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.
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.