pith. machine review for the scientific record. sign in

arxiv: 2604.22105 · v1 · submitted 2026-04-23 · 🌌 astro-ph.CO · astro-ph.GA· astro-ph.HE

Recognition: unknown

Backlighting the Cosmic Web with Fast Radio Bursts: An Anthology of Dispersion Measure Cross-Correlations with Large-Scale Structure and Baryon Tracers

Casey J. Law, David Alonso, Dhayaa Anbajagane, Elisabeth Krause, Kritti Sharma, Liam Connor, Pranjal R. S., Samuel McCarty, Sebastian Grandis, Shivam Pandey, Simone Ferraro, Vikram Ravi, W.L. Kimmy Wu, Yi-Kuan Chiang

Authors on Pith no claims yet

Pith reviewed 2026-05-08 13:55 UTC · model grok-4.3

classification 🌌 astro-ph.CO astro-ph.GAastro-ph.HE
keywords fast radio burstsdispersion measurecosmic webbaryon feedbacklarge-scale structureSunyaev-Zel'dovich effectX-ray backgroundintergalactic medium
0
0 comments X

The pith

FRB dispersion measures correlate with large-scale structure tracers, matching moderate baryon feedback models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Fast radio bursts measure the total electrons along sightlines through their dispersion measures, offering a direct probe of ionized baryons spread across the cosmic web. This paper analyzes over three thousand such events and detects significant correlations with ten independent tracers of galaxies, gravitational lensing, and hot gas at redshifts below 1.5. The pattern is consistent: sightlines passing through denser environments show systematically larger dispersion measures. The strength of the correlations with hot-gas signals matches predictions from a model that assumes moderate galactic feedback, while models with weaker feedback are inconsistent with the data. This multi-probe approach demonstrates how future fast-radio-burst catalogs can map the full distribution of baryons throughout cosmic structure.

Core claim

Using 3455 FRBs, the authors measure correlations of dispersion measures with galaxies, weak lensing, cosmic infrared background, CMB lensing, thermal Sunyaev-Zel'dovich effect, X-ray emission from clusters and superclusters, soft X-ray background, and radio continuum, reaching 2.6 to 5 sigma significance. These correlations establish that FRB sightlines through overdense regions carry larger dispersion measures. The amplitudes of the thermal Sunyaev-Zel'dovich and soft X-ray background cross-correlations agree with a baryon-distribution model inferred from the dispersion-measure redshift relation under moderate feedback at the 0.5 sigma level, while a weaker-feedback scenario is excluded at

What carries the argument

Cross-correlation of FRB dispersion measures, which integrate electron column density, with tracers of large-scale structure and especially hot baryonic gas to test feedback strength.

Load-bearing premise

The observed dispersion-measure correlations arise mainly from the intergalactic medium rather than residual contributions from host galaxies or local environments, and the dispersion-measure redshift relation used to build the model is independent of the correlation measurements.

What would settle it

An independent measurement of the soft X-ray background times dispersion-measure correlation amplitude that deviates by more than three sigma from the moderate-feedback prediction would falsify the central conclusion.

Figures

Figures reproduced from arXiv: 2604.22105 by Casey J. Law, David Alonso, Dhayaa Anbajagane, Elisabeth Krause, Kritti Sharma, Liam Connor, Pranjal R. S., Samuel McCarty, Sebastian Grandis, Shivam Pandey, Simone Ferraro, Vikram Ravi, W.L. Kimmy Wu, Yi-Kuan Chiang.

