Bob Sturm was awarded the "People's Choice Award" at the Spring 2022 Data Science Poster Fair
Data Science master's student Bob Sturm was awarded the "People's Choice Award" at the Spring 2022 Data Science Poster Fair. He was honored for his project, "Towards Efficient IACT Calibration with Deep learning-based Muon Identification".
The Data Science graduate program hosts an annual poster fair each spring. As a part of their degree requirements students present the research they have conducted over the span of one or multiple semesters under the guidance of their faculty advisor. The event is open to the public, and each poster and presentation was judged by a variety of students, faculty, and industry members.
Congratulations to Bob for receiving this year's award!
Student: Bob Sturm
Advisor: Lucy Fortson
Capstone: Towards Efficient IACT Calibration with Deep learning-based Muon Identification
Capstone: Towards Efficient IACT Calibration with Deep learning-based Muon Identification
Abstract: Extensive air showers are initiated when very energetic particles, including very-high-energy gamma rays, enter the earth’s atmosphere. Imaging Atmospheric Cherenkov Telescopes (IACTs) are used to measure the Cherenkov light from charged particles emitted during air showers. Muons are secondary particles emitted during these air showers and IACTs are calibrated by comparing the Cherenkov light signal in IACT images of muons to known theoretically expected values. The main goal of this project is to improve the efficiency of IACT calibration. With this aim, we a) Implemented a Convolutional Neural Network (CNN) proposed by Flanagan and trained it on a sample of ~600,000 IACT images, b) Explored ways to simplify the model’s complexity and computational performance without loss of prediction performance, c) Developed a muon fitting algorithm that determines a muon ring’s centroid and radius, and d) Analyzed the relationship between muon ring radii and the total electromagnetic signal in images.