Baryonic Feedback Using FRBs

• Baryonic feedback using FRBs is a method that decodes cosmic baryon distribution by analyzing dispersion measures from both the IGM and CGM.
• The approach decomposes observed FRB DMs into contributions from the Milky Way, host galaxies, IGM, and CGM using statistical models and simulations.
• Results indicate that with ~100 well-localized FRBs, researchers can tightly constrain the IGM baryon fraction and differentiate between competing feedback models.

Fast Radio Bursts (FRBs) are brief, luminous radio pulses originating at cosmological distances whose signals are dispersed by their propagation through ionized media. The integrated electron column density encountered by each FRB is encoded in its dispersion measure (DM), which is a direct and quantitative tracer of cosmic baryons in both the circumgalactic medium (CGM) and intergalactic medium (IGM). FRB DMs thus provide a unique observational channel for constraining the distribution, state, and feedback-regulated evolution of baryonic matter in the Universe.

1. Principles of FRB Dispersion as a Baryonic Probe

The observed DM of an FRB can be decomposed into several contributions: $$\mathrm{DM_{obs}} = \mathrm{DM_{MW}} + \mathrm{DM_{host}} + \mathrm{DM_{EG}}$$ where $\mathrm{DM_{MW}}$ is the Milky Way (MW) foreground, $\mathrm{DM_{host}}$ is the host galaxy’s contribution, and $$\mathrm{DM_{EG}} = \mathrm{DM_{IGM}} + \mathrm{DM_{CGM}}$$ encompasses both IGM and (foreground) CGM contributions (Ravi, 2018). In practice, careful subtraction of MW and host terms is essential to isolate the extragalactic signal.

The IGM DM is modeled as a cosmic line-of-sight integral: $$\mathrm{DM_{IGM}}(z) = \int_0^{z_{\mathrm{FRB}}} \frac{n_e(z)}{1+z}\frac{c}{H(z)}dz$$ with $n_e(z)$ scaling with the IGM baryon fraction ($f_{\mathrm{IGM}}$). Conversely, the CGM’s DM contribution is assembled by summing bulk electron columns around intervening galactic halos encountered by the sightline. This requires assumptions about the radial electron profiles (e.g., constant-density vs. power-law/isothermal) and detailed knowledge of the galactic halo mass and redshift distribution along the path (Ravi, 2018).

Because feedback from star formation and AGN activity heats and redistributes baryons—regulating their escape from halos and their thermal state—the statistical properties of FRB DMs encode the integrated effects of these energetic processes across cosmic time (Medlock et al., 4 Mar 2024, Reischke et al., 23 Jul 2025).

2. Separation and Quantification of CGM and IGM Contributions

Robust discrimination between CGM and IGM DM components relies on associating foreground halos with the FRB sightline. This entails:

• Localizing the FRB and measuring its redshift.
• Identifying intervening galaxies via deep imaging and wide-field spectroscopy.
• Inferring halo masses (via stellar mass–halo mass relations) and using analytic profiles (e.g., NFW or variants) to calculate the predicted CGM DM for each halo (Ravi, 2018, Williams et al., 2022).

The total extragalactic DM is then analyzed as: $$\mathrm{DM_{EG}} = \sum_i \mathrm{DM_{CGM},i} + \mathrm{DM_{IGM}}$$ where the sum extends over all identified foreground halos, and the remaining unexplained DM is attributed to the diffuse IGM component. A likelihood function is constructed by comparing predicted and observed extragalactic DMs for an ensemble of FRBs, with model parameters including $f_{\mathrm{IGM}}$ and the power-law index $\alpha$ defining the assumed CGM density profile: $$\mathcal{L}(f_{\mathrm{IGM}}, \alpha) \propto \prod_i \exp\left[ -\frac{(\hat{\mathrm{DM}}_{\mathrm{EG},i} - \mathrm{DM}_{\mathrm{EG},i}(f_{\mathrm{IGM}}, \alpha))^2}{2\sigma_{\mathrm{DM}}^2} \right]$$ (Ravi, 2018, Medlock et al., 4 Mar 2024).

Sensitivity studies show that with $N_{\mathrm{FRB}} \sim 100$, constraints on $f_{\mathrm{IGM}}$ can reach an uncertainty of $\sim 0.06\, N_{\mathrm{FRB}}^{-1/2}$. Constraints on the CGM density profile ($\alpha$) are weaker due to the comparatively smaller impact of profile shape on DM for a fixed baryon fraction (Ravi, 2018).

3. Simulations, Observational Systematics, and Uncertainties

The frameworks employed in this context combine realistic simulated FRB samples—distributed in redshift according to cosmic star formation rate and weighted by volumetric cosmological factors—with mock samples of intervening halos constructed from the halo mass function and geometric cross-section arguments (Ravi, 2018, Williams et al., 2022). Each simulated sightline incorporates:

• Intrinsic IGM DM scatter ($\sim 10\,\mathrm{pc\,cm}^{-3}$) due to cosmic structure.
• Host DM, typically modeled as a log-normal distribution with median and scatter as additional nuisance parameters (Macquart et al., 2020, Theis et al., 13 Mar 2024).
• Uncertainties in MW and CGM contributions, with MW DM modeled via Galactic electron density models (e.g., NE2001, YMW16).
• Incompleteness in galaxy identification and errors in photometric/spectroscopic mass estimates ($\sim 0.3$ dex).

