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Analyses of extended arcs in strong gravitational lensing images to date haveconstrained the properties of dark matter by measuring the parameters of one ortwo individual subhalos. However, since such analyses are reliant onlikelihood-based methods like Markov-chain Monte Carlo or nested sampling, theyrequire various compromises to the realism of lensing models for the sake ofcomputational tractability, such as ignoring the numerous other subhalos andline-of-sight halos in the system, assuming a particular form for the sourcemodel and requiring the noise to have a known likelihood function. Here we showthat a simulation-based inference method called truncated marginal neural ratioestimation (TMNRE) makes it possible to relax these requirements by trainingneural networks to directly compute marginal posteriors for subhalo parametersfrom lensing images. By performing a set of inference tasks on mock data, weverify the accuracy of TMNRE and show it can compute posteriors for subhaloparameters marginalized over populations of hundreds of subhalos andline-of-sight halos, as well as lens and source uncertainties. We also find theMLP Mixer network works far better for such tasks than the convolutionalarchitectures explored in other lensing analyses. Furthermore, we show thatsince TMNRE learns a posterior function it enables direct statistical checksthat would be extremely expensive with likelihood-based methods. Our resultsshow that TMNRE is well-suited for analyzing complex lensing data, and that thefull subhalo and line-of-sight halo population must be included when measuringthe properties of individual dark matter substructures.

One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses