Dark Matter Astrometry: Accuracy of sub-halo positions for the measurement of self-interaction cross sections

TL;DR

This paper assesses the precision of weak gravitational lensing in measuring dark matter substructure positions, crucial for understanding dark matter interactions, demonstrating sufficient accuracy for detecting expected offsets in galaxy cluster mergers.

Contribution

It provides the first detailed analysis of the accuracy of dark matter substructure position measurements using simulated HST data with existing mass reconstruction algorithms.

Findings

Substructure positions can be measured with less than 0.3 bias.

The method can detect mean offsets at 3σ significance in 50 clusters.

Simulated data confirms the feasibility of using weak lensing for dark matter offset measurements.

Abstract

Direct evidence for the existence of dark matter and measurements of its interaction cross-section have been provided by the physical offset between dark matter and intra- cluster gas in merging systems like the Bullet Cluster. Although a smaller signal, this effect is more abundant in minor mergers where infalling substructure dark matter and gas are segregated. In such low-mass systems the gravitational lensing signal comes primarily from weak lensing. A fundamental step in determining such an offset in sub- structure is the ability to accurately measure the positions of dark matter sub-peaks. Using simulated Hubble Space Telescope observations, we make a first assessment of the precision and accuracy with which we can measure infalling groups using weak gravitational lensing. We demonstrate that using an existing and well-used mass re- construction algorithm can measure the positions of 1.5x1013 M substructures that have parent halos ten times more massive with a bias of less than 0.3 . In this regime, our analysis suggests the precision is sufficient to detect (at 3 {\sigma} statistical significance) the expected mean offset between dark matter and baryonic gas in infalling groups from a sample of 50 massive clusters.