  Disentangling the stellar population in the central galaxy from the intrahalo
light can help us shed light on the formation history of the host halo, as the
properties of the stellar components are expected to retain traces of its
formation history. Many approaches are adopted, depending on different physical
assumptions (e.g. the light profile, chemical composition, and kinematical
differences) and on whether the full six-dimensional phase-space information is
known (much like in simulations) or whether one analyses projected quantities
(i.e. observations). This paper paves the way for a new approach to bridge the
gap between observational and simulation methods. We propose the use of
projected kinematical information from stars in simulations in combination with
deep learning to create a robust method for identifying intrahalo light in
observational data to enhance understanding and consistency in studying the
process of galaxy formation. Using a U-Net, we developed a methodology for
predicting these contributions from a sample of mock images from hydrodynamical
simulations to train, validate and test the network. Reinforced training
(Attention U-Net) was used to improve the first results, as the innermost
central regions of the mock images consistently overestimate the stellar
intrahalo contribution. Our work shows that adequate training over a
representative sample of mock images can lead to good predictions of the
intrahalo light distribution. The model is mildly dependent on the training
size and its predictions are less accurate when applied to mock images from
different simulations. However, the main features (spatial scales and gradients
of the stellar fractions) are recovered for all tests. While the method
presented here should be considered as a proof of concept, future work is
required to enable the application of the proposed model to observational data.
