2023/02/21 by Parmiggiani, N., Bulgarelli, A., Macaluso, A. +12
#FOS: Physical sciences #High Energy Astrophysical Phenomena (astro-ph.HE) #Instrumentation and Methods for Astrophysics (astro-ph.IM)
paper · doi:10.48550/arxiv.2302.10557
AGILE is an ASI space mission launched in 2007 to study X-ray and gamma-ray phenomena in the energy range from ∼20 keV to ∼10 GeV. The AGILE Team developed a real-time analysis pipeline for the fast detection of transient sources, and the follow-up of external science alerts received through networks such as the General Coordinates Network. We developed a new Deep Learning method for detecting and localizing Gamma-Ray Bursts (GRB) in the AGILE/GRID sky maps. We trained the model using sky maps with GRBs simulated in a radius of 20 degrees from the center of the map, which is larger than 99.5 % of the error region present in the GRBWeb catalog. We also plan to apply this method to search for counterparts of gravitational wave events, which typically have a wider localization error region. The method comprises two Deep Learning models implemented with two Convolutional Neural Networks. The first model detects and filters sky maps containing a GRB, while the second model localizes its position. We trained and tested the models using simulated data. The detection model achieves an accuracy of 95.7 %, and the localization model has a mean error lower than 0.8 degrees. We configured a Docker container with all the required software for data simulation and deployed it using the Amazon Web Service to calculate the p-value distribution under different conditions. With the p-value distribution, we can calculate the statistical significance of a detection.