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

Abstract: Analyses of extended arcs in strong gravitational lensing images to date have constrained the properties of dark matter by measuring the parameters of one or two individual subhalos. However, since such analyses are reliant on likelihood-based methods like Markov-chain Monte Carlo or nested sampling, they require various compromises to the realism of lensing models for the sake of computational tractability, such as ignoring the numerous other subhalos and line-of-sight halos in the system, assuming a particular form for the source model and requiring the noise to have a known likelihood function. Here we show that a simulation-based inference method calledTruncated Marginal Neural Ratio Estimation (TMNRE) makes it possible to relax these requirements by training neural networks to directly compute marginal posteriors for subhalo parameters from lensing images. By performing a set of inference tasks on mock data, we verify the accuracy of TMNRE and show it can compute posteriors for subhalo parameters marginalized over populations of hundreds of substructures, as well as lens and source uncertainties. We also find the \gls*{mlp} Mixer network works far better for such tasks than the convolutional architectures explored in other lensing analyses. Furthermore, we show that since \gls*{tmnre} learns a posterior function it enables direct statistical checks that would be extremely expensive with likelihood-based methods. Our results show that TMNRE is well-suited for analyzing complex lensing data, and that the full subhalo and line-of-sight halo population must be included when measuring the properties of individual dark matter substructures with this technique.

• The paper demonstrates that TMNRE enables accurate inference of subhalo parameters by overcoming limitations of traditional likelihood-based methods.
• It employs neural networks, specifically MLP Mixer architectures, to integrate numerous subhalos and line-of-sight halos, minimizing biases in lens models.
• The findings significantly impact dark matter research by refining subhalo measurements, with implications for future surveys by JWST and the Rubin Observatory.

Analysis of Perturbing Effects on Subhalo Measurements in Gravitational Lensing

The paper "One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses" presents a detailed study on the complexities involved in measuring dark matter (DM) subhalos in strong gravitational lensing scenarios. By employing a simulation-based inference method, namely truncated marginal neural ratio estimation (TMNRE), the authors aim to address the challenges posed by traditional likelihood-based methods and offer a more encompassing analysis of subhalo effects within lensing data.

Context and Motivation

Gravitational lensing offers a unique avenue for probing the distribution of dark matter, particularly through its effects on light from distant galaxies. Traditionally, analyses focused on individual subhalos have provided insights into DM properties, yet these approaches often necessitate compromises such as neglecting the extensive population of surrounding subhalos and line-of-sight (LOS) structures. Classic likelihood-based techniques like Markov-chain Monte Carlo (MCMC) and nested sampling require simplifying assumptions to manage computational demands, potentially leading to mischaracterizations of the cosmic DM substructure.

Introduction of TMNRE for Comprehensive Subhalo Analysis

In this study, the authors propose using TMNRE as a robust alternative to traditional methods. TMNRE is capable of directly calculating marginalized posteriors from lensing images without needing an explicit likelihood function. It employs neural networks to estimate these posteriors, allowing for the inclusion of numerous subhalos and LOS halos in the analyses and accounting for both lens and source uncertainties.

Key results and observations include:

• Verification of TMNRE Accuracy: Through inference on mock data, TMNRE was able to accurately compute posteriors for subhalo parameters, thus validating its performance against analytically-calculated benchmarks.
• Evaluation of Model Complexity: The application of TMNRE allowed for the consideration of a large number of potential perturbations from additional subhalos and LOS halos which are typically ignored. This holistic view of the lensing system helps avoid biases introduced by overly simplified models.
• Superior Architecture Choice: The authors highlight the superior performance of MLP Mixer networks over convolutional neural networks (CNNs) for handling the unique data structure in lensing images, underscoring the importance of choosing model architectures that are well-suited to the characteristics of astronomical data.

Implications and Future Directions

The findings underscore the necessity of including the full population of DM perturbers in analyzing strong lensing data to avoid systematic biases in subhalo mass function measurements. This has significant implications for constraints on DM properties, potentially allowing for refined models of DM physics that could resolve existing tensions in the ΛCDM model, such as the missing satellites and cusp-core problems.

The method's applicability to real-world, complex noise environments without known likelihood functions presents a significant advancement. This is particularly relevant as upcoming telescopes like the James Webb Space Telescope (JWST) and the Rubin Observatory will vastly increase the volume and quality of available lensing data, necessitating advanced methods for data analysis.

The authors' proposal to apply TMNRE for model comparison could enhance the detection and characterization of lenses' substructure, serving as a basis for future developments in weak lensing analyses. Expanding this framework to accommodate even more complex cosmological simulations and additional observations can drive forward our understanding of DM's fundamental properties.

In conclusion, this paper provides a strong case for TMNRE as a powerful tool for gravitational lensing analyses. It not only challenges the assumptions of previous methods but also expands the horizon of possibilities for future investigations into the microphysical nature of dark matter.