Speaker
Description
The Galactic Plane Survey (GPS) as proposed by CTAO is one of the key science projects that will cover an energy range from ~30 GeV to ~100 TeV with unprecedented sensitivity leading to an increase in the known gamma-ray source population by a factor of five.
Here we tested our deep-learning-based automatic source detection techniques and compared them with traditional likelihood detection methods. For the simulation, we considered the inner-galactic region ∣b∣≤6, different energy bins with relevant IRFs as implemented in Gammapy (open-source software developed by CTAO) and all point-like sources with the same extensions. Our automatic source detection and localization pipeline (ASID) based on U-Shaped network (U-Net) and Laplacian of Gaussian (LoG) has also been tested for Fermi-LAT data and optical data (from MeerLICHT telescope).
We show that with our pipeline and Log-scaled counts map, we could achieve 2x sensitivity than expected from GPS using likelihood methods, especially for identifying fainter sources. We also show the potential to detect DM subhalos (as point sources) using our method and we could achieve a lower limit on the self-annihilation thermal cross-section $\langle \sigma v \rangle$ = $2.4 \times 10^{-23}$ cm$^3$s$^{-1}$.
We are also currently exploring some diffusion-based methods to remove background from the generated counts map to further increase the detection and localization efficiency.
