Speaker
Description
The Cherenkov Telescope Array (CTA) is entering its production phase and the upcoming data will drastically improve the point source sensitivity compared to previous imaging atmospheric Cherenkov telescopes. The Galactic Plane Survey (GPS), proposed as one of the Key Science Projects for CTA observation will focus on the observation of the inner galactic region ($|b|<6^∘$).
Here we discuss our recent results from extending our Deep Learning (DL)-based pipeline, AutoSource-ID, (see the talk by F. Stoppa et.al, at this conference) for classifying gamma-ray sources with different extensions and predicting the fluxes with uncertainties. This pipeline was initially developed for detecting point sources at optical and gamma-ray wavelengths and tested using MeerLicht (optical) and Fermi-LAT (gamma-ray) data. For the CTA data, we are using Gammapy for simulation which already includes the updated instrument response functions. We test the classification and flux prediction capability for simulated gamma-ray sources lying in the inner galactic region (specifically $0^∘
