Testing strong lensing subhalo detection with a cosmological simulation

Abstract

Strong gravitational lensing offers a compelling test of the cold dark matter paradigm, as it allows for subhaloes with masses of ∼ 10 9 M and below to be detected. We test commonly-used techniques for detecting dark matter subhaloes superposed in images of strongly lensed galaxies. For the lens we take a simulated galaxy in a ∼ 10 13 M halo grown in a high-resolution cosmological hydrodynamics simulation, which we view from two different directions. To remove particle noise, we represent the projected galaxy mass distribution by a series of analytic Gaussian profiles which precisely capture the features of the projected galaxy. We first model the lens mass as a broken power-law density profile (the parameterization of which includes a core ensuring the results are unaffected by a numerical central core in the simulated galaxy) and then search for small haloes. Of the two projections, one has a regular elliptical shape, while the other has distinct deviations from an elliptical shape. For the former, the broken power-law model gives no false positives and correctly recovers the mass of the superposed small halo, but for the latter we do find false positives and the inferred halo mass is overestimated by a factor of ∼ 4 − 5. We then replace the broken power-law model by a more complex model in which the lens mass is decomposed into separate stellar and dark matter components. In this case, we show that we can precisely capture the complex projected structures of the simulated galaxy and correctly infer the input parameters of the superposed small halo. By improving the lens galaxy mass model, we argue this enables more robust subhalo inference that can detect lower mass dark matter subhaloes, strengthening the technique's ability to test alternative models of dark matter which do not form dark matter subhaloes below a cut-off mass.

Abell 1201: detection of an ultramassive black hole in a strong gravitational lens

“…Recent works using PyAutoLens include modeling strong lenses simulated using stellar dynamics models (Cao et al 2021) and via a cosmological simulation He et al (2022), an automated analysis of 59 lenses (Etherington et al 2022) and studies of dark matter substructure (He et al 2020;Amorisco et al 2022).…”

“…Each fit in this chain uses the nested sampler (dynesty Speagle 2020) . The models used to perform this analysis extend the Source, Light and Mass (SLaM) pipelines described by Etherington et al (2022, hereafter E22), Cao et al (2021) and He et al (2022). They are available at https://github.com/ Jammy2211/autolens_workspace.…”

Lensed radio arcs at milli-arcsecond resolution: Methods, science results, and current status

“…However, it is becoming increasingly apparent that lens models should include azimuthal degrees of freedom as well. Overly simplistic models can bias inferences on dark matter subhalo populations, as disks and other baryonic structures can be mistaken for dark matter if not properly accounted for (Gilman et al 2017;Hsueh et al 2018;He et al 2022). Contraints on H 0 using time delays between lensed images can also suffer from biases if angular structure is not taken into account (Kochanek 2021;Cao et al 2022;Van de Vyvere et al 2022).…”

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

“…These assumptions are often priors on the shape of mass and light profiles, or on their higher order statistical properties. Currently used analytical profiles are sufficient to capture first-order properties of the lens mass distribution (e.g., power-law profiles), but they lack the degrees of freedom to capture those small-scale features that are critical for determining DM properties (e.g., He et al 2022).…”