ULISSE: Determination of the star formation rate and stellar mass based on the one-shot galaxy imaging technique

Modern sky surveys produce vast amounts of observational data, which makes the application of classical methods for estimating galaxy properties challenging and time-consuming. This challenge can be significantly alleviated by employing automatic machine- and deep-learning techniques. We propose an implementation of the algorithm to determine the physical parameters of galaxies, in particular, the star formation rates (SFR) and stellar masses (mathcal M _*), based on composite-colour images alone. is able to rapidly and efficiently identify candidates from a single image based on photometric and morphological similarities to a given reference object with known properties. This approach leverages features extracted from the ImageNet dataset to perform similarity searches among all objects in the sample. This eliminates the need for extensive neural-network training. Our experiments were performed on the Sloan Digital Sky Survey. They demonstrate that we are able to predict the joint SFR and mathcal M of the target galaxies within 1,dex in 60 to 80 of cases, depending on the investigated subsample (quiescent and star-forming galaxies, early- and late-type, etc.), and within 0.5,dex when we consider these parameters separately. This is approximately twice the fraction obtained from a random guess extracted from the parent population. Additionally, we found that is more effective for galaxies with an active star formation than for elliptical galaxies with quenched star formation. Additionally performs more efficiently for galaxies with bright nuclei such as active galactic nuclei. Our results suggest that is a promising tool for a preliminary estimation of SFR and mathcal M for galaxies based only on single images in current and future wide-field surveys (e.g. Euclid and LSST), which target millions of sources nightly.