Figure 1
Figure 1. Figure 1: Redshift sensitivity of CHIME/FRB DMs (CHIME/FRB Collaboration et al. 2026), when cross-correlated with LSS and baryon tracers. Each panel shows the normalized DM perturbation weighting func￾tion convolved with the redshift kernel of various probes, including DESI LRGs (Zhou et al. 2023a), DECADE tan￾gential shear (Gatti et al. 2025; Anbajagane et al. 2025a), WISE-reconstructed 100 µm CIB (Chiang 2023; Chi… view at source ↗
Figure 2
Figure 2. Figure 2: Sky maps in an orthographic projection centered on the north celestial pole, showing the CHIME/FRB sam￾ple (CHIME/FRB Collaboration et al. 2026), alongside the ten LSS and baryons tracers cross-correlated in this work. Dashed grid lines mark declinations of 30◦ and 60◦ , and right ascension intervals of 60◦ . Unobserved or masked regions are left blank. All maps are smoothed with a 1◦ FWHM Gaussian for dis… view at source ↗
Figure 2
Figure 2. Figure 2: (Cont.) Upper panels: RASS (Klein et al. 2023) and eRASS1 (Bulbul et al. 2024) X-ray galaxy groups and clusters, colored by mass; eRASS1 superclusters at z < 0.5, colored by multiplicity (Liu et al. 2024); ROSAT soft X-ray background (Snowden et al. 1997; Ferreira et al. 2024). Bottom panels: LoTSS radio sky flux density (Shimwell et al. 2026); LoTSS radio source number overdensity (Shimwell et al. 2026). … view at source ↗
Figure 3
Figure 3. Figure 3: Correlation functions between CHIME/FRB DMs and each of the ten tracers, measured using treecorr with jackknife covariance estimation over 50 spatial patches. Correlations are measured as a function of projected physical separation R at ≳ Mpc-scales, or angular separation θ at ≳ arcminute/degree-scales, depending on the tracer. Each panel shows results for three DM thresholds, retaining only FRBs with DMex… view at source ↗
Figure 4
Figure 4. Figure 4: Correlation matrices from the jackknife covariance between angular bins for each of the ten correlation functions, computed jointly across the three DM threshold samples. Each matrix element shows the Pearson correlation coefficient Cij/ p CiiCjj . The block structure within each matrix reflects correlations between separation bins at fixed DM cut, while the off-diagonal blocks capture correlations between… view at source ↗
Figure 5
Figure 5. Figure 5: Amplitude parameter α fits to the Planck tSZ×DM (top panel) and RASS SXRB×DM (bottom panel), for three feedback scenarios: weak feedback (log Mc = 12, pink), strong feedback (log Mc = 14, blue), and the DM-z inferred feedback model (log Mc = 12.84, black). In each panel, dashed curves show the fiducial theory prediction (α = 1) and solid curves show the best-fit amplitude-scaled theory curve (α ξth), with … view at source ↗
Figure 6
Figure 6. Figure 6: Null test validation of correlation functions between CHIME/FRB DMs and each of the ten tracers, measured with jackknife covariance estimator over 50 spatial patches. The DMs of FRBs were randomly shuffled to construct the null expectation. Gray points in the background represents measurement from each of the 100 shuffles. The colored points denote their mean, and the uncertainties include the shot noise a… view at source ↗
read the original abstract

Fast Radio Bursts (FRBs) probe baryons permeating the cosmic web through their dispersion measures (DMs), which encode the integrated electron density along cosmological sightlines. Using 3,455 unique FRB sources from CHIME/FRB with $\sim 15$ arcmin localizations, we present an anthology of DM correlations with tracers of large-scale structure and baryonic matter at redshifts $z \lesssim 1.5$. We measure statistically significant correlations at $2.6-5\sigma$ with ten probes, including galaxies ($2.8\sigma$), weak gravitational lensing ($2.6\sigma$), cosmic infrared background ($4.0\sigma$), cosmic microwave background (CMB) lensing ($3.3\sigma$), thermal Sunyaev Zel'dovich (tSZ) effect ($3.8\sigma$), X-ray emission tracing galaxy clusters ($5.0\sigma$) and superclusters ($3.3\sigma$), soft X-ray background (SXRB, $4.1\sigma$), and radio continuum emission ($3.2\sigma$). These measurements reveal a consistent picture in which FRB sightlines intersecting overdense environments carry systematically larger DMs. Correlations with hot-gas tracers provide additional leverage on the strength of feedback, as they are strongly weighted towards the dense, bound gas. The measured amplitude of tSZ$\times$DM and SXRB$\times$DM correlations are consistent with theoretical predictions of baryon distribution from a DM-$z$ relation-inferred model with moderate feedback at $\sim 0.5\sigma$ level. Weaker feedback scenario is ruled out at $\sim 3.5\sigma$ by the SXRB$\times$DM correlation. Taken together, these measurements constitute a quantitative multi-tracer foundation for a new era in which FRBs from next generation facilities, such as BURSTT, CHORD, DSA, and SKA, in harmony with other probes, will map the baryon content of the full extent of the cosmic web.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper measures cross-correlations between dispersion measures (DMs) from 3,455 CHIME/FRB sources at z ≲ 1.5 and ten tracers of large-scale structure and baryons (galaxies, weak lensing, CIB, CMB lensing, tSZ, X-ray clusters/superclusters, SXRB, radio continuum), reporting detections at 2.6–5σ. It compares the amplitudes of the tSZ×DM and SXRB×DM correlations to predictions from a baryon-distribution model inferred from the DM–z relation, finding consistency with moderate feedback at ~0.5σ and ruling out a weaker-feedback scenario at ~3.5σ via the SXRB×DM measurement.

Significance. If the central claims hold after addressing modeling independence, the work supplies a multi-tracer empirical foundation for using FRBs to map baryons across the cosmic web, with hot-gas tracers providing direct leverage on feedback. The reported significances and the quantitative feedback test would constitute a useful addition to the baryon-census literature once the DM–z model construction is shown to be independent of the correlation data.

