Sensitivity of strong lensing observations to dark matter substructure: a case study with Euclid

Abstract

We introduce a machine learning method for estimating the sensitivity of strong lens observations to dark matter subhaloes in the lens. Our training data include elliptical power-law lenses, Hubble Deep Field sources, external shear, and noise and PSF for the Euclid VIS instrument. We set the concentration of the subhaloes using a vmax–rmax relation. We then estimate the dark matter subhalo sensitivity in 16 000 simulated strong lens observations with depth and resolution resembling Euclid VIS images. We find that with a 3σ detection threshold, 2.35 per cent of pixels inside twice the Einstein radius are sensitive to subhaloes with a mass Mmax ≤ 1010 M☉, 0.03 per cent are sensitive to Mmax ≤ 109 M☉, and the limit of sensitivity is found to be Mmax = 108.8 ± 0.2 M☉. Using our sensitivity maps and assuming CDM, we estimate that Euclid-like lenses will yield 1.43+0.14-0.11[fsub-1] detectable subhaloes per lens in the entire sample, but this increases to 35.6+0.9-0.9[fsub-1] per lens in the most sensitive lenses. Estimates are given in units of the inverse of the substructure mass fraction fsub-1. Assuming fsub = 0.01, one in every 70 lenses in general should yield a detection, or one in every ∼ three lenses in the most sensitive sample. From 170 000 new strong lenses detected by Euclid, we expect ∼2500 new subhalo detections. We find that the expected number of detectable subhaloes in warm dark matter models only changes relative to cold dark matter for models which have already been ruled out, i.e. those with half-mode masses Mhm > 108 M☉