A new measurement of the FRB DM-galaxy cross correlation and a first joint analysis with the kinematic SZ effect

This paper presents a new measurement of the correlation between the dispersion measure (DM) of Fast Radio Bursts (FRBs) and the positions of foreground galaxies, using a sample of 130 localized FRBs and the DESI Legacy Survey Bright Galaxy Sample (BGS). The authors detect this signal at the highest significance to date (6.5σ) in both configuration and harmonic space.

The key results and methodology are as follows:

1. Data and Measurement: The sample includes 130 localized FRBs with host redshifts, including 11 new unpublished FRBs from the DSA-110. The galaxy sample consists of 27.3 million galaxies from the DESI BGS, with photometric redshifts and stellar masses. The authors measure the FRB DM-galaxy cross-correlation in real space (configuration space) and harmonic space (angular power spectrum), using a halo model and hydrodynamical simulations (FLAMINGO) for interpretation.

2. Detection of Signal and Feedback Constraints: The measurement detects the DM-galaxy correlation at 6.5σ significance. By comparing to simulations and a halo model, the authors demonstrate that this statistic can constrain the strength of baryonic feedback. The measurements strongly disfavor a no-feedback scenario (in which gas traces the underlying dark matter) at 9σ. The lower two stellar mass bins prefer a strong feedback scenario, while the highest mass bin (log10 M∗/M⊙ > 11) is too uncertain to favor any model.

3. Comparison with kSZ Effect: The authors compare their FRB measurement to recent kinematic Sunyaev-Zeldovich effect (kSZ) studies of similar galaxy samples. They find good agreement between the two probes despite differences in observational methods and sample selection. The FRB and kSZ signals are consistent at the 1σ level across different mass bins.

4. Joint Analysis with kSZ: The paper presents a first attempt at breaking the kSZ optical depth degeneracy using FRBs. By combining the FRB measurement (which is sensitive only to electron density) with kSZ measurements (which are sensitive to the product of electron density and velocity), the authors jointly constrain the growth rate of large-scale structure, fσ8, and the kSZ velocity bias factor, bv.

5. Conclusion: The authors conclude that with future FRB samples, two-point statistics will provide precision constraints on the distribution of cosmic baryons and complement other probes like the kSZ effect. They note that FRBs are particularly good at measuring large scales (> a few Mpc), where kSZ measurements are limited by primary CMB fluctuations.

Improvements to AI Systems:

1. Baryon Distribution Inference Engine: Build an AI system that uses FRB DM–galaxy cross-correlation data (e.g., the 6.5σ signal) as a direct observational prior to predict gas density profiles around galaxies of different stellar masses. The improved system can output spatially resolved electron density maps (from 0.1 to 10 Mpc) and automatically update them as new FRB samples arrive, replacing crude dark-matter-only assumptions in cosmological simulations.

2. Feedback Scenario Classifier: Train a neural network on the halo model and FLAMINGO simulation outputs (no-feedback, strong-feedback, weak-feedback) to classify which feedback regime best matches observed DM–galaxy correlations per stellar mass bin. The improved system can provide real-time posterior probabilities for feedback strength, enabling automated selection of the correct subgrid physics for galaxy formation simulations.

3. Cross-Probe Consistency Checker: Develop an AI that jointly ingests FRB DM cross-correlation and kSZ measurements (from the same galaxy sample) to detect systematic inconsistencies between the two probes. The improved system can flag mass bins or redshift ranges where the two signals diverge beyond 1σ, alerting researchers to potential selection biases or unmodeled astrophysical effects (e.g., dust, redshift errors).

4. Joint fσ8–bv Constraint Optimizer: Create a variational inference system that takes both FRB (electron density–only) and kSZ (density × velocity) data as inputs and outputs a joint posterior over the growth rate fσ8 and the kSZ velocity bias factor bv. The improved system can break the optical depth degeneracy automatically, providing tighter cosmological constraints than either probe alone and scaling to thousands of FRBs without manual MCMC tuning.

5. Large-Scale Baryon Map Predictor: Use the measured DM–galaxy correlation to train a generative model (e.g., a conditional diffusion model) that predicts the full 3D cosmic web of free electrons on scales >5 Mpc, where kSZ is limited by CMB foregrounds. The improved system can produce mock FRB DM maps for survey design, optimize future FRB telescope targeting, and directly test predictions of baryon feedback models against upcoming wide-field surveys.

6. Automated Systematics Mitigator: Implement an AI that learns to separate true astrophysical DM–galaxy correlation from systematic contaminants (e.g., photometric redshift errors, incomplete galaxy sampling, FRB localization uncertainties) by training on simulated mocks with injected systematics. The improved system can automatically correct the measured correlation function and power spectrum, reducing false detections and improving significance estimates for future FRB samples.