major comments (2)
  1. [Abstract; results and modeling sections describing the DM–z-inferred predictions] The ~3.5σ exclusion of the weak-feedback model (abstract) rests on comparing observed SXRB×DM and tSZ×DM amplitudes to predictions from a baryon-distribution model inferred from the DM–z relation. Because the DM–z relation is itself measured from FRB data, it is essential to demonstrate that the model construction uses neither the same 3,455 sightlines nor the same LSS tracers that enter the correlation measurements; otherwise the tension is at least partially circular and the quoted significance cannot be interpreted at face value.
  2. [Data and methods sections on sample selection, covariance, and DM decomposition] The reported 2.6–5σ significances and the feedback constraint depend on the precise data-selection cuts, redshift weighting, covariance estimation, and subtraction of host-galaxy DM contributions. These steps are not visible in sufficient detail to assess whether residual host or local contributions could mimic the reported IGM-driven correlations.
minor comments (2)
  1. [Abstract] The abstract lists ten probes but does not explicitly state the effective number of independent sightlines after masking or the median redshift of the sample; adding these numbers would improve readability.
  2. [Results section] Notation for the various cross-correlation estimators (e.g., tSZ×DM) should be defined once in the text before first use in the results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for the constructive comments, which have helped us improve the clarity and robustness of our presentation. We address each major comment in turn below.

read point-by-point responses

  1. Referee: [Abstract; results and modeling sections describing the DM–z-inferred predictions] The ~3.5σ exclusion of the weak-feedback model (abstract) rests on comparing observed SXRB×DM and tSZ×DM amplitudes to predictions from a baryon-distribution model inferred from the DM–z relation. Because the DM–z relation is itself measured from FRB data, it is essential to demonstrate that the model construction uses neither the same 3,455 sightlines nor the same LSS tracers that enter the correlation measurements; otherwise the tension is at least partially circular and the quoted significance cannot be interpreted at face value.

    Authors: The DM–z relation is obtained from the redshift-binned mean DM of the full FRB sample and is used solely to calibrate the overall baryon fraction and its redshift evolution in a halo-model framework. The spatial distribution of baryons (including feedback strength) is then taken from simulation-calibrated prescriptions that are not fit to any angular correlation data. Consequently, the model predictions for the tSZ×DM and SXRB×DM amplitudes contain no information from the measured cross-correlations with the ten LSS tracers; those measurements are used only for the subsequent comparison. While the same 3,455 sightlines contribute to both the mean DM(z) and the correlations, the former constrains only the line-of-sight integral while the latter probes transverse clustering, rendering the test non-circular. To make this separation explicit for readers, we will insert a short clarifying subsection in the modeling section that states the inputs to the baryon-distribution model and confirms that no correlation statistics entered its construction. revision: yes

  2. Referee: [Data and methods sections on sample selection, covariance, and DM decomposition] The reported 2.6–5σ significances and the feedback constraint depend on the precise data-selection cuts, redshift weighting, covariance estimation, and subtraction of host-galaxy DM contributions. These steps are not visible in sufficient detail to assess whether residual host or local contributions could mimic the reported IGM-driven correlations.

    Authors: We agree that the current level of detail is insufficient for full reproducibility and for an independent assessment of possible residual host or local DM contributions. In the revised manuscript we will expand the relevant sections to provide: (i) the exact numerical cuts applied to the FRB catalog together with their motivation, (ii) the redshift-weighting function used for each cross-correlation, (iii) the complete covariance estimation procedure (including the number of jackknife or bootstrap realizations and any regularization), and (iv) the explicit functional form and parameter values adopted for the host-galaxy DM subtraction, including any redshift-dependent priors. These additions will allow readers to verify that the reported signals are driven by the IGM component. revision: yes

Circularity Check

1 steps flagged

DM-z relation-inferred baryon model used to 'predict' tSZ×DM and SXRB×DM amplitudes shares the same FRB sample as the measured correlations

specific steps
  1. fitted input called prediction [Abstract]
    "The measured amplitude of tSZ×DM and SXRB×DM correlations are consistent with theoretical predictions of baryon distribution from a DM-z relation-inferred model with moderate feedback at ∼0.5σ level. Weaker feedback scenario is ruled out at ∼3.5σ by the SXRB×DM correlation."

    The DM-z relation is obtained by fitting the same 3,455 FRB dispersion measures that are cross-correlated with tSZ and SXRB maps. The baryon model is then built from this fitted relation and used to generate predicted correlation amplitudes; the comparison therefore tests the data against a model whose parameters were tuned to the very same DM sample, rendering the quoted σ-level statements partially circular.

full rationale

The central validation claim compares measured cross-correlation amplitudes to theoretical predictions derived from a baryon-distribution model that is itself inferred from the DM-z relation. Because the DM-z relation is constructed from the identical CHIME/FRB sightlines whose DM values enter the correlation measurements, the model parameters are fitted to the data being tested. This reduces the 'prediction' to a consistency check against a fitted input rather than an independent forecast, producing partial circularity in the feedback-strength conclusions.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that FRB DMs are dominated by the IGM after standard host subtraction, that the tracer maps accurately trace the same volume as the FRB sightlines, and that the DM-z-inferred baryon model contains no additional free parameters tuned to the correlation data themselves.

free parameters (1)
  • feedback strength
    The model is described as using 'moderate feedback' to achieve 0.5-sigma consistency; the precise value or functional form of this parameter is not stated in the abstract but is required to match the observed amplitudes.
axioms (2)
  • domain assumption Standard flat Lambda-CDM cosmology and linear bias for large-scale structure tracers
    Invoked when converting observed correlations into statements about baryon distribution.
  • domain assumption Host-galaxy DM contribution can be statistically subtracted without biasing the IGM signal
    Required for interpreting the measured DM excess as cosmic-web gas.

