Undergraduate Honors Thesis

Abstract

• The Standard Model has made incredible predictions in particle physics. The Large Hadron Collider (LHC) is colliding protons to probe new physics beyond the standard model. These collisions occur through bunches of protons that cross inside detectors at a rate of 40 MHz. Multiple pairs of protons collide in each bunch, which is called pileup. As protons collide, many particles are produced, leaving behind tracks as they move through the detector. After a series of upgrades occurring in the next six years, the pileup is expected to increase from 50 to 200. The track reconstruction algorithm increases non-linearly in complexity with pileup. With larger pileup, the number of fake tracks (tracks not associated with any particle) increases. My research aims to use Machine Learning to create a model capable of differentiating real vs. fake tracks after they come out of the algorithm.

Creator

• Alterkait, Omar A

Date Awarded

• 2021-04-06

Academic Affiliation

• Physics

Advisor

• Stenson, Kevin M

Committee Member

• Cumalat, John P
• Zaharatos, Brian R

Granting Institution

• University of Colorado Boulder

Subject

• Track Reconstruction
• High Energy Physics
• Machine Learning
• CMS Detector

Last Modified

• 2021-05-13

Resource Type

• Undergraduate Honors Thesis

Rights Statement

• In Copyright

Language

• English [eng]

Relationships

In Collection:

• Arts and Sciences Honors Program

Items

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	Alterkait_Omar_Defense_Copy.pdf	2021-04-13	Public	Download