Neural posterior estimation of the line-of-sight and subhalo populations in galaxy-scale strong lensing systems

TL;DR

This paper investigates the use of neural density estimators to detect anisotropic signatures in galaxy-scale strong lensing images, aiming to infer dark matter substructure and line-of-sight halos, but faces challenges due to data generation and modeling limitations.

Contribution

It introduces a neural density estimation approach to identify anisotropic features in lensing data related to dark matter substructure and line-of-sight halos, highlighting current limitations.

Findings

Difficulty in accurately recovering subhalo mass functions.

Degeneracy between line-of-sight halo and subhalo parameters.

Limited accuracy in predicting two-point function multipoles.

Abstract

Strong gravitational lensing is a powerful probe for studying the fundamental properties of dark matter on sub-galactic scales. Detailed analyses of galaxy-scale lenses have revealed localized gravitational perturbations beyond the smooth mass distribution of the main lens galaxy, largely attributed to dark matter subhalos and intervening line-of-sight halos. Recent studies suggest that, in contrast to subhalos, line-of-sight halos imprint distinct anisotropic features on the two-point correlation function of the effective lensing deflection field. These anisotropies are particularly sensitive to the collisional nature of dark matter, offering a potential means to test alternatives to the cold dark matter paradigm. In this study, we explore whether a neural density estimator can directly identify such anisotropic signatures from galaxy-galaxy strong lens images. We model the multipoles of the two-point function using a power-law parameterization and train a neural density estimator to predict the corresponding posterior distribution of lensing parameters, alongside parameter distributions for dark matter substructure. Our results show that recovering the dark matter substructure mass functions and mass-concentration parameters remains challenging, owing to difficulties in generating uniform training data set while using physically motivated priors. We also unveil an important degeneracy between the line-of-sight halo mass-function amplitude and the subhalo mass-function normalization. Furthermore, the network exhibits limited accuracy in predicting the two-point function multipole parameters, suggesting that both the training data and the adopted power-law fitting function may inadequately represent the true underlying structure of the anisotropic signal.