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| Paper: |
Preliminary Results of a New Deep Learning Method to Detect and Localize GRBs in the AGILE/GRID Sky Maps |
| Volume: |
538, ADASS XXXII |
| Page: |
309 |
| Authors: |
N. Parmiggiani; A. Bulgarelli; A. Macaluso; V. Fioretti; A. Di Piano; L. Baroncelli; A. Addis; M. Landoni; C. Pittori; F. Verrecchia; F. Lucarelli; A. Giuliani; F. Longo; D. Beneventano; M. Tavani |
| DOI: |
10.26624/TSLA2354 |
| Abstract: |
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 (https://www.docker.com/)
container with all the required software for data simulation and deployed it using the
Amazon Web Service (https://aws.amazon.com/) to calculate the p-value distribution
under different conditions. With the p-value distribution, we can calculate the statistical
significance of a detection. |
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