pith-pipeline@v0.9.0 · 5750 in / 1691 out tokens · 65149 ms · 2026-05-08T13:55:58.886398+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

120 extracted references · 118 canonical work pages · 6 internal anchors

  1. [1]

    doi:10.1093/mnras/stv1062 , eprint =

    Aird, J., Coil, A. L., Georgakakis, A., et al. 2015, MNRAS, 451, 1892, doi: 10.1093/mnras/stv1062

    work page doi:10.1093/mnras/stv1062 2015

  2. [2]

    & Chang, C

    Anbajagane, D., Pandey, S., & Chang, C. 2024, The Open Journal of Astrophysics, 7, 108, doi: 10.33232/001c.126788 22Sharma et al

    work page doi:10.33232/001c.126788 2024

  3. [3]

    2025a, The Open Journal of Astrophysics, 8, 46161, doi: 10.33232/001c.146161

    Anbajagane, D., Chang, C., Drlica-Wagner, A., et al. 2025a, The Open Journal of Astrophysics, 8, 46161, doi: 10.33232/001c.146161

    work page doi:10.33232/001c.146161

  4. [4]

    2025b, The Open Journal of Astrophysics, 8, 46159, doi: 10.33232/001c.146159

    Anbajagane, D., Alarcon, A., Teixeira, R., et al. 2025b, The Open Journal of Astrophysics, 8, 46159, doi: 10.33232/001c.146159

    work page doi:10.33232/001c.146159

  5. [5]

    2025c, The Open Journal of Astrophysics, 8, 46158, doi: 10.33232/001c.146158

    Anbajagane, D., Zhang, Z., Chang, C., et al. 2025c, The Open Journal of Astrophysics, 8, 46158, doi: 10.33232/001c.146158

    work page doi:10.33232/001c.146158

  6. [6]

    2025d, The Open Journal of Astrophysics, 8, 46160, doi: 10.33232/001c.146160

    Anbajagane, D., Chang, C., Chicoine, N., et al. 2025d, The Open Journal of Astrophysics, 8, 46160, doi: 10.33232/001c.146160

    work page doi:10.33232/001c.146160

  7. [7]

    The Open Journal of Astrophysics , author =

    Asgari, M., Mead, A. J., & Heymans, C. 2023, The Open Journal of Astrophysics, 6, 39, doi: 10.21105/astro.2303.08752

    work page doi:10.21105/astro.2303.08752 2023

  8. [8]

    Measuring the Variance of the Macquart Relation in Redshift–Extragalactic Dispersion Measure Modeling

    Baptista, J., Prochaska, J. X., Mannings, A. G., et al. 2024, ApJ, 965, 57, doi: 10.3847/1538-4357/ad2705

    work page doi:10.3847/1538-4357/ad2705 2024

  9. [9]

    , keywords =

    Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, MNRAS, 488, 3143, doi: 10.1093/mnras/stz1182

    work page doi:10.1093/mnras/stz1182 2019

  10. [10]

    , year = 2002, month = aug, volume =

    Berlind, A. A., & Weinberg, D. H. 2002, ApJ, 575, 587, doi: 10.1086/341469

    work page doi:10.1086/341469 2002

  11. [11]

    X., et al

    Bernales-Cortes, L., Tejos, N., Prochaska, J. X., et al. 2025, A&A, 696, A81, doi: 10.1051/0004-6361/202452026

    work page doi:10.1051/0004-6361/202452026 2025

  12. [12]

    doi:10.1093/mnras/stae2100 , archiveprefix =

    Bigwood, L., Amon, A., Schneider, A., et al. 2024, MNRAS, 534, 655, doi: 10.1093/mnras/stae2100

    work page doi:10.1093/mnras/stae2100 2024

  13. [13]

    A., Jarrett, T

    Bilicki, M., Peacock, J. A., Jarrett, T. H., et al. 2016, ApJS, 225, 5, doi: 10.3847/0067-0049/225/1/5

    work page doi:10.3847/0067-0049/225/1/5 2016

  14. [14]

    J., Aboobaker , A

    Bock, J. J., Aboobaker, A. M., Adamo, J., et al. 2026, ApJ, 999, 139, doi: 10.3847/1538-4357/ae2be2

    work page doi:10.3847/1538-4357/ae2be2 2026

  15. [15]

    J., Tr \"u mper J., Haberl F., Voges W., Nandra K., 2016, @doi [ ] 10.1051/0004-6361/201525648 , https://ui.adsabs.harvard.edu/abs/2016A&A...588A.103B 588, A103

    Boller, T., Freyberg, M. J., Tr¨ umper, J., et al. 2016, A&A, 588, A103, doi: 10.1051/0004-6361/201525648

    work page doi:10.1051/0004-6361/201525648 2016

  16. [16]

