Speaker
Description
Precision analysis of galaxy-galaxy strong gravitational lensing images provides a unique way of characterizing dark matter (DM) low-mass halos and could allow us to uncover the fundamental properties of DM's constituents. In recent years, gravitational imaging techniques made it possible to detect a few heavy subhalos. However, gravitational lenses contain numerous subhalos and line-of-sight halos, whose subtle imprint is extremely difficult to detect individually. Existing methods for marginalizing over this large population of sub-threshold perturbers in order to infer population-level parameters are typically computationally expensive, or require compressing observations into hand-crafted summary statistics, such as a power spectrum of residuals.
We will present the first analysis pipeline to combine parametric lensing models with a recently-developed targeted simulation-based inference technique called truncated marginal neural ratio estimation (TMNRE), in order to constrain the WDM halo mass function cutoff scale directly from multiple lensing images. In a proof-of-concept application to simulated data with Hubble Space Telescope (HST) resolution, we will show that our approach enables empirically testable inference of the DM cutoff mass down to $10^7\ M_\odot$, through marginalization over a large population of realistic perturbers that would be undetectable on their own, and over lens and source parameters uncertainties. Our results demonstrate that TMNRE is in principle able to extract the wealth of information regarding DM's nature contained in existing lensing data and in the large sample of lenses that will be delivered by near-future telescopes. We will conclude showing preliminary results on real HST data.