Evidence for Enhancement in the Rate of Fast Radio Bursts Toward Galaxy Clusters

The paper demonstrates that the second CHIME/FRB baseband catalog contains Fast Radio Bursts (FRBs) emitted from within or behind galaxy clusters identified from the latest DECaLS data release. The authors isolate a sample of 26 FRBs where this is likely, including one repeating FRB and two FRBs that intersect the Coma cluster. Comparing their results against a range of simulated FRB populations, they conclude that the number of these associations represents a 3σ rate enhancement toward galaxy clusters, constituting 1.4 ± 0.4% of the FRBs detected in the second CHIME/FRB baseband catalog. They suggest that this enhancement is caused by an equal proportion of member galaxies hosting additional FRBs, and gravitational lensing magnifying background sources. They demonstrate that such contributions are sensitive to alternative progenitor channels and high redshift evolution in the FRB population, providing a future avenue for constraining these features. Considering the redshifts and masses of the associated clusters, they identify FRB 20211113A, which is aligned with the strong gravitational lens Abell 2218 as a potential lensed candidate for further consideration.

The paper's abstract states: "Massive galaxy clusters can introduce gravitational lensing and populations of suppressed star formation member galaxies into the line of sight, potentially changing the distribution of observable sources toward them from that of the average sky. As a result, Fast Radio Bursts (FRBs) aligned with galaxy clusters can provide a unique window into parameter spaces of the FRB population that other lines of sight do not afford. In this study, we demonstrate that the second CHIME/FRB baseband catalog contains FRBs emitted from within or behind clusters identified from the latest DECaLS data release; we isolate a sample of 26 FRBs where this is likely, including one repeating FRB and two FRBs that intersect the Coma cluster. Comparing our results against a range of simulated FRB populations, we conclude that the number of these associations represents a 3σ rate enhancement toward galaxy clusters, constituting 1.4 ± 0.4% of the FRBs detected in the second CHIME/FRB baseband catalog. We suggest that this enhancement is caused by an equal proportion of member galaxies hosting additional FRBs, and gravitational lensing magnifying background sources. We demonstrate that such contributions are sensitive to alternative progenitor channels and high redshift evolution in the FRB population, providing a future avenue for constraining these features. Considering the redshifts and masses of the associated clusters, we identify FRB 20211113A, which is aligned with the strong gravitational lens Abell 2218 as a potential lensed candidate for further consideration."

The paper's conclusion section states: "To date, FRB population modeling has been largely successful in unifying the observations from disparate FRB surveys into a cohesive depiction of FRB behaviour. To extend this success further, we propose that population studies of future FRB samples consider specific FRB subpopulations in isolation, as a way to investigate emerging discrepancies in simple population models and to probe blind-spots in the data for which FRB surveys are, on average, insensitive. Specifically, we show that FRBs emitted from within or behind massive galaxy clusters are sensitive to the high-redshift regime and to the way in which the FRB population traces stellar mass, providing an insightful window for future population studies. To enable these studies, we demonstrate that the second CHIME/FRB baseband catalog must contain FRBs piercing foreground galaxy clusters. We show this by extracting a relation between impact parameter and DM that matches the characteristic shape and temperature for an ICM with an NFW profile. Using a Monte Carlo resampling to characterize the likelihood of cluster association, we isolate a subsample of 26 FRBs from the CHIME/FRB catalog that are likely to be associated with massive galaxy clusters, including one repeating FRB, and two FRBs that pierce the Coma Cluster at different impact parameters. We compare our results with a sophisticated set of simulations predicting the expected number of galaxy cluster associations due to emissions from their member galaxies, random interceptions, and gravitational lensing of background sources. We find that random interceptions by unmagnified FRBs are the dominant source of these cluster associations, but are insufficient to completely explain the number of observed associations at the 3σ level, constituting the detection of a 1.4 ± 0.4% increase in the total number of detected FRBs in the second CHIME/FRB baseband catalog, due to the presence of massive galaxy clusters. For reasonable assumptions for the FRB population, we suggest that both FRBs emitted from within, and lensed by galaxy clusters are required to reconcile our simulations with the observed sample. We suggest that lensed and member FRBs should each account for 5 − 10% of cluster associations that we make, with direct cluster emission only dominating over CHIME background rates for massive nearby clusters. Despite the small fraction of associations we expect to be contributed by direct and lensed modes, we find that such contributions will eventually allow the number of associated cluster FRBs to constrain the high-redshift behaviour of, and alternative progenitor channels in the FRB population. Not only does this highlight the importance of cluster FRBs as probes of sub-dominant features in the FRB population, but more generally, it shows that through statistical methods, unlocalized bursts remain a useful data product for understanding the FRB phenomena. Finally, by characterising the typical redshift and mass of the FRB-associated clusters, we find that high-mass cluster associations (M ≥ 5 × 1014 M⊙) are far more likely to be contributed by gravitational lensing than by direct cluster emission or chance interception. We identify one such burst in our sample, FRB 20211113A, which is aligned with the strong lensing cluster Abell 2218 with M500 = 9.2 × 1014 M⊙. From a single unlocalized burst, there is no way to definitively confirm magnification from gravitational lensing, however we highlight FRB 20211113A as an interesting candidate for further consideration."

