  Precision analysis of galaxy-galaxy strong gravitational lensing images
provides a unique way of characterizing small-scale dark matter halos, and
could allow us to uncover the fundamental properties of dark matter's
constituents. Recently, 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 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. Here,
we present the first analysis pipeline to combine parametric lensing models and
a recently-developed neural simulation-based inference technique called
truncated marginal neural ratio estimation (TMNRE) to constrain the warm dark
matter halo mass function cutoff scale directly from multiple lensing images.
Through a proof-of-concept application to simulated data, we show that our
approach enables empirically testable inference of the dark matter cutoff mass
through marginalization over a large population of realistic perturbers that
would be undetectable on their own, and over lens and source parameters
uncertainties. To obtain our results, we combine the signal contained in a set
of images with Hubble Space Telescope resolution. Our results suggest that
TMNRE can be a powerful approach to put tight constraints on the mass of warm
dark matter in the multi-keV regime, which will be relevant both for existing
lensing data and in the large sample of lenses that will be delivered by
near-future telescopes.
