Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation. Issue 2 (7th November 2022)

• Record Type: Journal Article
• Authors: Anau Montel, Noemi; Coogan, Adam; Correa, Camila; Karchev, Konstantin; Weniger, Christoph
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 518:Issue 2(2023)
• Page Start: 2746
• Page End: 2760
• Publication Date: 2022-11-07
• Subjects: gravitational lensing: strong -- methods: statistical -- dark matter; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stac3215
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Precision analysis of galaxy–galaxy strong gravitational lensing images provides a unique way of characterizing small-scale dark matter haloes, 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 subhaloes. However, gravitational lenses contain numerous subhaloes and line-of-sight haloes, whose subtle imprint is extremely difficult to detect individually. Existing methods for marginalizing over this large population of subthreshold 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 cut-off 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 cut-off mass through marginalization over a large population of realistic perturbers that would be undetectable on their own, and over lens and source parameter 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.