The effect of the perturber population on subhalo measurements in strong gravitational lenses

Analyses of extended arcs in strong gravitational lensing images to date have constrained the properties of dark matter by measuring the parameters of one or two individual subhaloes. However, since such analyses are reliant on likelihood-based methods like Markov-chain Monte Carlo or nested sampling, they require various compromises to the realism of lensing models for the sake of computational tractability, such as ignoring the numerous other subhaloes and line-of-sight haloes in the system, assuming a particular form for the source model and requiring the noise to have a known likelihood function. Here, we show that a simulation-based inference method called truncated marginal neural ratio estimation (TMNRE) makes it possible to relax these requirements by training neural networks to directly compute marginal posteriors for subhalo parameters from lensing images. By performing a set of inference tasks on mock data, we verify the accuracy of TMNRE and show it can compute posteriors for subhalo parameters marginalized over populations of hundreds of substructures, as well as lens and source uncertainties. We also find that the multilayer perceptron (MLP) mixer network works far better for such tasks than the convolutional architectures explored in other lensing analyses. Furthermore, we show that since TMNRE learns a posterior function it enables direct statistical checks that would be extremely expensive with likelihood-based methods. Our results show that TMNRE is well-suited for analysing complex lensing data, and that the full subhalo and line-of-sight halo population must be included when measuring the properties of individual dark matter substructures with this technique

GAMA/H-ATLAS: Common star formation rate indicators and their dependence on galaxy physical parameters

We compare common star formation rate (SFR) indicators in the local Universe in the Galaxy and Mass Assembly (GAMA) equatorial fields (∼160 deg2), using ultraviolet (UV) photometry from GALEX, far-infrared and sub-millimetre (sub-mm) photometry from Herschel Astrophysical Terahertz Large Area Survey, and Hα spectroscopy from the GAMA survey. With a high-quality sample of 745 galaxies (median redshift 〈z〉 = 0.08), we consider three SFR tracers: UV luminosity corrected for dust attenuation using the UV spectral slope β (SFRUV, corr), Hα line luminosity corrected for dust using the Balmer decrement (BD) (SFRH α, corr), and the combination of UV and infrared (IR) emission (SFRUV + IR). We demonstrate that SFRUV, corr can be reconciled with the other two tracers after applying attenuation corrections by calibrating Infrared excess (IRX; i.e. the IR to UV luminosity ratio) and attenuation in the Hα (derived from BD) against β. However, β, on its own, is very unlikely to be a reliable attenuation indicator. We find that attenuation correction factors depend on parameters such as stellar mass (M*), z and dust temperature (Tdust), but not on Hα equivalent width or Sérsic index. Due to the large scatter in the IRX versus β correlation, when compared to SFRUV + IR, the β-corrected SFRUV, corr exhibits systematic deviations as a function of IRX, BD and Tdust

Herschel-ATLAS: the surprising diversity of dust-selected galaxies in the local submillimetre Universe

Galaxie

Galaxy And Mass Assembly (GAMA): data release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions

Galaxie

Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation

Computational astrophysic