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

Context. 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. Aims. We propose an implementation of the <ns0:monospace>ULISSE</ns0:monospace> algorithm to determine the physical parameters of galaxies, in particular, the star formation rates (SFR) and stellar masses (ℳ*), based on composite-colour images alone. Methods. <ns0:monospace>ULISSE</ns0:monospace> 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 <ns0:monospace>ImageNet</ns0:monospace> dataset to perform similarity searches among all objects in the sample. This eliminates the need for extensive neural-network training. Results. Our experiments were performed on the Sloan Digital Sky Survey. They demonstrate that we are able to predict the joint SFR and ℳ* of the target galaxies within 1 dex in 60% to 80% of cases, depending on the investigated subsample (quiescent and starforming 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 <ns0:monospace>ULISSE</ns0:monospace> is more effective for galaxies with an active star formation than for elliptical galaxies with quenched star formation. Additionally, <ns0:monospace>ULISSE</ns0:monospace> performs more efficiently for galaxies with bright nuclei such as active galactic nuclei. Conclusions. Our results suggest that <ns0:monospace>ULISSE</ns0:monospace> is a promising tool for a preliminary estimation of SFR and 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.