Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2306.16483 |
| Tags: |
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Inhaltsangabe:
- We propose a novel statistical method to extend Fermi-LAT catalogues of high-latitude $γ$-ray sources below their nominal threshold. To do so, we rely on a recent determination of the differential source-count distribution of sub-threshold sources via the application of deep learning methods to the $γ$-ray sky. By simulating ensembles of synthetic skies, we assess quantitatively the likelihood for pixels in the sky with relatively low-test statistics to be due to sources. Besides being useful to orient efforts towards multi-messenger and multi-wavelength identification of new $γ$-ray sources, we expect the results to be especially advantageous for statistical applications such as cross-correlation analyses.