Recent work calibrates and quantifies these uncertainties, resulting in realistic forecasted extragalactic DM errors of $\sim 50\,\mathrm{pc\,cm}^{-3}$ per sightline (Ravi, 2018). Bayesian inference frameworks are widely adopted to marginalize over the diverse parameter space and incorporate observational systematics (Lin et al., 2023).

4. Results: Constraints on Baryonic Feedback and Partitioning

Application of the above methodology to observed samples and simulated FRBs yield robust results on baryon partitioning:

• With as few as 100 well-localized FRBs ($z < 1$), the overall baryon fraction in the IGM ($f_{\mathrm{IGM}}$) can be estimated to within 5–10% (95% confidence) (Ravi, 2018, Liu et al., 4 Jun 2025).
• The characteristic radial profile of halo gas (e.g., isothermal vs. constant-density) remains only weakly constrained with current sample sizes.
• Strong feedback models—ejecting baryons from halos to the IGM—yield reduced DM variance along sightlines and produce a more homogeneous IGM (quantified by a lower $F$ parameter, the fractional DM scatter) (Medlock et al., 4 Mar 2024).
• Mild or absent feedback retains more baryons in halos, producing enhanced DM variance and a broader sightline-to-sightline DM distribution.

Simulations (e.g., CAMELS: SIMBA, IllustrisTNG, Astrid) show that the detailed implementation of feedback (especially AGN jet feedback) alters both the mean and the variance of cosmic DM. SIMBA-like strong feedback leads to the lowest DM variance; Astrid’s weak feedback yields the highest (Medlock et al., 4 Mar 2024, Medlock et al., 29 Jan 2025). Comparisons with recent observational lower limits on $F$ indicate that too-strong feedback may be disfavored, whereas moderate feedback models conform to current measurements (Medlock et al., 4 Mar 2024).

5. Host Galaxy Contribution and the DM Distribution

The distribution of host galaxy DM ($\mathrm{DM_{host}}$) is vital for unbiased baryon census. Fits to simulated and observed host DM distributions indicate that both log-normal and Burr (Type XII) distributions can model the data, but heavy tails may be more accurately captured by the Burr family (Theis et al., 13 Mar 2024). Lévy distributions fit the extreme tail, implying that rare, high-DM events can arise from stochastic feedback activity or sightlines traversing dense CGM structures.

Quadratic models of host DM as a function of host stellar mass reveal a statistically significant anti-correlation: more massive hosts show systematically lower DMs, in tension with CGM models that predict a positive trend unless the ISM contribution is fine-tuned to be negative or suppressed (Leung et al., 22 Jul 2025). This empirical relation provides a lower bound on the strength of baryonic feedback in local ($z < 0.2$) $L^*$ halos.

6. Practical Applications and Future Prospects

The presented framework permits the following:

• Unambiguous resolution to the "missing baryons" problem: constraints from FRB DMs are consistent with both CMB and BBN baryon budgets, confirming that the majority of baryons reside in diffuse, ionized media (Macquart et al., 2020, Liu et al., 4 Jun 2025).
• Empirical discrimination between feedback models: Well-localized FRB samples, in combination with galaxy catalogs and large-volume simulations, provide a pathway to measure AGN and stellar feedback strengths on the cosmic baryon budget (Medlock et al., 4 Mar 2024, Medlock et al., 29 Jan 2025, Zhang et al., 17 Mar 2025).
• Synergy with lensing and SZ observations: Joint analyses of FRBs with weak lensing (Reischke et al., 2023) and Sunyaev-Zel'dovich measurements (Muñoz et al., 2018) can break degeneracies between baryonic and cosmological effects, refining estimates of the sum of neutrino masses and baryon-driven suppression parameters.
• Extensions to 3D baryon mapping: With increasing numbers of localized FRBs—expected from facilities such as ASKAP, VLA, DSA, and forthcoming imaging surveys—foreground tomography of electron density, and thus baryon mapping, is achievable on Mpc scales (Lee et al., 2021, Hsu et al., 6 May 2025).

Critical future improvements include larger FRB samples with precise redshifts, refined host DM priors (from host spectroscopy and multiwavelength properties), and the inclusion of spectroscopic census of foregrounds to minimize mass incompleteness in intervening halos (Lee et al., 2021). Improved hydrodynamical simulations (with baryonic resolution at the CGM/IGM interface) and machine learning–driven profile inference may further enhance discriminatory power.

7. Synthesis and Outlook

FRB dispersion measures have rapidly established themselves as a quantitative, high-precision, and physically direct probe of baryonic feedback and the baryon cycle in the circumgalactic and intergalactic media. By statistically analyzing sightline-integrated DMs in concert with the cosmic web traced by intervening galaxies and calibrated with state-of-the-art cosmological simulations, the community can now robustly partition cosmic baryons between the IGM and various halo environments, and measure the imprint of feedback physics—especially AGN-driven activity—on the thermodynamic state and spatial distribution of baryons. These advances lay the foundation for high-fidelity baryon censuses and the eventual unification of galactic feedback constraints across electromagnetic and gravitational observables—crucial steps for percent-level cosmology in the era of large-scale radio transient surveys.