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Examining the properties of subhalos with strong gravitational lensing imagescan shed light on the nature of dark matter. From upcoming large-scale surveys,we expect to discover orders of magnitude more strong lens systems that can beused for subhalo studies. To optimally extract information from a large numberof strong lensing images, machine learning provides promising avenues forefficient analysis that is unachievable with traditional analysis methods, butapplication of machine learning techniques to real observations is stilllimited. We build upon previous work, which uses a neural likelihood-ratioestimator, to constrain the effective density slopes of subhalos anddemonstrate the feasibility of this method on real strong lensing observations.To do this, we implement significant improvements to the forward simulationpipeline and undertake careful model evaluation using simulated images.Ultimately, we use our trained model to predict the effective subhalo densityslope from combining a set of strong lensing images taken by the \textit{HubbleSpace Telescope}. We found the subhalo slope measurement of this set ofobservations to be steeper than the slope predictions of cold dark mattersubhalos. Our result adds to several previous works that also measured highsubhalo slopes in observations. Although a possible explanation for this isthat subhalos with steeper slopes are easier to detect due to selection effectsand thus contribute to statistical bias, our result nevertheless points to theneed for careful analysis of more strong lensing observations from futuresurveys.

Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation