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

Abstract

Examining the properties of subhalos with strong gravitational lensing images can 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 be used for subhalo studies. To optimally extract information from a large number of strong lensing images, machine learning provides promising avenues for efficient analysis that is unachievable with traditional analysis methods, but application of machine learning techniques to real observations is still limited. We build upon previous work, which uses a neural likelihood-ratio estimator, to constrain the effective density slopes of subhalos and demonstrate the feasibility of this method on real strong lensing observations. To do this, we implement significant improvements to the forward simulation pipeline and undertake careful model evaluation using simulated images. Ultimately, we use our trained model to predict the effective subhalo density slope from combining a set of strong lensing images taken by the \textit{Hubble Space Telescope}. We found the subhalo slope measurement of this set of observations to be steeper than the slope predictions of cold dark matter subhalos. Our result adds to several previous works that also measured high subhalo slopes in observations. Although a possible explanation for this is that subhalos with steeper slopes are easier to detect due to selection effects and thus contribute to statistical bias, our result nevertheless points to the need for careful analysis of more strong lensing observations from future surveys.

11 pages, 6 figures, v2; matches published version in MNRAS

References in corpus (20)

• The Cosmic Evolution Survey (COSMOS) -- Overview
• The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample
• Do ImageNet Classifiers Generalize to ImageNet?
• The stellar mass-halo mass relation of isolated field dwarfs: a critical test of $\Lambda$CDM at the edge of galaxy formation
• Inference of the Cold Dark Matter substructure mass function at z=0.2 using strong gravitational lenses
• An excess of small-scale gravitational lenses observed in galaxy clusters
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• The tidal evolution of dark matter substructure -- II. The impact of artificial disruption on subhalo mass functions and radial profiles
• Core-collapse, evaporation and tidal effects: the life story of a self-interacting dark matter subhalo
• Self-Interacting Dark Matter and Small-Scale Gravitational Lenses in Galaxy Clusters
• On the Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses
• Substructure Detection Reanalyzed: Dark Perturber shown to be a Line-of-Sight Halo
• The probability of galaxy-galaxy strong lensing events in hydrodynamical simulations of galaxy clusters
• From Images to Dark Matter: End-To-End Inference of Substructure From Hundreds of Strong Gravitational Lenses
• A persistent excess of galaxy-galaxy strong lensing observed in galaxy clusters
• Inferring the concentration of dark matter subhalos perturbing strongly lensed images
• Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation
• Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
• Image segmentation for analyzing galaxy-galaxy strong lensing systems
• Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation

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• Semi-Analytic Modeling of Dark Matter Subhalo Encounters with Thin Stellar Streams: Statistical Predictions for GD-1-like Streams in CDM
• Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing
• LSST Strong Lensing Systems Dark Matter Sensitivity Analysis with Neural Ratio Estimators