Sensitivity of strong lensing observations to dark matter substructure: a case study with Euclid by Conor M. O'Riordan et al. on Wednesday 30 November

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 $v_\mathrm{max}$-$r_\mathrm{max}$

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 $3\sigma$ detection threshold, $2.35$ per cent

of pixels inside twice the Einstein radius are sensitive to subhaloes with a

mass $M_\mathrm{max}\leq 10^{10}M_\odot$, $0.03$ per cent are sensitive to

$M_\mathrm{max}\leq 10^{9}M_\odot$, and, the limit of sensitivity is found to

be $M_\mathrm{max}=10^{8.8\pm0.2}M_\odot$. Using our sensitivity maps and

assuming CDM, we estimate that Euclid-like lenses will yield

$1.43^{+0.14}_{-0.11}[f_\mathrm{sub}^{-1}]$ detectable subhaloes per lens in

the entire sample, but this increases to

$35.6^{+0.9}_{-0.9}[f_\mathrm{sub}^{-1}]$ per lens in the most sensitive

lenses. Estimates are given in units of the inverse of the substructure mass

fraction $f_\mathrm{sub}^{-1}$. Assuming $f_\mathrm{sub}=0.01$, one in every

$70$ lenses in general should yield a detection, or one in every $\sim$ three

lenses in the most sensitive sample. From $170,000$ new strong lenses detected

by Euclid, we expect $\sim 2500$ 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 $M_\mathrm{hm}>10^8M_\odot$.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.15679v1