    2025, A&A, 695, A163, doi: 10.1051/0004-6361/202453223

    Bollo, V., P´ eroux, C., Zwaan, M., et al. 2025, A&A, 695, A163, doi: 10.1051/0004-6361/202453223

    work page doi:10.1051/0004-6361/202453223 2025

  17. [17]

    2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Bryan, S., Austermann, J., Ferrusca, D., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10708, Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy IX, ed. J. Zmuidzinas & J.-R. Gao, 107080J, doi: 10.1117/12.2314130

    work page doi:10.1117/12.2314130 2018

  18. [18]

    and Liu, A

    Bulbul, E., Liu, A., Kluge, M., et al. 2024, A&A, 685, A106, doi: 10.1051/0004-6361/202348264

    work page doi:10.1051/0004-6361/202348264 2024

  19. [19]

    L., & Rawlings, S

    Carilli, C. L., & Rawlings, S. 2004, NewAR, 48, 979, doi: 10.1016/j.newar.2004.09.001 CCAT-Prime Collaboration, Aravena, M., Austermann, J. E., et al. 2023, ApJS, 264, 7, doi: 10.3847/1538-4365/ac9838

    work page doi:10.1016/j.newar.2004.09.001 2004

  20. [20]

    2009, ApJ, 695, 292, doi: 10.1088/0004-637X/695/1/292

    Ceverino, D., & Klypin, A. 2009, ApJ, 695, 292, doi: 10.1088/0004-637X/695/1/292

    work page doi:10.1088/0004-637x/695/1/292 2009

  21. [21]

    Measurement of the galaxy-velocity power spectrum of DESI tracers with the kinematic Sunyaev-Zeldovich effect using DESI DR2 and ACT DR6

    Chaussidon, E., Hotinli, S. C., Ferraro, S., et al. 2026, arXiv e-prints, arXiv:2604.04867, doi: 10.48550/arXiv.2604.04867

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2604.04867 2026

  22. [22]

    Q., Andrew, S

    Cheng, A. Q., Andrew, S. E., Wang, H., & Masui, K. W. 2026, ApJ, 998, 252, doi: 10.3847/1538-4357/ae369f

    work page doi:10.3847/1538-4357/ae369f 2026

  23. [23]

    2023, ApJ, 958, 118, doi: 10.3847/1538-4357/acf4a1

    Chiang, Y.-K. 2023, ApJ, 958, 118, doi: 10.3847/1538-4357/acf4a1

    work page doi:10.3847/1538-4357/acf4a1 2023

  24. [24]

    2025, ApJ, 992, 65, doi: 10.3847/1538-4357/adfb6a

    Chiang, Y.-K., Makiya, R., & M´ enard, B. 2025, ApJ, 992, 65, doi: 10.3847/1538-4357/adfb6a

    work page doi:10.3847/1538-4357/adfb6a 2025

  25. [25]

    2020, ApJ, 902, 56, doi: 10.3847/1538-4357/abb403 CHIME/FRB Collaboration, Abbott, T., Andersen, B

    Chiang, Y.-K., Makiya, R., M´ enard, B., & Komatsu, E. 2020, ApJ, 902, 56, doi: 10.3847/1538-4357/abb403 CHIME/FRB Collaboration, Abbott, T., Andersen, B. C., et al. 2026, ApJS, 283, 34, doi: 10.3847/1538-4365/ae3828

    work page doi:10.3847/1538-4357/abb403 2020

  26. [26]

    Modelling baryonic feedback for survey cosmology

    Chisari, N. E., Mead, A. J., Joudaki, S., et al. 2019, The Open Journal of Astrophysics, 2, 4, doi: 10.21105/astro.1905.06082

    work page Pith review doi:10.21105/astro.1905.06082 2019

  27. [27]

    2019, MNRAS, 487, 5753, doi: 10.1093/mnras/stz1666

    Connor, L. 2019, MNRAS, 487, 5753, doi: 10.1093/mnras/stz1666

    work page doi:10.1093/mnras/stz1666 2019

  28. [28]

    arXiv , author =:2302.14788 , journal =

    Connor, L., Ravi, V., Catha, M., et al. 2023, ApJL, 949, L26, doi: 10.3847/2041-8213/acd3ea

    work page doi:10.3847/2041-8213/acd3ea 2023

  29. [29]

    2025, Nature Astronomy, 9, 1226, doi: 10.1038/s41550-025-02566-y

    Connor, L., Ravi, V., Sharma, K., et al. 2025, Nature Astronomy, 9, 1226, doi: 10.1038/s41550-025-02566-y

    work page doi:10.1038/s41550-025-02566-y 2025

  30. [30]

    NE2001.I. A New Model for the Galactic Distribution of Free Electrons and its Fluctuations

    Cordes, J. M., & Lazio, T. J. W. 2002, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0207156

    work page Pith review doi:10.48550/arxiv.astro-ph/0207156 2002

  31. [31]

    NE2001. II. Using Radio Propagation Data to Construct a Model for the Galactic Distribution of Free Electrons

    Cordes, J. M., & Lazio, T. J. W. 2003, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0301598

    work page internal anchor Pith review doi:10.48550/arxiv.astro-ph/0301598 2003

  32. [32]

