Presently, MI is in use for collider Run-II commissioning. The MI and the RR will play major roles in the future HEP programs at Fermilab.

scholarly journals Interactions of Beams with Surroundings

2020 ◽  
pp. 183-203
Author(s):  
M. Brugger ◽  
H. Burkhardt ◽  
B. Goddard ◽  
F. Cerutti ◽  
R. G. Alia
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Fixed Target ◽  
Fixed Target Experiments ◽  
Secondary Particles ◽  
Radiation Sources ◽  
Residual Gas ◽  
The Impact ◽  
High Energy Particles ◽  
Energy Physics

AbstractWith the exceptions of Synchrotron Radiation sources, beams of accelerated particles are generally designed to interact either with one another (in the case of colliders) or with a specific target (for the operation of Fixed Target experiments, the production of secondary beams and for medical applications). However, in addition to the desired interactions there are unwanted interactions of the high energy particles which can produce undesirable side effects. These interactions can arise from the unavoidable presence of residual gas in the accelerator vacuum chamber, or from the impact of particles lost from the beam on aperture limits around the accelerator, as well as the final beam dump. The wanted collisions of the beams in a collider to produce potentially interesting High Energy Physics events also reduces the density of the circulating beam and can produce high fluxes of secondary particles.

scholarly journals Influence of Backside Energy Leakages from Hadronic Calorimeters on Fluctuation Measures in Relativistic Heavy-Ion Collisions

Universe ◽  
2019 ◽  
Vol 5 (5) ◽  
pp. 126
Author(s):  
Andrey Seryakov
Keyword(s):  
High Energy Physics ◽  
System Size ◽  
Heavy Ion ◽  
High Energy ◽  
Relativistic Heavy Ion Collisions ◽  
Research Subject ◽  
Fixed Target ◽  
Main Research ◽  
Ion Collisions ◽  
Energy Physics

The phase diagram of the strongly interacting matter is the main research subject for different current and future experiments in high-energy physics. System size and energy scan programs aim to find a possible critical point. One of such programs was accomplished by the fixed-target NA61/SHINE experiment in 2018. It includes six beam energies and six colliding systems: p + p, Be + Be, Ar + Sc, Xe + La, Pb + Pb and p + Pb. In this study, we discuss how the efficiency of centrality selection by forward spectators influences multiplicity and fluctuation measures and how this influence depends on the size of colliding systems. We use SHIELD and EPOS Monte-Carlo (MC) generators along with the wounded nucleon model, introduce a probability to lose a forward spectator and spectator energy loss. We show that for light colliding systems such as Be or Li even a small inefficiency in centrality selection has a dramatic impact on multiplicity scaled variance. Conversely, heavy systems such as Ar + Sc are much less prone to the effect.

High Energy Physics Looks to the Future

Science ◽  
1983 ◽  
Vol 220 (4599) ◽  
pp. 809-811 ◽  
Author(s):  
M. M. WALDROP
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
The Future ◽  
Energy Physics

Neural network trigger algorithms for heavy quark event selection in a fixed target high energy physics experiment

1992 ◽  
Vol 25 (4) ◽  
pp. 413-421 ◽  
Author(s):  
Lalit Gupta ◽  
Anand M. Upadhye ◽  
Bruce Denby ◽  
Salvator R. Amendolia ◽  
Giovanni Grieco
Keyword(s):  
Neural Network ◽  
Heavy Quark ◽  
High Energy Physics ◽  
High Energy ◽  
Fixed Target ◽  
Event Selection ◽  
Physics Experiment ◽  
Energy Physics

scholarly journals Quantum machine learning and its supremacy in high energy physics

2020 ◽  
pp. 2030024
Author(s):  
Kapil K. Sharma
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Particle Physics ◽  
High Energy Physics ◽  
Recognition Task ◽  
High Energy ◽  
Particle Identification ◽  
Quantum Machine Learning ◽  
The Future ◽  
Energy Physics

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.

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