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Title: Quantum Sensors and Materials for High Energy Physics: LLNL Perspective

Authors:
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1418917
Report Number(s):
LLNL-PROC-744620
DOE Contract Number:
AC52-07NA27344
Resource Type:
Conference
Resource Relation:
Conference: Presented at: Workshop on Quantum Sensing, Lemont, IL, United States, Dec 12 - Dec 14, 2017
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Carosi, G. Quantum Sensors and Materials for High Energy Physics: LLNL Perspective. United States: N. p., 2018. Web.
Carosi, G. Quantum Sensors and Materials for High Energy Physics: LLNL Perspective. United States.
Carosi, G. 2018. "Quantum Sensors and Materials for High Energy Physics: LLNL Perspective". United States. doi:. https://www.osti.gov/servlets/purl/1418917.
@article{osti_1418917,
title = {Quantum Sensors and Materials for High Energy Physics: LLNL Perspective},
author = {Carosi, G},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2018,
month = 1
}

Conference:
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  • Silicon sensors, widely used in high energy and nuclear physics experiments, suffer severe radiation damage that leads to degradations in sensor performance. These degradations include significant increases in leakage current, bulk resistivity, and space charge concentration. The increase in space charge concentration is particularly damaging since it can significantly increase the sensor full depletion voltage, causing either breakdown if operated at high biases or charge collection loss if operated at lower biases than full depletion. Several strategies can be used to make Si detectors more radiation had tolerant to particle radiations. In this paper, the main radiation induced degradations inmore » Si detectors will be reviewed. The details and specifics of the new engineering strategies: material/impurity/defect engineering (MIDE); device structure engineering (DSE); and device operational mode engineering (DOME) will be given.« less
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  • Feed forward and recurrent neural networks are introduced and related to standard data analysis tools. Tips are given on applications of neural nets to various areas of high energy physics. A review of applications within high energy physics and a summary of neural net hardware status are given.