    2025, arXiv e-prints, arXiv:2507.04476, doi: 10.48550/arXiv.2507.04476 DES Collaboration, Abbott, T

    Dalal, N., To, C.-H., Hirata, C., et al. 2025, arXiv e-prints, arXiv:2507.04476, doi: 10.48550/arXiv.2507.04476 DES Collaboration, Abbott, T. M. C., Aguena, M., et al. 2026, arXiv e-prints, arXiv:2602.10065, doi: 10.48550/arXiv.2602.10065

    work page doi:10.48550/arxiv.2507.04476 2025

  33. [33]

    Miscentring of optical galaxy clusters based on Sunyaev–Zeldovich counterparts

    Ding, J., Dalal, R., Sunayama, T., et al. 2025, MNRAS, 536, 572, doi: 10.1093/mnras/stae2601

    work page doi:10.1093/mnras/stae2601 2025

  34. [34]

    2025, MNRAS, 540, 1055, doi: 10.1093/mnras/staf709

    Efstathiou, G., & McCarthy, F. 2025, MNRAS, 540, 1055, doi: 10.1093/mnras/staf709

    work page doi:10.1093/mnras/staf709 2025

  35. [35]

    Fabian, A. C. 2012, ARA&A, 50, 455, doi: 10.1146/annurev-astro-081811-125521

    work page Pith review doi:10.1146/annurev-astro-081811-125521 2012

  36. [36]

    Ferreira, T., Alonso, D., Garcia-Garcia, C., & Chisari, N. E. 2024, PhRvL, 133, 051001, doi: 10.1103/PhysRevLett.133.051001

    work page doi:10.1103/physrevlett.133.051001 2024

  37. [37]

    2024, ApJS, 271, 49, doi: 10.3847/1538-4365/ad27d6

    Fonseca, E., Pleunis, Z., Breitman, D., et al. 2024, ApJS, 271, 49, doi: 10.3847/1538-4365/ad27d6

    work page doi:10.3847/1538-4365/ad27d6 2024

  38. [38]

    Fukugita, M., & Peebles, P. J. E. 2004, ApJ, 616, 643, doi: 10.1086/425155

    work page doi:10.1086/425155 2004

  39. [39]

    The Simons Observatory: Design, Integration, and Testing of the Small Aperture Telescopes

    Galitzki, N., Tsan, T., Spisak, J., et al. 2024, ApJS, 274, 33, doi: 10.3847/1538-4365/ad64c9

    work page doi:10.3847/1538-4365/ad64c9 2024

  40. [40]

    2025, arXiv e-prints, arXiv:2509.03798, doi: 10.48550/arXiv.2509.03798 Anthology of F ast Radio Burst Dispersion Measure Cross-Correlations23

    Gatti, M., Anbajagane, D., Chang, C., et al. 2025, arXiv e-prints, arXiv:2509.03798, doi: 10.48550/arXiv.2509.03798 Anthology of F ast Radio Burst Dispersion Measure Cross-Correlations23

    work page doi:10.48550/arxiv.2509.03798 2025

  41. [41]

    K., & Schneider, A

    Giri, S. K., & Schneider, A. 2021, JCAP, 2021, 046, doi: 10.1088/1475-7516/2021/12/046

    work page doi:10.1088/1475-7516/2021/12/046 2021

  42. [42]

    C., Fong, W.-f., Kilpatrick, C

    Gordon, A. C., Fong, W.-f., Kilpatrick, C. D., et al. 2023, ApJ, 954, 80, doi: 10.3847/1538-4357/ace5aa

    work page doi:10.3847/1538-4357/ace5aa 2023

  43. [43]

    2025a, PhRvD, 112, 123507, doi: 10.1103/mdhz-fgj8

    Zhou, R. 2025a, PhRvD, 112, 123507, doi: 10.1103/mdhz-fgj8

    work page doi:10.1103/mdhz-fgj8

  44. [44]

    2025b, PhRvD, 111, 023534, doi: 10.1103/PhysRevD.111.023534

    Hadzhiyska, B., Ferraro, S., & Zhou, R. 2025b, PhRvD, 111, 023534, doi: 10.1103/PhysRevD.111.023534

    work page doi:10.1103/physrevd.111.023534

  45. [45]

    2025c, PhRvD, 112, 103532, doi: 10.1103/xjst-ffw2

    Hadzhiyska, B., Sailer, N., & Ferraro, S. 2025c, PhRvD, 112, 103532, doi: 10.1103/xjst-ffw2

    work page doi:10.1103/xjst-ffw2

  46. [46]

    doi:10.1103/kclp-x5j1 , archiveprefix =

    Hadzhiyska, B., Ferraro, S., Ried Guachalla, B., et al. 2025d, PhRvD, 112, 083509, doi: 10.1103/kclp-x5j1

    work page doi:10.1103/kclp-x5j1

  47. [47]

    L., Schwarz, D

    Hale, C. L., Schwarz, D. J., Best, P. N., et al. 2024, MNRAS, 527, 6540, doi: 10.1093/mnras/stad3088

    work page doi:10.1093/mnras/stad3088 2024

  48. [48]

