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

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.