Improvements to AI Systems:

1. Enhanced Gravitational Lensing Detection for Unlocalized Transients

• Improvement: Train AI models to identify potential strongly lensed FRBs by cross-referencing unlocalized burst positions with galaxy cluster catalogs (e.g., DECaLS, Abell clusters) and using cluster mass/redshift as priors.

• Capability: The AI can automatically flag candidate lensed FRBs (like FRB 20211113A) with probability scores, even without arcsecond localization, by modeling the expected magnification bias and DM excess from cluster halos.

2. Bayesian Inference for FRB–Cluster Association

• Improvement: Implement a Monte Carlo resampling–based Bayesian framework (as in the paper) to compute posterior probabilities of FRB–cluster associations, incorporating impact parameter, DM contribution from the intracluster medium (NFW profile), and foreground galaxy contamination.

• Capability: The AI can output a calibrated confidence level (e.g., 3σ enhancement) for any given FRB sample, separating chance alignments from true physical associations.

3. Population Synthesis with Subdominant Progenitor Channels

• Improvement: Extend existing FRB population simulators to include separate emission channels: (a) member galaxies in clusters, (b) gravitationally lensed background sources, and (c) random interceptions. The AI can then fit observed cluster-FRB counts to constrain the relative contributions (e.g., 5–10% each) and high-redshift evolution.

• Capability: The AI can predict how many cluster-associated FRBs are needed to distinguish between progenitor models (e.g., magnetars vs. binary mergers) and to infer the star-formation–stellar-mass tracing of FRB sources at z > 1.

4. DM–Impact Parameter Relation Extraction

• Improvement: Train a regression model to map observed dispersion measures (DM) of FRBs to cluster impact parameters, using the characteristic NFW shape and temperature of the intracluster medium as a physical prior.

• Capability: The AI can estimate the line-of-sight distance of an FRB through a cluster, enabling automatic classification of piercing events and improving cluster mass estimates from single bursts.

5. Automated Identification of High-Mass Cluster Lensing Candidates

• Improvement: Build a classifier that prioritizes FRBs aligned with high-mass clusters (M ≥ 5×1014 M⊙) as likely lensed, based on the paper's finding that lensing dominates in this regime.

• Capability: The AI can generate a shortlist of FRBs for follow-up with very long baseline interferometry (VLBI) or optical/IR imaging, maximizing the chance of confirming magnification and measuring source redshifts.

6. Simulation-to-Data Calibration for Rate Enhancements

• Improvement: Develop an AI that automatically calibrates simulated FRB populations against real catalogs (e.g., CHIME/FRB baseband) to detect subtle rate enhancements (e.g., 1.4 ± 0.4%) toward clusters, with built-in uncertainty propagation.

• Capability: The AI can flag statistically significant overdensities in any sky region (not just clusters) and quantify the contribution of foreground structures to observed FRB counts.

7. High-Redshift Evolution Constraint via Cluster FRBs

• Improvement: Use the AI to model how the fraction of cluster-associated FRBs evolves with redshift, given that clusters act as cosmic magnifying glasses for distant sources.

• Capability: The AI can infer the redshift distribution of FRB progenitors and test whether the FRB rate follows cosmic star formation history or stellar mass density, even without individual redshift measurements.

8. Unlocalized Burst Exploitation for Cosmology

• Improvement: Implement a statistical pipeline that treats unlocalized FRBs as a population, using angular cross-correlation with cluster catalogs to extract cosmological signals (e.g., lensing magnification bias, optical depth).

• Capability: The AI can turn large, unlocalized FRB samples into a probe of dark matter distribution and cluster physics, without requiring expensive localization follow-ups.

9. Progenitor Channel Discrimination via Cluster Environment

• Improvement: Train a model to predict the expected DM and scattering properties of FRBs from cluster member galaxies vs. lensed background sources, using cluster mass and redshift as inputs.

• Capability: The AI can classify individual cluster-associated FRBs as likely member or lensed based on their DM excess and angular offset, providing direct evidence for alternative FRB birth sites.

10. Adaptive Survey Strategy for Cluster FRB Follow-Up

• Improvement: Use reinforcement learning to optimize future FRB survey pointing and exposure times toward known massive clusters, maximizing the yield of lensed and member FRBs.

• Capability: The AI can recommend real-time observing schedules for CHIME or next-generation arrays to efficiently build a cluster-FRB sample that can constrain high-redshift FRB physics within a few years.