| |
 |
| Paper: |
A New Deep Learning Model for Gamma-Ray Bursts’ Light Curves Simulation |
| Volume: |
541, ADASS XXXIII |
| Page: |
230 |
| Authors: |
R. Falco; N. Parmiggiani; A. Bulgarelli; G. Panebianco; L. Castaldini; A. Di Piano; V. Fioretti; M. Lombardi; C. Pittori; M. Tavani |
| DOI: |
10.26624/BXNK3217 |
| Abstract: |
The development of reliable detection algorithms for Gamma-Ray Bursts
(GRBs) constitutes a complex undertaking. To train or test these algorithms, it is necessary to have a large GRB dataset, but usually, there are not enough real data available.
This represents a problem for high-energy astrophysics projects such as AGILE, COSI
and CTA. This work aims to develop a Deep Learning-based model for generating synthetic GRBs that closely replicate the distribution of real GRBs. Results show that the
simulated GRBs are compatible with the real ones, with similar count rate distribution. |
|
|
|
|
|
