SMILE: Discriminating milli-lens systems in a VLBI pilot project

Context. Dark matter (DM) remains poorly probed on critical sub-galactic scales, where predictions from different models diverge in terms of abundance and density profiles of halos. Gravitational lens systems on milli-arcsecond scales (milli-lenses) are expected for a population of dense DM halos (free-floating or sub-halos) and free-floating supermassive black holes (SMBHs) in the mass range of 106 to 109 M⊙ that might partly be comprised of primordial black holes (PBHs). Aims. In this paper, we aim to look for possible milli-lens systems via a systematic search in a large sample of radio-loud active galactic nuclei (AGN) observed with very long baseline interferometry (VLBI). We present the observational strategy to discriminate milli-lens systems from contaminant objects mimicking a milli-lens morphology. Methods. In a pilot project, we have investigated VLBI images from 13 828 sources from the Astrogeo VLBI image database and reduced the number of lens candidates to 40 in a first step. We present here the images and analysis of new sensitive follow-up observations with the European VLBI network at 5 and 22 GHz and streamline our analysis to reject milli-lens candidates. By using constraints such as the surface brightness ratio, conservation of spectral shape, stability of flux ratios over time, and changes in morphology at higher frequencies, we can confidently discriminate between milli-lenses and contaminant objects that mimick them. Results. Using the above constraints, we ruled out 31 of our initial 40 candidates of milli-lens systems, demonstrating the power of our approach. In addition, we found many new candidate compact symmetric objects (CSOs), which are thought to be primarily short-lived jetted radio sources. Conclusions. Additional observations of the remaining candidates will be necessary to confirm or reject their nature as milli-lenses or CSOs. This study serves as a pathfinder for the final sample used for the Search for MIlli-LEnses (SMILE) project, which will allow DM models to be constrained by comparing the results to theoretical predictions. This SMILE sample will consist of ∼5000 sources based on the VLA CLASS survey and will include many observations obtained for this project specifically.