Pith. sign in

REVIEW 3 major objections 4 minor 54 references

Probing Cosmic Magnetism with Rotation Measure-Squared-Galaxy Cross-Correlations

T0 review · 3 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper proposes that cross-correlating the squared Faraday rotation measure, RM², with the foreground galaxy density isolates cosmic magnetic fields near the galaxies' redshifts, and forecasts that the signal is measurable with current

desk verdict The RM²-galaxy statistic is genuinely new and the derivation is clean, but the detectability forecasts lean on a low-resolution simulation run and a fitted fudge factor, so the headline SNRs should be treated as provisional. read the letter →

arxiv 2512.06584 v2 pith:4UNU34JT submitted 2025-12-06 astro-ph.CO

Zekai Zhang , Adam Lidz This is my paper · ORCID
classification astro-ph.CO
keywords FaradayrotationmeasurecosmicmagneticfieldsRM-squaredgalaxycross-correlationtomographybispectrumfieldevolutionprojectedestimatorkineticSunyaev-Zel'dovicheffect
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper introduces a new statistic, the cross-correlation between the squared Faraday rotation measure (RM²) of background radio sources and the projected density of foreground galaxies, and argues that it cleanly extracts the magnetic field contribution arising at the redshifts of those galaxies. This matters because direct RM–galaxy correlations cancel out—magnetic fields can point toward or away from us—while the absolute-value variant is biased by measurement noise. The RM²-based estimator is noise-free and can be written as a bispectrum of two electron-density–weighted magnetic fields and one galaxy overdensity, so it can be modeled and tomographically binned in galaxy redshift. Using cosmological magnetohydrodynamic simulations, the paper finds the signal grows by roughly three orders of magnitude from redshift 3 to 0 and forecasts high-significance detections with upcoming radio and galaxy surveys.

What carries the argument

The central object is the squared rotation measure field A = RM² and its two-point cross-correlation with the projected galaxy density, ⟨RM²×g⟩, built in direct analogy to the 'projected fields' estimator used for the kinetic Sunyaev-Zel'dovich effect. The paper shows ⟨RM²×g⟩ equals a line-of-sight projection of a bispectrum of two copies of the electron-density–weighted line-of-sight magnetic field and one galaxy overdensity, and introduces a heuristic approximation (involving a simulation-calibrated factor K) in which the correlation's shape is the electron–galaxy cross-correlation and its amplitude is the projected power of the electron-density–weighted magnetic field. That approximation

What would settle it

Measure ⟨RM²×g⟩ at z≈0.1 using a few thousand background RMs and a wide-area galaxy catalog; the paper forecasts signal-to-noise around 4 for such a sample, so a null detection at that level would indicate the simulated amplitude or the statistic's assumptions need revision.

Watch

Extended reading notes

Core claim

The central claim is that ⟨RM²×g⟩ measures the line-of-sight integral of the electron-density–weighted magnetic field produced near foreground galaxy redshifts, free of contamination from Milky Way, source-intrinsic, and other line-of-sight RM contributions. The statistic is related to a bispectrum involving two copies of the electron-density–weighted magnetic field and one galaxy overdensity; in the paper's approximate model the shape of the correlation is set by the electron–galaxy two-point function while the amplitude is set by the projected power of the electron-weighted line-of-sight magnetic field. In the simulations the effective field strength is dominated by the inner regions of ha

Load-bearing premise

The predicted signal amplitude and detection significance depend on the magnetic field statistics of the simulations being representative; the paper itself shows that higher-resolution runs—which resolve more magnetic field reversals—produce a weaker projected signal, so the forecasts could be optimistic if the real field is as tangled as those runs suggest.

Editorial extensions

If this is right

  • The RM²–galaxy estimator is immune to the noise bias that suppresses |RM|–galaxy correlations, because RM² noise terms are uncorrelated with the galaxy field.
  • Splitting the foreground galaxies into redshift bins turns the statistic into a tomographic probe of the electron-density–weighted magnetic field strength across cosmic time.
  • With ~10⁷ RM measurements (as expected from future radio surveys) and ~10⁶ galaxies per redshift bin, the signal is forecast to be detectable at high significance over roughly 0.5–50 Mpc/h scales and redshifts up to z≈1.
  • Even with current-size samples (~10³ RMs), the paper forecasts a marginal detection at low redshift, making pilot measurements worthwhile now.
  • The shape of the correlation largely follows the electron–galaxy clustering, so it can be calibrated by other probes such as fast radio burst dispersion measure and kSZ cross-correlations, leaving the amplitude as a cleaner measure of magnetic field strength.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the statistic works as claimed, the redshift-binned amplitude provides a measurement of cosmic magnetic energy density evolution that does not require assuming a particular magnetogenesis scenario, since the electron-density weighting can be calibrated separately.
  • The strong dependence of the signal on the RM smoothing scale suggests that denser future RM catalogs could probe the small-scale cutoff of the magnetic field power spectrum; conversely, a detection at a given smoothing scale carries information about field coherence length.
  • Comparing RM² and |RM| estimators on the same data would isolate the non-Gaussian tail of the electron-density–weighted field, since RM² weights rare high-RM pixels quadratically while |RM| weights them linearly.
  • A natural extension is to cross-correlate RM² with galaxy lensing or with higher-order galaxy statistics to separate the halo-mass dependence of magnetic field amplification from the pure redshift evolution.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a new estimator for extragalactic magnetic fields: the cross-correlation between the squared Faraday rotation measure, RM^2, toward background sources and the projected galaxy overdensity, ⟨RM^2 × g⟩. The authors derive its relation to a bispectrum involving two electron-density–weighted line-of-sight magnetic field factors and one galaxy overdensity (Eq. 2.6), work in the flat-sky/Limber approximations, and develop an approximate analytic model in which the scale dependence follows the electron–galaxy cross-power and the amplitude is set by an integral over the electron-density–weighted magnetic field power spectrum (Eq. 3.3). They calibrate a multiplicative factor K=3 against Illustris-TNG TNG300-3 simulations, check the smoothing-scale dependence, quantify the noise bias of ⟨|RM| × g⟩, and make forecasts for current and SKA-era RM catalogs. The central claimed result is that ⟨RM^2 × g⟩ avoids the sign cancellation and noise bias of |RM|-based estimators and can tomographically probe the redshift evolution of cosmic magnetic fields.

Significance. If the simulation-calibrated amplitude is correct, the proposed estimator is a genuinely new and potentially powerful probe: it is immune to zero-mean RM noise bias in the average signal, it has a cleaner connection to the underlying bispectrum than |RM| correlators, and it offers a tomographic redshift decomposition by binning foreground galaxies. The derivation in Appendix A is a useful contribution, and the comparison with TNG simulations is a reasonable first modeling step. However, the quantitative forecasting rests on an analytic model with an explicit heuristic replacement of P_B by P_tildeB and a fitted factor K, and the calibration is performed on the lowest-resolution TNG300 run. Since Appendix C shows the signal decreases substantially with resolution, the predicted detection significances are not yet robust. The conceptual method is valuable, but the numerical forecasts need to be either re-derived at converged resolution or presented with a much larger systematic uncertainty.

major comments (3)
  1. [§4.2, Fig. 9, and Appendix C] The forecast amplitudes are not converged with simulation resolution. The forecasts use TNG300-3, which has 64× fewer resolution elements than TNG300-1. Appendix C, Fig. 11, shows that w_RM2,g at z=0 decreases by roughly a factor of 2–4 as resolution increases, attributed to better-resolved magnetic field reversals cancelling along the line of sight. Since all SNR estimates in §4.2 scale linearly with signal amplitude, the headline numbers (SNR 36.5 for 10^5 RMs, 3.9 for the NVSS-like sample at z=0.1) would drop to roughly 9–18 and 1–2 if the TNG300-1 amplitude is closer to the true signal. The statement in Appendix C that the main conclusions are robust is therefore contradicted by the figure it accompanies. The authors should either rerun the forecasts with TNG300-1-calibrated amplitudes, or explicitly marginalize over the resolution uncertainty and soften the detectability claims.
  2. [§3.2, Eq. (3.2)–(3.3), and Appendix A.3] The analytic model used to interpret the simulations and to extrapolate forecasts to scales beyond the simulation box is built on an unproven replacement of P_B by P_tildeB and a calibration factor K=3 fitted to the same TNG300-3 simulations. The paper itself describes this as a “heuristic step which we do not attempt to rigorously justify.” Since Eq. (3.3) is then used in §4 to extend the correlation function to r⊥ > 20 Mpc/h and to predict SNRs, the systematic error in K and in the P_B→P_tildeB replacement propagates directly into all detectability claims. A comparison against TNG300-1 or another MHD simulation is needed to establish that K is not resolution-dependent; alternatively, the forecasts should be presented as conditional on the TNG300-3 calibration, with the calibration uncertainty included in the error budget.
  3. [§4.2 and Appendix B] The forecast covariance assumes that both the RM field and the galaxy field are noise-dominated and that the error bars in different radial bins are uncorrelated. The paper acknowledges the neglect of sample variance, but the resulting error formula, Eq. (4.2), is used to claim high-significance detections. Given that the signal itself is only calibrated to a single simulation at a single resolution, the forecast SNR values should be interpreted as upper limits modulo these simplifications. At minimum, the authors should state explicitly how much of the claimed SNR comes from the assumed noise-dominated covariance and how much could be affected by sample variance on the scales where the signal is actually measured.
minor comments (4)
  1. [Appendix A.1] Typo: “notoation” should be “notation” in the sentence introducing Eq. (A.3).
  2. [Appendix B] The text says “Our general aim is to calculate…” and later “in our forecasts (§4.3)”; the forecasts of the RM^2 statistic are in §4.2, while §4.3 concerns |RM|. Please correct the cross-reference.
  3. [Fig. 11 caption] The caption states that “the main conclusions are robust to simulation resolution,” but the figure itself shows a factor–2–4 decrease in amplitude. Either soften the claim or provide a quantitative convergence test, e.g., on the total SNR under TNG300-1.
  4. [§3.4] The discussion of smoothing is helpful, but the comparison between the 3D mesh smoothing and the angular smoothing of real RM catalogs is only qualitative. Since the signal depends so strongly on the smoothing scale, a more explicit treatment of the mapping between 3D voxel size and effective angular beam would improve the paper's readiness for data application.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the RM²×g statistic is derived self-containedly and the simulation-calibrated model is transparent, not passed off as an independent first-principles prediction.

full rationale

The central derivation (Eqs. 2.6-2.10) follows from the definition of RM (Eq. 2.2) using Fourier convolution, the Limber approximation, and a bispectrum decomposition; none of the inputs (ne, B∥, δg, Wg) already contains w_RM2,g, so the relation is not self-definitional. The approximate analytic model in Eq. 3.3 is explicitly presented as heuristic: Section 3.2 states that the replacement PB → PtildeB 'includes a heuristic step which we do not attempt to rigorously justify,' and K is described as 'a redshift-independent calibration factor determined from the simulations,' with K=3 fitted on intermediate scales. Using this calibrated formula to extrapolate forecasts is a standard, conditional simulation-based forecast rather than a hidden reduction: the fitted constant K does not by itself determine the large-scale signal, which also uses simulation-measured PtildeB and Pe,g and independent survey parameters (N_RM, N_gal, σtot). No load-bearing self-citations appear; the kSZ/projected-fields analogy cites external work (Doré et al. 2004; Hill et al. 2016; Ferraro et al. 2016). Appendix C's resolution dependence—the amplitude decreasing from TNG300-3 to TNG300-1—is an admitted convergence/robustness caveat, not a circularity: it affects the accuracy of the forecast amplitude but does not make any derived quantity equal to an input by construction. The paper's own limitations, including the unproven PB→PtildeB substitution and the need to constrain w_e,g independently via FRB/kSZ measurements, further confirm that the analysis is model-dependent but not circular.

