Presenter(s): Jacob Bieker – Physics, Computer and Information Science
Faculty Mentor(s): Tim Cohen
Poster 37
Oral Session 2S
Research Area: Physical Science
Funding: DAAD Research Internships in Science and Engineering (RISE)
Finding gamma-ray sources is of paramount importance for Imaging Air Cherenkov Telescopes (IACT). This study looks at using deep neural networks on data from the First G-APD Cherenkov Telescope (FACT) as a proof-of-concept of finding gamma-ray sources with deep learning for the upcoming Cherenkov Telescope Array (CTA). In this study, FACT’s individual photon level observation data from the last 5 years was used with convolutional neural networks to determine if one or more sources were present. The output from the neural networks were compared using the default method of finding sources as a baseline. The neural networks used various architectures to determine which architectures were most successful in finding sources. Neural networks offer a promising method for finding gamma-ray sources for IACTs. With further improvement and modifications, they offer a compelling method for source detection for the next generation of IACTs.