How should we look for extragalactic globular clusters with Rubin/LSST?
The amount of high-quality data made available from large sky surveys is ever-increasing. LSST is no exception. Naturally, looking for compact objects such as extragalactic globular clusters (GCs) can become extremely difficult. Even with multi-band optical photometry, GCs in distant galaxies are easily confusable with background galaxies and foreground stars. Considering the expected data from Rubin, exactly how well could we distinguish unresolved GCs from contaminants? Besides that, once we understand the limitations inherent to the data, what is the best strategy to look for extragalactic star clusters with Rubin? The SMWLV star clusters group has teamed up to answer these questions. Schweder-Souza et al. 2026 (to be published) study the expected contamination and completeness rates in samples of extragalactic GC candidates using LSST-like data of the Fornax Cluster. The colors provided by the Rubin filterset may allow us to achieve contamination rates as low as ~ 40%. However, and perhaps most importantly, sophisticated machine learning methods cannot lower this rate. Therefore, regarding the identification of extragalactic GCs, we highlight the importance of augmenting Rubin/LSST data with ancillary information. Most importantly, NIR data from surveys such as VISTA (already to be cross-matched with LSST as part of the UK in-kind contribution) and new space facilities, e.g., Roman and Euclid. With even richer datasets in terms of spectral coverage, astronomers will be able to accurately map extragalactic GC systems across immense areas of the sky, reaching high completeness rates for unprecedentedly distant galaxies and vast areas of the sky. This way, it will be possible to fully explore the scientific potential of Rubin/LSST data for extragalactic star clusters studies.