Assumptions & free parameters 3 free parameters · 8 assumptions · 0 invented entities

The paper introduces no new physical entity. Its quantitative predictions rest on two fitted quantities (K and b_e b_g), a hand-chosen smoothing scale, and a series of domain assumptions about noise, halo-galaxy biasing, and the fidelity of Illustris-TNG. The analytic model is calibrated to the simulations it is used to interpret, so the amplitude is not an externally grounded prediction.

free parameters (3)
  • K (calibration factor) = 3
    Redshift-independent factor introduced in Eq. 3.2 / A.20 and 'adjusted to match the simulation results' on intermediate scales around 1-10 Mpc/h; described in Appendix A.3 as 'essentially a fudge factor'.
  • b_e b_g (linear biasing amplitude) = 0.72 at z=0; 2 at z=3 (varies with redshift)
    Used in Section 3.2 to extrapolate the electron-halo cross-power as P_e,g(k)=b_e b_g P_lin(k), with the product 'adjusted to match the simulation results at low k'.
  • 3D mesh voxel size = 0.41 Mpc/h
    Hand-chosen gridding scale that sets the effective RM smoothing; Section 3.4 shows the signal changes by orders of magnitude with smoothing scale, so the mesh choice is a modeling assumption that affects the amplitude.
assumptions (8)
  • domain assumption Flat-sky and Limber approximations are valid for the angular scales and redshifts considered.
    Adopted throughout the derivation, stated in Section 2.2 and Appendix A; these are standard large-scale-structure approximations but not exact on small scales.
  • standard math The magnetic field is divergence-free and hence transverse (B_los(k) is the projection of a transverse field).
    Used in Eq. A.4-A.6 and A.15 to write the bispectrum and power spectrum; standard MHD/physics for magnetic fields.
  • domain assumption Instrumental, intrinsic-source, and residual Milky Way RM noise are zero-mean and uncorrelated with the foreground galaxy overdensity.
    This is the basis for claiming that RM²×g is unbiased by noise (Section 4.1, 4.3) and that these terms only contribute to the variance.
  • domain assumption The Illustris-TNG simulation provides a sufficiently faithful model of cosmic magnetic fields, electron density, and halo clustering for the signal and forecast amplitudes.
    All signal amplitudes and forecasts inherit this; Appendix C partially tests resolution dependence but cannot test the underlying MHD model.
  • ad hoc to paper The electron-density-weighted magnetic field power spectrum can be substituted for the bare magnetic field power spectrum in the triangle power spectrum, with a fitted factor K.
    Eq. 3.2 / A.20; the paper explicitly says this replacement 'lacks rigorous justification' and is a heuristic step.
  • ad hoc to paper Gaussian field approximation and neglect of B-δe-δg correlations at leading order in the bispectrum expansion.
    Appendix A.3 assumes Gaussian fields and neglects cross-correlations between B, δe, and δg to derive the leading-order triangle power spectrum.
  • domain assumption Halos trace galaxies on large scales with a linear biasing relation, so RM²-halo cross-correlations proxy for RM²-galaxy signals up to an overall amplitude.
    Stated in Section 2.3; the paper notes the halo-based signal may underestimate galaxy-based correlations by a factor ~2.
  • domain assumption Noise-dominated limit and shot-noise-dominated galaxy field for the forecast covariance.
    Appendix B derives the diagonal error formula under approximations N_A,A >> C_A,A and 1/n_g >> C_g,g; the paper acknowledges this is imperfect at high galaxy number density.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Probing Cosmic Magnetism with Rotation Measure-Squared-Galaxy Cross-Correlations." pith.science (2026). https://pith.science/paper/4UNU34JT

@misc{pith2026251206584,
  author       = {Pith},
  title        = {Pith review of: Probing Cosmic Magnetism with Rotation Measure-Squared-Galaxy Cross-Correlations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4UNU34JT}},
  note         = {Machine review of arXiv:2512.06584}
}
abstract

