Relevant Degree Programs
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - April 2022)
A novel software platform for global optimization was developed to create a baseline design for the TRIUMF Energy Recovery Linac (ERL). The platform is parallel capable, scalable, and allows flexible combinations of various accelerator tracking tools such as Madx and Free Electron Laser (FEL) tools such as Genesis. The TRIUMF machine includes simultaneously a two-pass ERL and a rare isotope line. Many parameters are coupled, including RF and the separator system which are shared for all three linac passes. The global optimization platform can study dynamic relationships between different processes, a practice not easily performed with piecewise optimization. The FEL induced energy spread, which grows by an order of magnitude after deceleration and increases the difficulty of beam disposal, creates a tradeoff, or Pareto front, between the gain and the dump energy spread. Another front forms between energy recovery and final energy spread due to RF settings. The Pareto fronts give insights on how objectives are related and the repercussions of design decisions. Pareto relationships are presented, along with potential lattice solutions found by the optimization platform.
In experimental particle physics, researchers must often construct a mathematical model of the experiment that can be used in fits to extract parameter values. With very large data sets, the statistical precision of measurements improves, and the required level of detail of the model increases. It can be extremely difficult or impossible to write a sufficiently precise analytical model for modern particle physics experiments. To avoid this problem, we have developed a new method for estimating parameter values from experimental data, using a Maximum Likelihood fit which compares the data distribution with a “Monte Carlo Template”, rather than an analytical model. In this technique, we keep a large number of simulated events in computer memory, and for each iteration of the fit, we use the stored true event and the current guess at the parameters to re-weight the event based on the probability functions of the underlying physical models. The re-weighted Monte-Carlo (MC) events are then used to recalculate the template histogram, and the process is repeated until convergence is achieved. We use simple probability functions for the underlying physical processes, and the complicated experimental resolution is modeled by a highly detailed MC simulation, instead of trying to capture all the details in an analytical form. We derive and explain in detail the “Monte-Carlo Re-Weighting” (MCRW) fit technique, and then apply it to the problem of measuring the neutral B meson mixing frequency. In this thesis, the method is applied to simulated data, to demonstrate the technique, and to indicate the results that could be expected when this analysis is performed on real data in the future.