Calculating Multichannel Bayesian Limits Using a Markov Chain Monte Carlo Calculator
Advisor(s)
Dr. Peter Dong, Illinois Mathematics and Science Academy
Dr. Leonard Spiegel, Fermilab
Location
Room A147
Start Date
26-4-2019 11:25 AM
End Date
26-4-2019 11:40 AM
Abstract
Our group's goal is to find evidence of quark-lepton compositeness by analyzing contact interactions that would indicate the presence of preons, theoretical constituents of quarks and leptons. We focus specifically on the Bayesian statistical analysis that determines the lower limit for the energy scale at which such contact interactions would occur. We calculate limits using a Bayesian Markov chain Monte Carlo calculator which utilizes RooStats, a statistical analysis program, to find the 95% confidence interval for a given parameter of interest. We show that we can find simple single-bin limits, then include background processes and systematic uncertainties into the limit calculation before generating multi-channel limits. We are creating a program that computes the Bayesian limit of multiple channels with correlated systematic uncertainties. We will show our promising results and outline the issues that remain to be solved.
Calculating Multichannel Bayesian Limits Using a Markov Chain Monte Carlo Calculator
Room A147
Our group's goal is to find evidence of quark-lepton compositeness by analyzing contact interactions that would indicate the presence of preons, theoretical constituents of quarks and leptons. We focus specifically on the Bayesian statistical analysis that determines the lower limit for the energy scale at which such contact interactions would occur. We calculate limits using a Bayesian Markov chain Monte Carlo calculator which utilizes RooStats, a statistical analysis program, to find the 95% confidence interval for a given parameter of interest. We show that we can find simple single-bin limits, then include background processes and systematic uncertainties into the limit calculation before generating multi-channel limits. We are creating a program that computes the Bayesian limit of multiple channels with correlated systematic uncertainties. We will show our promising results and outline the issues that remain to be solved.