AGN -- host galaxy photometric decomposition using a fast, accurate and precise deep learning approach

Abstract

Identifying active galactic nuclei (AGN) is extremely important for understanding galaxy evolution and its connection with the assembly of supermassive black holes (SMBH). With the advent of deep and high angular resolution imaging surveys such as those conducted with the James Webb Space Telescope (JWST), it is now possible to identify galaxies with a central point source out to the very early Universe. In this study, we develop a fast, accurate and precise method to identify galaxies which host AGNs and recover the intrinsic AGN contribution to the observed total light ($f_{AGN}$). We trained a deep learning (DL) based method Zoobot to estimate the fractional contribution of a central point source to the total light. Our training sample comprises realistic mock JWST images of simulated galaxies from the IllustrisTNG cosmological hydrodynamical simulations. We injected different amounts of the real JWST point spread function (PSF) models to represent galaxies with different levels of $f_{AGN}$. We analyse the performance of our method and compare it with results obtained from the traditional light profile fitting tool GALFIT. We find excellent performance of our DL method in recovering the injected AGN fraction $f_{AGN}$, both in terms of precision and accuracy. The mean difference between the predicted and true injected $f_{AGN}$ is -0.002 and the overall root mean square error (RMSE) is 0.013. The relative absolute error (RAE) is 0.076 and the outlier (defined as predictions with RAE >20%) fraction is 6.5%. In comparison, using GALFIT on the same dataset, we achieve a mean difference of -0.02, RMSE of 0.12, RAE of 0.19 and outlier fraction of 19%. We applied our trained DL model to real JWST observations and found that 33% of X-ray-selected AGN and 15% of MIR-selected AGN are also identified as AGN using a cut at $f_{\rm AGN} > 0.1$.