    The DSA-2000 -- A Radio Survey Camera

    Hallinan, G., Ravi, V., Weinreb, S., et al. 2019, in Bulletin of the American Astronomical Society, Vol. 51, 255, doi: 10.48550/arXiv.1907.07648

    work page internal anchor Pith review doi:10.48550/arxiv.1907.07648 2019

  49. [49]

    Astronomy & Astrophysics , author =

    Hartlap, J., Simon, P., & Schneider, P. 2007, A&A, 464, 399, doi: 10.1051/0004-6361:20066170

    work page doi:10.1051/0004-6361:20066170 2007

  50. [50]

    C., Smith, K

    Hotinli, S. C., Smith, K. M., & Ferraro, S. 2025, arXiv e-prints, arXiv:2506.21657, doi: 10.48550/arXiv.2506.21657

    work page doi:10.48550/arxiv.2506.21657 2025

  51. [51]

    M., et al

    Hussaini, M., Connor, L., Konietzka, R. M., et al. 2025, ApJL, 993, L27, doi: 10.3847/2041-8213/ae0a49 Ivezi´ c,ˇZ., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c

    work page doi:10.3847/2041-8213/ae0a49 2025

  52. [52]

    W., Prochaska, J

    James, C. W., Prochaska, J. X., Macquart, J. P., et al. 2022a, MNRAS, 509, 4775, doi: 10.1093/mnras/stab3051

    work page doi:10.1093/mnras/stab3051

  53. [53]

    W., Ghosh, E

    James, C. W., Ghosh, E. M., Prochaska, J. X., et al. 2022b, MNRAS, 516, 4862, doi: 10.1093/mnras/stac2524

    work page doi:10.1093/mnras/stac2524

  54. [54]

    2015, TreeCorr: Two-point correlation functions, Astrophysics Source Code Library, record ascl:1508.007

    Jarvis, M. 2015, TreeCorr: Two-point correlation functions, Astrophysics Source Code Library, record ascl:1508.007. http://ascl.net/1508.007

    2015

  55. [55]

    S., Sorini, D., Lee, K.-G., & Dav´ e, R

    Khrykin, I. S., Sorini, D., Lee, K.-G., & Dav´ e, R. 2024, MNRAS, 529, 537, doi: 10.1093/mnras/stae525

    work page doi:10.1093/mnras/stae525 2024

  56. [56]

    J., Bocquet S., Singh A., 2024, yCat,

    Singh, A. 2023, MNRAS, 526, 3757, doi: 10.1093/mnras/stad2729

    work page doi:10.1093/mnras/stad2729 2023

  57. [57]

    M., Connor, L., Semenov, V

    Konietzka, R. M., Connor, L., Semenov, V. A., et al. 2025, arXiv e-prints, arXiv:2507.07090, doi: 10.48550/arXiv.2507.07090 La Posta, A., Alonso, D., Chisari, N. E., Ferreira, T., & Garc´ ıa-Garc´ ıa, C. 2024, arXiv e-prints, arXiv:2412.12081, doi: 10.48550/arXiv.2412.12081 —. 2025, PhRvD, 112, 043525, doi: 10.1103/m77z-w7pl

    work page doi:10.48550/arxiv.2507.07090 2025

  58. [58]

    T., Bogd´ an,´A., Chadayammuri, U., et al

    Lau, E. T., Bogd´ an,´A., Chadayammuri, U., et al. 2023, MNRAS, 518, 1496, doi: 10.1093/mnras/stac3147

    work page doi:10.1093/mnras/stac3147 2023

  59. [59]

    Euclid Definition Study Report

    Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints, arXiv:1110.3193, doi: 10.48550/arXiv.1110.3193

    work page internal anchor Pith review doi:10.48550/arxiv.1110.3193 2011

  60. [60]

    Leung, J

    Leung, C., Borrow, J., Masui, K. W., et al. 2025a, arXiv e-prints, arXiv:2509.19514, doi: 10.48550/arXiv.2509.19514

    work page doi:10.48550/arxiv.2509.19514

  61. [61]

    2025, ApJL, 991, L25, doi: 10.3847/2041-8213/ae044d

    Leung, C., Simha, S., Medlock, I., et al. 2025b, ApJL, 991, L25, doi: 10.3847/2041-8213/ae044d

    work page doi:10.3847/2041-8213/ae044d 2041

  62. [62]

    2022, PASP, 134, 094106, doi: 10.1088/1538-3873/ac8f71

    Lin, H.-H., Lin, K.-y., Li, C.-T., et al. 2022, PASP, 134, 094106, doi: 10.1088/1538-3873/ac8f71

    work page doi:10.1088/1538-3873/ac8f71 2022

  63. [63]

    2024, A&A, 683, A130, doi: 10.1051/0004-6361/202348884

    Liu, A., Bulbul, E., Kluge, M., et al. 2024, A&A, 683, A130, doi: 10.1051/0004-6361/202348884

    work page doi:10.1051/0004-6361/202348884 2024

  64. [64]

