Bin Optimization for Compositeness Limit Generation

Session Number

Project ID: CMPS 8

Advisor(s)

Dr. Peter Dong; llinois Mathematics and Science Academy

Discipline

Computer Science

Start Date

22-4-2020 9:10 AM

End Date

22-4-2020 9:25 AM

Abstract

The study of compositeness theory in particle physics postulates the hypothetical existence of preons, subatomic particles that constitute quarks and leptons. Searches for compositeness collect observed data from particle colliders and compare them to generated data from simulations to create histograms of dilepton invariant mass, evident from subatomic interactions. Data sets that are randomly generated using Monte Carlo simulations are called “toys” and are used as a tool to vary the histogram statistics of the generated data in a repeated, randomized process to determine a theoretical 95% confidence limit for the lambda energy scale at which Compositeness interactions are likely to be seen. We optimized the number and width of the bins that our statistical framework uses to make a faster, more efficient program for data analysis.

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Apr 22nd, 9:10 AM Apr 22nd, 9:25 AM

Bin Optimization for Compositeness Limit Generation

The study of compositeness theory in particle physics postulates the hypothetical existence of preons, subatomic particles that constitute quarks and leptons. Searches for compositeness collect observed data from particle colliders and compare them to generated data from simulations to create histograms of dilepton invariant mass, evident from subatomic interactions. Data sets that are randomly generated using Monte Carlo simulations are called “toys” and are used as a tool to vary the histogram statistics of the generated data in a repeated, randomized process to determine a theoretical 95% confidence limit for the lambda energy scale at which Compositeness interactions are likely to be seen. We optimized the number and width of the bins that our statistical framework uses to make a faster, more efficient program for data analysis.