Wenjin Yu
Pronouns: he/his/him
Research Mentor(s): Kimberlee Kearfott, Professor
Research Mentor School/College/Department: Nuclear Engineering and Radiological Sciences/Biomedical Engineering, College of Engineering
Presentation Date: Thursday, April 22, 2021
Session: Session 3 (1pm-1:50pm)
Breakout Room: Room 17
Presenter: 3
Abstract
Thermoluminescent dosimeters integrate incoming ionizing radiation dose and release photons proportional to it. When heat is applied to the material, peaks in the emitted light occur at different temperatures corresponding to characteristic quantum states. Separating these peaks may be done through fitting the data to thermoluminescence models, in a process called glow curve analysis. This analysis reveals information about signal fading and has the potential to improve dose quantitation. Dosimeter readings can have discontinuities due to experimental phenomena such as incineration of dust particles, which can result in imprecise glow curve fits. Additionally, the light emitted as a result of the readout process makes the initial fit inaccurate. As manual removal of the noise and the curve fitting process itself is time-consuming, an automated program is more efficient. This research creates software using C++ that automatically subtracts the background noise from raw data, removes any discontinuous spikes, and further smoothes the data. Peaks are identified by implementing custom functions and existing algorithms such as the Savitzky-Golay algorithm and the method of gradient descent. The final software fits the approximate curve for each peak according to first-order kinetic physics and outputs a figure of merit for the fit. An executable compatible with different computer systems allows greater portability. The software can help other researchers clean raw data and analyze peaks with efficiency and accuracy. The resulting software should result not only in greater insights into the thermoluminescent process and better radiation dosimetry, but may lend itself to applications to other fields.
Authors: Jack Yu, Jack Thiesen, Kimberlee Kearfott
Research Method: Experimental Research
