SIMBAD references

The microlensing signal in the light curves of gravitationally lensed quasars can shed light on the dark matter (DM) composition in their lensing galaxies. Here, we investigate a sample of six lensed quasars from the most recent and best COSMOGRAIL observations: HE 1104-1805, HE 0435-1223, RX J1131-1231, WFI 2033-4723, PG 1115+080, and J1206+4332, yielding a total of eight microlensing light curves, when combining independent image pairs and typically spanning ten years. We explore the microlensing signals to determine whether the standard assumptions on the stellar populations are sufficient to account for the amplitudes of the measured signals or whether additional microlenses are needed. We use the most detailed lens models to date from the H0LiCOW/TDCOSMO collaboration to derive the microlensing parameters, such as the convergence, shear, and stellar/dark matter mass fraction at the position of the quasar images. We use these parameters to generate simulated microlensing light curves. Finally, we propose a methodology based on the Kolmogorov-Smirnov test to verify whether the observed microlensing amplitudes in our data are compatible with the most standard scenario, whereby galaxies are composed of stars as compact bodies and smoothly distributed DM. Given our current sample, we show that the standard scenario cannot be rejected, in contrast with previous results by Hawkins (2020a, A&A, 633, A107), claiming that a population of stellar mass primordial black holes (PBHs) is necessary to explain the observed amplitude of the microlensing signal in lensed quasar light curves. We further estimate the number of microlensing light curves needed to effectively distinguish between the standard scenario with stellar microlensing and a scenario that describes that all the DM contained in galaxies is in the form of compact objects such as PBHs, with a mean mass of 0.2 M_⊙. We find that about 900 microlensing curves from the Rubin Observatory will be sufficient to discriminate between the two extreme scenarios at a 95% confidence level.