We present a new approach for extracting information about cosmic magnetic fields using cross-correlations between extragalactic Faraday rotation measure (RM) catalogs and galaxy surveys. Specifically, we propose measuring the two-point cross-correlation between RM squared, ${\rm RM}^2$, towards background sources and the projected density field of foreground galaxies, $\langle {\rm RM}^2 \times {\rm g} \rangle$, as a function of transverse separation. This statistic is analogous to the ''projected fields'' estimator used for the kinetic Sunyaev-Zel'dovich (kSZ) effect, $\langle {\rm kSZ}^2 \times {\rm g} \rangle$. Our estimator avoids contamination, and is also free from the noise bias that arises when correlating the absolute value of the RMs with galaxies. Moreover, by binning in foreground galaxy redshifts, $\langle {\rm RM}^2 \times {\rm g} \rangle$ enables a tomographic reconstruction of the redshift evolution of large-scale cosmic magnetic fields. We model this statistic using the Illustris-TNG cosmological magnetohydrodynamic simulations and compare with approximate analytic predictions. We show that $\langle {\rm RM}^2 \times {\rm g} \rangle$ can be related to a bispectrum involving two copies of the electron-density--weighted magnetic field strength and one of the galaxy overdensity. In Illustris-TNG, the effective field strength is primarily set by the magnetic field amplitudes within the inner regions of galaxy-hosting dark matter halos. It increases towards low redshift, driven by dynamo amplification and magnetized outflows. Our forecasts suggest that $\langle {\rm RM}^2 \times {\rm g} \rangle$ is detectable at high significance with current galaxy surveys and future RM catalogs from the SKA, offering a tomographic probe of large-scale magnetic fields across cosmic time.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 1 canonical work pages

  1. [1]

    Subramanian,The origin, evolution and signatures of primordial magnetic fields,Rept

    K. Subramanian,The origin, evolution and signatures of primordial magnetic fields,Rept. Prog. Phys.79(2016) 076901 [1504.02311]

    arXiv 2016

  2. [2]

    Beck,Galactic and extragalactic magnetic fields – a concise review,Astrophysics and Space Sciences Transactions5(2009) 43

    R. Beck,Galactic and extragalactic magnetic fields – a concise review,Astrophysics and Space Sciences Transactions5(2009) 43

    2009

  3. [3]

    Widrow,Origin of galactic and extragalactic magnetic fields,Rev

    L.M. Widrow,Origin of galactic and extragalactic magnetic fields,Rev. Mod. Phys.74(2002) 775

    2002

  4. [4]

    Taylor, I

    A.M. Taylor, I. Vovk and A. Neronov,Extragalactic magnetic fields constraints from simultaneous GeV-TeV observations of blazars,Astron. Astrophys.529(2011) A144 [1101.0932]

    arXiv 2011

  5. [5]

    Rybicki and A.P

    G.B. Rybicki and A.P. Lightman,Radiative Processes in Astrophysics(1986)

    1986

  6. [6]

    Haarlem, M.W

    M.P.v. Haarlem, M.W. Wise, A.W. Gunst, G. Heald, J.P. McKean, J.W.T. Hessels et al., LOFAR: The LOw-Frequency ARray,A&A556(2013) A2

    2013

  7. [7]

    O’Sullivan, T.W

    S.P. O’Sullivan, T.W. Shimwell, M.J. Hardcastle, C. Tasse, G. Heald, E. Carretti et al.,The Faraday Rotation Measure Grid of the LOFAR Two-metre Sky Survey: Data Release 2, Monthly Notices of the Royal Astronomical Society519(2023) 5723

    2023

  8. [8]

    Gaensler, R

    B.M. Gaensler, R. Beck and L. Feretti,The origin and evolution of cosmic magnetism,New Astronomy Reviews48(2004) 1003

    2004

Show all 54 references
  1. [9]

    Gaensler,Cosmic Magnetism with the Square Kilometre Array and its Pathfinders,Proc

    B.M. Gaensler,Cosmic Magnetism with the Square Kilometre Array and its Pathfinders,Proc. IAU4(2008) 645

    2008

  2. [10]

    Lee, U.-L

    J. Lee, U.-L. Pen, A.R. Taylor, J.M. Stil and C. Sunstrum,Detection of Large-Scale Cosmic Magnetic Fields, July, 2010. 10.48550/arXiv.0906.1631

    2010 doi arXiv

  3. [11]

    Stasyszyn, S.E

    F. Stasyszyn, S.E. Nuza, K. Dolag, R. Beck and J. Donnert,Measuring cosmic magnetic fields by rotation measure–galaxy cross-correlations in cosmological simulations,Monthly Notices of the Royal Astronomical Society408(2010) 684

    2010

  4. [12]

    Amaral, T

    A. Amaral, T. Vernstrom and B.M. Gaensler,Constraints on Large-Scale Magnetic Fields in the Intergalactic Medium Using Cross-Correlation Methods,Monthly Notices of the Royal Astronomical Society503(2021) 2913

