scholarly journals Efficiency Parameterization with Neural Networks

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Francesco Armando Di Bello ◽  
Jonathan Shlomi ◽  
Chiara Badiali ◽  
Guglielmo Frattari ◽  
Eilam Gross ◽  
...  
Keyword(s):  
Neural Network ◽  
High Energy Physics ◽  
High Energy ◽  
Heavy Flavor ◽  
Neural Network Approach ◽  
Large Samples ◽  
Local Densities ◽  
Flavor Tagging ◽  
Physics Experiments ◽  
Energy Physics

AbstractMultidimensional efficiency maps are commonly used in high-energy physics experiments to mitigate the limitations in the generation of large samples of simulated events. Binned efficiency maps are however strongly limited by statistics. We propose a neural network approach to learn ratios of local densities to estimate in an optimal fashion efficiencies as a function of a set of parameters. Graph neural network techniques are used to account for the high dimensional correlations between different physics objects in the event. We show in a specific toy model how this method is applicable to produce accurate multidimensional efficiency maps for heavy-flavor tagging classifiers in HEP experiments, including for processes on which it was not trained.

scholarly journals Particle identification for Higgs physics at future electron–positron collider

2020 ◽  
Vol 35 (15n16) ◽  
pp. 2041013 ◽  
Author(s):  
Wei-Ming Yao
Keyword(s):  
Higgs Physics ◽  
Yukawa Coupling ◽  
High Energy Physics ◽  
Flavor Physics ◽  
High Energy ◽  
Particle Identification ◽  
Heavy Flavor ◽  
Electron Positron ◽  
Physics Experiments ◽  
Energy Physics

Particle identification (PID) plays a key role in heavy-flavor physics in high-energy physics experiments. However, its impact on Higgs physics is still not clear. In this note, we will explore some of the potential of PID to improve the identification of heavy-flavor jets by using identified charged kaons in addition to the traditional vertexing information. This could result in a better measurement of the Higgs-charm Yukawa coupling at the future [Formula: see text] colliders.

NEURAL NETWORK DISCRIMINATION OF HEAVY FLAVOR JETS: A SURVEY

1992 ◽  
Vol 03 (supp01) ◽  
pp. 243-254 ◽  
Author(s):  
P. Mazzanti ◽  
R. Odorico
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Heavy Flavor ◽  
Comparative Performance ◽  
Statistical Classifiers ◽  
Short Survey ◽  
Energy Physics

A short survey of the use of neural networks and statistical discriminants in high energy physics for recognition of heavy flavor jets is presented. After illustrating the various neural and statistical classifiers currently used, some assessment of their comparative performance for top and bottom jets is made.

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 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

Status of Cerium Fluoride Performances

1994 ◽  
Vol 348 ◽  
Author(s):  
E. Auffray ◽  
I. Dafinei ◽  
P. Lecoq ◽  
M. Schneegans
Keyword(s):  
Decay Time ◽  
High Energy Physics ◽  
Light Yield ◽  
High Energy ◽  
Radiation Hardness ◽  
Cerium Fluoride ◽  
The World ◽  
Electromagnetic Calorimeters ◽  
Physics Experiments ◽  
Energy Physics

ABSTRACTCerium fluoride offers a reasonable compromise between parameters like the density, the light yield, the scintillation characteristics (particularly the decay time) and the radiation hardness, and is considered today as the best candidate for large electromagnetic calorimeters in future High Energy Physics experiments. Details on the performances of large crystals produced by different manufacturers all over the world and measured by the Crystal Clear collaboration will be shown and the usefulness of a good collaboration between the industry and the users will be highlighted by some examples on the light yield and radiation hardness improvement.

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