Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation

Abstract

Precision analysis of galaxy-galaxy strong gravitational lensing images provides a unique way of characterizing small-scale dark matter halos, and could allow us to uncover the fundamental properties of dark matter's constituents. Recently, gravitational imaging techniques made it possible to detect a few heavy subhalos. However, gravitational lenses contain numerous subhalos and line-of-sight halos, whose subtle imprint is extremely difficult to detect individually. Existing methods for marginalizing over this large population of sub-threshold perturbers to infer population-level parameters are typically computationally expensive, or require compressing observations into hand-crafted summary statistics, such as a power spectrum of residuals. Here, we present the first analysis pipeline to combine parametric lensing models and a recently-developed neural simulation-based inference technique called truncated marginal neural ratio estimation (TMNRE) to constrain the warm dark matter halo mass function cutoff scale directly from multiple lensing images. Through a proof-of-concept application to simulated data, we show that our approach enables empirically testable inference of the dark matter cutoff mass through marginalization over a large population of realistic perturbers that would be undetectable on their own, and over lens and source parameters uncertainties. To obtain our results, we combine the signal contained in a set of images with Hubble Space Telescope resolution. Our results suggest that TMNRE can be a powerful approach to put tight constraints on the mass of warm dark matter in the multi-keV regime, which will be relevant both for existing lensing data and in the large sample of lenses that will be delivered by near-future telescopes.

arXiv comment15 pages, 11 figures; Updated to match version published in MNRAS

References in corpus (60)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Planck 2015 results. XIII. Cosmological parameters
• A Universal Density Profile from Hierarchical Clustering
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• Dark Matter Substructure in Galactic Halos
• Where are the missing galactic satellites?
• The James Webb Space Telescope
• The Aquarius Project: the subhalos of galactic halos
• Toward a halo mass function for precision cosmology: the limits of universality
• Direct Detection of CDM Substructure
• The Sloan Lens ACS Survey. I. A Large Spectroscopically Selected Sample of Massive Early-Type Lens Galaxies
• Evidence for substructure in lens galaxies?
• The properties of warm dark matter haloes
• Substructure and halo density profiles in a Warm Dark Matter Cosmology
• Sterile Neutrino Dark Matter
• Gravitational detection of a low-mass dark satellite at cosmological distance
• Detection of lensing substructure using ALMA observations of the dusty galaxy SDP.81
• Lenstronomy: multi-purpose gravitational lens modelling software package
• Detection of a Dark Substructure through Gravitational Imaging
• Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
• Nonlinear Evolution of Cosmological Structures in Warm Dark Matter Models
• Warm dark matter chills out: constraints on the halo mass function and the free-streaming length of dark matter with 8 quadruple-image strong gravitational lenses
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• A Bayesian analysis of regularised source inversions in gravitational lensing
• Inference of the Cold Dark Matter substructure mass function at z=0.2 using strong gravitational lenses
• The BOSS Emission-Line Lens Survey (BELLS). I. A large spectroscopically selected sample of Lens Galaxies at redshift ~ 0.5
• SHARP -- VII. New constraints on the dark matter free-streaming properties and substructure abundance from gravitationally lensed quasars
• Analytic models of plausible gravitational lens potentials
• Detection of substructure with adaptive optics integral field spectroscopy of the gravitational lens B1422+231
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Probing dark matter structure down to $10^7$ solar masses: flux ratio statistics in gravitational lenses with line of sight halos
• Modelling the line-of-sight contribution in substructure lensing
• Low-mass halo perturbations in strong gravitational lenses at redshift z$\sim$0.5 are consistent with CDM
• Lensing substructure quantification in RXJ1131-1231: A 2 keV lower bound on dark matter thermal relic mass
• Dissecting the Gravitational Lens B1608+656. I. Lens Potential Reconstruction
• Statistics of mass substructure from strong gravitational lensing: quantifying the mass fraction and mass function
• The impact of baryonic physics on the subhalo mass function and implications for gravitational lensing
• Probing dark matter substructure in the gravitational lens HE0435-1223 with the WFC3 grism
• Measuring the power spectrum of dark matter substructure using strong gravitational lensing
• Constraining sterile neutrino cosmologies with strong gravitational lensing observations at redshift z~0.2
• Gravitational Lens Magnification and the Mass of Abell 1689
• The LSST DESC DC2 Simulated Sky Survey
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• Quantifying dwarf satellites through gravitational imaging: the case of SDSS J120602.09+514229.5
• SHARP - IV. An apparent flux ratio anomaly resolved by the edge-on disc in B0712+472
• Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
• Probing the nature of dark matter by forward modeling flux ratios in strong gravitational lenses
• SHARP - II. Mass structure in strong lenses is not necessarily dark matter substructure: A flux ratio anomaly from an edge-on disc
• Deep Learning the Morphology of Dark Matter Substructure
• The inner mass power spectrum of galaxies using strong gravitational lensing: beyond linear approximation
• Probing the small-scale structure in strongly lensed systems via transdimensional inference
• Towards a general parametrization of the warm dark matter halo mass function
• Strong Gravitational Lensing with SKA
• Beyond subhalos: Probing the collective effect of the Universe's small-scale structure with gravitational lensing
• The Saas Fee Lectures on Strong Gravitational Lensing
• SLITronomy: towards a fully wavelet-based strong lensing inversion technique
• Quantifying the power spectrum of small-scale structure in semi-analytic galaxies
• A high-resolution cosmological simulation of a strong gravitational lens
• Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

Cited by in corpus (13)

• Strong Lensing by Galaxies
• One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses
• Scalable inference with Autoregressive Neural Ratio Estimation
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines
• Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation
• Fast Likelihood-free Reconstruction of Gravitational Wave Backgrounds
• Fast likelihood-free inference in the LSS Stage IV era
• SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data
• Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
• Detecting the hidden population of low-mass haloes in strong lenses
• Deep inference of simulated strong lenses in ground-based surveys
• Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation
• How to embed any likelihood into SBI: Application to Planck + Stage IV galaxy surveys and Dynamical Dark Energy