    2021

  5. [13]

    Kolatt,Determination of the primordial magnetic field power spectrum by Faraday rotation correlations,Astrophys

    T. Kolatt,Determination of the primordial magnetic field power spectrum by Faraday rotation correlations,Astrophys. J.495(1998) 564 [astro-ph/9704243]

    1998 arXiv

  6. [14]

    Dore, J.F

    O. Dore, J.F. Hennawi and D.N. Spergel,Beyond the damping tail: Cross - correlating the kinetic Sunyaev - Zel’dovich effect with cosmic shear,Astrophys. J.606(2004) 46 [astro-ph/0309337]

    2004 arXiv

  7. [15]

    J.C. Hill, S. Ferraro, N. Battaglia, J. Liu and D.N. Spergel,Kinematic Sunyaev-Zel’dovich Effect with Projected Fields: A Novel Probe of the Baryon Distribution with Planck, WMAP, and WISE Data,Phys. Rev. Lett.117(2016) 051301 [1603.01608]

    2016 arXiv

  8. [16]

    Ferraro, J.C

    S. Ferraro, J.C. Hill, N. Battaglia, J. Liu and D.N. Spergel,Kinematic Sunyaev-Zel’dovich effect with projected fields. II. Prospects, challenges, and comparison with simulations,Phys. Rev. D94(2016) 123526 [1605.02722]

    2016 arXiv

  9. [17]

    Dolag and F.A

    K. Dolag and F.A. Stasyszyn,An MHD Gadget for cosmological simulations,Mon. Not. Roy. Astron. Soc.398(2009) 1678 [0807.3553]

    2009 arXiv

  10. [18]

    Pakmor, A

    R. Pakmor, A. Bauer and V. Springel,Magnetohydrodynamics on an unstructured moving grid, Monthly Notices of the Royal Astronomical Society418(2011) 1392

    2011

  11. [19]

    Vazza, M

    F. Vazza, M. Brüggen, C. Gheller and P. Wang,On the amplification of magnetic fields in cosmic filaments and galaxy clusters, MNRAS445(2014) 3706 [1409.2640]. – 32 –

    2014 arXiv

  12. [20]

    Pillepich, D

    A. Pillepich, D. Nelson, L. Hernquist, V. Springel, R. Pakmor, P. Torrey et al.,First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies, Monthly Notices of the Royal Astronomical Society475(2018) 648

    2018

  13. [21]

    Marinacci, M

    F. Marinacci, M. Vogelsberger, R. Pakmor, P. Torrey, V. Springel, L. Hernquist et al.,First results from the IllustrisTNG simulations: radio haloes and magnetic fields,Monthly Notices of the Royal Astronomical Society(2018)

    2018

  14. [22]

    Garaldi, R

    E. Garaldi, R. Pakmor and V. Springel,Magnetogenesis around the first galaxies: the impact of different field seeding processes on galaxy formation, MNRAS502(2021) 5726 [2010.09729]

    2021 arXiv

  15. [23]

    Weinberger, V

    R. Weinberger, V. Springel, L. Hernquist, A. Pillepich, F. Marinacci, R. Pakmor et al., Simulating galaxy formation with black hole driven thermal and kinetic feedback, MNRAS465 (2017) 3291 [1607.03486]

    2017 arXiv

  16. [24]

    Pillepich, V

    A. Pillepich, V. Springel, D. Nelson, S. Genel, J. Naiman, R. Pakmor et al.,Simulating galaxy formation with the IllustrisTNG model, MNRAS473(2018) 4077 [1703.02970]

    2018 arXiv

  17. [25]

    Nelson, A

    D. Nelson, A. Pillepich, V. Springel, R. Pakmor, R. Weinberger, S. Genel et al.,First results from the TNG50 simulation: galactic outflows driven by supernovae and black hole feedback, Monthly Notices of the Royal Astronomical Society490(2019) 3234

    2019

  18. [26]

    Nelson, A

    D. Nelson, A. Pillepich, V. Springel, R. Weinberger, L. Hernquist, R. Pakmor et al.,First results from the IllustrisTNG simulations: the galaxy colour bimodality,Monthly Notices of the Royal Astronomical Society475(2018) 624