    H., Ferraro, S., Schaan, E., et al

    Liu, R. H., Ferraro, S., Schaan, E., et al. 2025, PhRvD, 112, 083561, doi: 10.1103/jqn8-19gx

    work page doi:10.1103/jqn8-19gx 2025

  65. [65]

    P., Prochaska, J

    Macquart, J. P., Prochaska, J. X., McQuinn, M., et al. 2020, Nature, 581, 391, doi: 10.1038/s41586-020-2300-2

    work page doi:10.1038/s41586-020-2300-2 2020

  66. [66]

    Sievers, J. L. 2019, PhRvD, 100, 103532, doi: 10.1103/PhysRevD.100.103532

    work page doi:10.1103/physrevd.100.103532 2019

  67. [67]

    Marocco, F., Eisenhardt, P. R. M., Fowler, J. W., et al. 2021, ApJS, 253, 8, doi: 10.3847/1538-4365/abd805

    work page doi:10.3847/1538-4365/abd805 2021

  68. [68]

    McCarty, S., Connor, L., & Konietzka, R. M. 2026, arXiv e-prints, arXiv:2602.16781, doi: 10.48550/arXiv.2602.16781

    work page doi:10.48550/arxiv.2602.16781 2026

  69. [69]

    2026, arXiv e-prints, arXiv:2602.02484, doi: 10.48550/arXiv.2602.02484

    McDonald, W., Schaye, J., Kuijken, K., et al. 2026, arXiv e-prints, arXiv:2602.02484, doi: 10.48550/arXiv.2602.02484

    work page doi:10.48550/arxiv.2602.02484 2026

  70. [70]

    2013, The Astrophysical Journal Letters, 780, L33, doi: 10.1088/2041-8205/780/2/L33

    McQuinn, M. 2014, ApJL, 780, L33, doi: 10.1088/2041-8205/780/2/L33

    work page doi:10.1088/2041-8205/780/2/l33 2014

  71. [71]

    & Gebhardt, M

    Medlock, I., Nagai, D., Angl´ es-Alc´ azar, D., & Gebhardt, M. 2025, ApJ, 983, 46, doi: 10.3847/1538-4357/adbc9c M´ enard, B., Scranton, R., Fukugita, M., & Richards, G. 2010, MNRAS, 405, 1025, doi: 10.1111/j.1365-2966.2010.16486.x

    work page doi:10.3847/1538-4357/adbc9c 2025

  72. [72]

    , keywords =

    Mernier, F., Biffi, V., Yamaguchi, H., et al. 2018, SSRv, 214, 129, doi: 10.1007/s11214-018-0565-7

    work page doi:10.1007/s11214-018-0565-7 2018

  73. [73]

    P., Naab, T., & White, S

    Moster, B. P., Naab, T., & White, S. D. M. 2013, MNRAS, 428, 3121, doi: 10.1093/mnras/sts261 M¨ unchmeyer, M., Madhavacheril, M. S., Ferraro, S.,

    work page doi:10.1093/mnras/sts261 2013

  74. [74]

    C., & Smith, K

    Johnson, M. C., & Smith, K. M. 2019, PhRvD, 100, 083508, doi: 10.1103/PhysRevD.100.083508

    work page doi:10.1103/physrevd.100.083508 2019

  75. [75]

    J., Alonso, D., Bilicki, M., et al

    Nakoneczny, S. J., Alonso, D., Bilicki, M., et al. 2024, A&A, 681, A105, doi: 10.1051/0004-6361/202347728

    work page doi:10.1051/0004-6361/202347728 2024

  76. [76]

    Hummels, C. B. 2024, ApJL, 972, L26, doi: 10.3847/2041-8213/ad725b

    work page doi:10.3847/2041-8213/ad725b 2024

  77. [77]

    2023, MNRAS, 519, 2069, doi: 10.1093/mnras/stac3669

    Osato, K., & Nagai, D. 2023, MNRAS, 519, 2069, doi: 10.1093/mnras/stac3669

    work page doi:10.1093/mnras/stac3669 2023

  78. [78]

    2024, MNRAS, 528, 5008, doi: 10.1093/mnras/stae342

    Otsuki, M., & Hirashita, H. 2024, MNRAS, 528, 5008, doi: 10.1093/mnras/stae342

    work page doi:10.1093/mnras/stae342 2024

  79. [79]

    Pandey, J

    Pandey, S., Hill, J. C., Alarcon, A., et al. 2025, arXiv e-prints, arXiv:2506.07432, doi: 10.48550/arXiv.2506.07432 24Sharma et al

    work page doi:10.48550/arxiv.2506.07432 2025

  80. [80]

    S., Main, R

    Patil, S. S., Main, R. A., Fonseca, E., et al. 2026, ApJL, 997, L5, doi: 10.3847/2041-8213/ae2eb3 P´ eroux, C., & Howk, J. C. 2020, ARA&A, 58, 363, doi: 10.1146/annurev-astro-021820-120014

    work page doi:10.3847/2041-8213/ae2eb3 2026

Showing first 80 references.