Evaluating summarization and inference techniques for high energy physics applications
Name
1203061824-MIT.pdf
Size
7.9 MB
Format
Adobe PDF
Checksum (MD5)
a872c2e49319213155bd1eade05a839b
Author(s)
Diehl, Hannah R.
Advisor(s)
Michael Williams.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
With continuing developments in experimental high energy physics, more and more data is being produced for analysis. As the size of data sets grows, the runtime and computational requirements of traditional inference procedures can become intractable. The problem of scalable inference appears in many fields, and thus it is an area of continuous development in computer science. With the proliferation of improved methods for data summarization and inference, an increasingly large onus is placed on individual researchers to determine the most appropriate methods for their specific problems. This work outlines the fundamentals of inference in high energy physics to establish a common foundation for readers in physics and computer scientist. It continues on to present a new set of tools that is designed to be used by researchers to evaluate summarization and inference methods for use on customized problems. The work presents sample evaluation results that can be produced by this tool. Finally, the work outlines how the tool can be used by researchers and highlights potential directions of interest in the search for more efficient inference techniques to be used in the field of high energy physics.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 87-89).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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