Probing sub-galactic mass structure with the power spectrum of surface-brightness anomalies in high-resolution observations of galaxy-galaxy strong gravitational lenses. I. Power-spectrum measurement and feasibility study
arXiv:2302.00480 · doi:10.1093/mnras/stad1403
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.
arXiv comment20 pages, 19 figures, 2 tables, submitted to MNRAS
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- Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
- Investigation of mass substructure in gravitational lens system SDP.81 with ALMA long-baseline observations
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