Speaker
Fatemeh Akhondi
Description
Background noise elimination is a pivotal concern in data analysis and signal detection. Here, we introduce a novel denoising technique tailored specifically for gamma-ray data collected by the Fermi-LAT telescope. Our study highlights the transformative impact of integrating this method before applying clustering algorithms such as DBSCAN and MST. This integration significantly enhances the accuracy and efficiency of detecting gamma-ray sources. Through the examination of both simulated and authentic 15-year Fermi-LAT data, we emphasize the method's ability to unveil previously unnoticed sources and improve the characterization of established ones.
Primary author
Co-authors
Mr
Mehran Soor
Dr
Hadi Hedayati Kh
