Euclid Quick Data Release (Q1). First Euclid statistical study of the active galactic nuclei contribution fraction

Understand

Active galactic nuclei (AGN) play a key role in galaxy evolution but are challenging to identify due to their varied observational signatures.

• Furthermore, understanding their impact requires quantifying their strength relative to their host galaxies.
• We developed a deep learning (DL) model for identifying AGN in imaging data by deriving the contribution of the central point source.
• Trained on Euclidised mock galaxy images with injected AGN levels, in the form of varying contributions of the point-spread function (PSF), our model can precisely and accurately recover the injected AGN contribution fraction $f_{\rm PSF}$, with a mean difference between the predicted and true $f_{\rm PSF}$ of $-0.0078$ and an overall root mean square error (RMSE) of 0.051.