Author ORCID Identifier

Date Available


Year of Publication


Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation


Arts and Sciences


Physics and Astronomy

First Advisor

Dr. Tim Gorringe


The goal of the new Muon g-2 E989 experiment at Fermi National Accelerator Laboratory (FNAL) is a precise measurement of the muon anomalous magnetic moment, aμ ≡ (g-2)/2. The previous BNL experiment measured the anomaly aμ(BNL) with an uncertainty of 0.54 parts per million (ppm). The discrepancy between the current standard model calculation of the aμ(SM) and the previous measurement aμ(BNL) is over 3σ. The FNAL Muon g-2 experiment aims at increasing the precision to 140 parts per billion (ppb) to resolve the discrepancy between the theoretical calculation and the experiment result.

The anomaly, aμ is determined experimentally by measuring two frequencies. The magnetic field of the storage ring is measured with NMR probes and given in terms of equivalent proton spin precession frequency ωp in a spherical water sample at 34.7 °C. The difference frequency ωa between the muon spin-precession frequency and the cyclotron frequency in the storage ring magnetic field is encoded in the energy of the positrons from the muon decay and is measured with 24 electromagnetic calorimeters. By calculating the ratio ωa/ωp and combining with known constants, we can extract the anomaly aμ.

This dissertation describes my contribution to the experiment, focusing on the extraction of the frequency ωa. My work can be classified into three categories: 1. Fast Data Acquisition (DAQ) system development, 2. A frequency-domain filtering approach to the analysis of the energy-integrated ωa data, 3. A GPU-based Monte Carlo of the frequency-domain filtering approach. The GPS timestamps readout, the DAQ health monitor and GPS data quality monitor page are presented in the Chapter 3. The FFT-based digital filtering analysis is presented in the Chapter 4. The GPU-based Monte Carlo simulation is presented in Chapter 5. The analysis work in the dissertation is based on the Run-1 data which is collected from March 2018 to July 2018.

Digital Object Identifier (DOI)

Funding Information

This study was supported by the National Science Foundation Award (no.: 1807266) in 2018 and National Science Foundation Award (no.:1503552).