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ABSTRACT While the direct detection of the dark-matter particle remains very challenging, the nature of dark matter could be possibly constrained by comparing the observed abundance and properties of small-scale sub-galactic mass structures with predictions from the phenomenological dark-matter models, such as cold, warm, or hot dark matter. Galaxy-galaxy strong gravitational lensing provides a unique opportunity to search for tiny surface-brightness anomalies in the extended lensed images (i.e. Einstein rings or gravitational arcs), induced by possible small-scale mass structures in the foreground lens galaxy. In this paper, the first in a series, we introduce and test a methodology to measure the power spectrum of such surface-brightness anomalies from high-resolution Hubble Space Telescope (HST) imaging. In particular, we focus on the observational aspects of this statistical approach, such as the most suitable observational strategy and sample selection, the choice of modelling techniques, and the noise correction. We test the feasibility of the power-spectrum measurement by applying it to a sample of galaxy-galaxy strong gravitational lens systems from the Sloan Lens ACS Survey, with the most extended, bright, high-signal-to-noise-ratio lensed images, observed in the rest-frame ultraviolet. In the companion paper, we present the methodology to relate the measured power spectrum to the statistical properties of the underlying small-scale mass structures in the lens galaxy and infer the first observational constraints on the sub-galactic matter power spectrum in a massive elliptical (lens) galaxy.
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From Images to Dark Matter: End-to-end Inference of Substructure from Hundreds of Strong Gravitational Lenses
Abstract Constraining the distribution of small-scale structure in our universe allows us to probe alternatives to the cold dark matter paradigm. Strong gravitational lensing offers a unique window into small dark matter halos (<1010M⊙) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large data sets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST’s COSMOS field. Using a simulation-based inference pipeline, we train a neural posterior estimator of the subhalo mass function (SHMF) and place constraints on populations of lenses generated using a separate set of galaxy sources. We find that by combining our network with a hierarchical inference framework, we can both reliably infer the SHMF across a variety of configurations and scale efficiently to populations with hundreds of lenses. By conducting precise inference on large and complex simulated data sets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys.
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- Award ID(s):
- 1716527
- PAR ID:
- 10391268
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 942
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 75
- Size(s):
- Article No. 75
- Sponsoring Org:
- National Science Foundation
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