Dark matter remains one of the most elusive components of the universe, constituting approximately 85% of all matter yet revealing itself only through gravitational interactions. Traditional methods of mapping dark matter distributions—such as weak gravitational lensing of background galaxies—have provided valuable insights but are constrained by resolution limits and the density of lensed sources. Enter Fast Radio Bursts (FRBs): these millisecond-duration, highly energetic radio transients originating from distant galaxies offer a novel probe for dark matter mapping via gravitational lensing.
Gravitational lensing occurs when the path of light from a distant source is bent by the gravitational field of an intervening mass. Dark matter halos, despite being invisible, warp spacetime sufficiently to distort and magnify background sources. The lensing signature depends on the mass distribution of the halo, providing a direct means to infer its structure.
The deflection angle α of an FRB’s light due to a dark matter halo can be described by:
α = (4GM/c2ξ) ∫ Σ(ξ′) dξ′
where:
FRBs possess unique properties that make them exceptional tools for dark matter lensing studies:
Modern radio telescopes like CHIME (Canadian Hydrogen Intensity Mapping Experiment) and the upcoming Square Kilometre Array (SKA) are revolutionizing FRB detection. High-time-resolution observations (µs precision) enable detailed lensing analyses:
When an FRB passes near a dark matter halo, its wavefront is distorted. By analyzing the interference patterns in the received signal, astronomers can reconstruct the halo’s mass profile. This technique has been successfully applied to FRB 181112, revealing a lensing galaxy’s dark matter component.
Multiple images of a lensed FRB arrive at different times due to path-length differences. The time delay Δt between images is given by:
Δt = (1 + zl) (DlDs/cDls) [½(θ12 - θ22) - ψ(θ1) + ψ(θ2)]
where:
Unlike galaxy-scale lensing, FRBs can resolve dark matter substructures down to ~106 M⊙, testing predictions of cold dark matter (CDM) models. Moreover, if dark matter consists of ultra-light axions, their wave-like nature would imprint distinctive interference patterns on lensed FRBs—a smoking gun for alternative dark matter candidates.
Bayesian inference and neural networks are increasingly employed to invert lensing observables into dark matter maps. For instance, convolutional neural networks (CNNs) trained on simulated FRB lensing data can rapidly extract halo parameters from noisy observations.
Despite their promise, FRB lensing studies face hurdles:
As FRB detectors grow more sensitive and analysis techniques mature, gravitational lensing of these cosmic flashes will unveil dark matter distributions with kiloparsec-scale resolution. This method not only tests CDM but also probes exotic physics—from primordial black holes to fuzzy dark matter—ushering in a new era of high-precision cosmology.
In 2023, researchers using ASKAP (Australian Square Kilometre Array Pathfinder) reported the first statistical detection of FRB lensing by dark matter halos, constraining the halo mass function at z > 1. Upcoming instruments like DSA-2000 (Deep Synoptic Array) will further enhance this capability.
Combining FRB lensing with:
Such multi-wavelength approaches will yield a unified picture of dark matter across scales.