    2018

  19. [27]

    Springel, R

    V. Springel, R. Pakmor, A. Pillepich, R. Weinberger, D. Nelson, L. Hernquist et al.,First results from the IllustrisTNG simulations: matter and galaxy clustering,Monthly Notices of the Royal Astronomical Society475(2018) 676

    2018

  20. [28]

    Naiman, A

    J.P. Naiman, A. Pillepich, V. Springel, E. Ramirez-Ruiz, P. Torrey, M. Vogelsberger et al., First results from the IllustrisTNG simulations: a tale of two elements - chemical evolution of magnesium and europium,Monthly Notices of the Royal Astronomical Society477(2018) 1206

    2018

  21. [29]

    Arámburo-García, K

    A. Arámburo-García, K. Bondarenko, A. Boyarsky, D. Nelson, A. Pillepich and A. Sokolenko, Magnetization of the intergalactic medium in the IllustrisTNG simulations: the importance of extended, outflow-driven bubbles,Monthly Notices of the Royal Astronomical Society505 (2021) 5038

    2021

  22. [30]

    Sinha and L.H

    M. Sinha and L.H. Garrison,Corrfunc — A Suite of Blazing Fast Correlation Functions on the CPU,Monthly Notices of the Royal Astronomical Society491(2020) 3022

    2020

  23. [31]

    Eisenstein and W

    D.J. Eisenstein and W. Hu,Baryonic features in the matter transfer function,The Astrophysical Journal496(1998) 605–614

    1998

  24. [32]

    Diemer,COLOSSUS: A Python Toolkit for Cosmology, Large-scale Structure, and Dark Matter Halos,ApJS239(2018) 35

    B. Diemer,COLOSSUS: A Python Toolkit for Cosmology, Large-scale Structure, and Dark Matter Halos,ApJS239(2018) 35

    2018

  25. [33]

    Leung, S

    C. Leung, S. Simha, I. Medlock, D. Nagai, K.W. Masui, L.A. Kahinga et al.,Stellar Mass–Dispersion Measure Correlations Constrain Baryonic Feedback in Fast Radio Burst Host Galaxies, ApJ991(2025) L25 [2507.16816]

    2025 arXiv

  26. [34]

    Madhavacheril, N

    M.S. Madhavacheril, N. Battaglia, K.M. Smith and J.L. Sievers,Cosmology with the kinematic Sunyaev-Zeldovich effect: Breaking the optical depth degeneracy with fast radio bursts, Phys. Rev. D100(2019) 103532 [1901.02418]

    2019 arXiv

  27. [35]

    McQuinn,Locating the “Missing” Baryons with Extragalactic Dispersion Measure Estimates, ApJ780(2014) L33 [1309.4451]

    M. McQuinn,Locating the “Missing” Baryons with Extragalactic Dispersion Measure Estimates, ApJ780(2014) L33 [1309.4451]

    2014 arXiv

  28. [36]

    Hadzhiyska, S

    B. Hadzhiyska, S. Ferraro, B. Ried Guachalla, E. Schaan, J. Aguilar, S. Ahlen et al.,Evidence for large baryonic feedback at low and intermediate redshifts from kinematic Sunyaev-Zel’dovich observations with ACT and DESI photometric galaxies, Phys. Rev. D112(2025) 083509 [2407...

    2025 arXiv

  29. [37]

    Marinacci, M

    F. Marinacci, M. Vogelsberger, P. Mocz and R. Pakmor,The large-scale properties of simulated cosmological magnetic fields,Monthly Notices of the Royal Astronomical Society453(2015) 3999

    2015

  30. [38]

    Arámburo-García, K

    A. Arámburo-García, K. Bondarenko, A. Boyarsky, A. Neronov, A. Scaife and A. Sokolenko, Revision of Faraday rotation measure constraints on the primordial magnetic field using the IllustrisTNG simulation,Monthly Notices of the Royal Astronomical Society515(2022) 5673

    2022

  31. [39]

    Taylor, J.M

    A.R. Taylor, J.M. Stil and C. Sunstrum,A Rotation Measure Image of the Sky,The Astrophysical Journal702(2009) 1230

    2009

  32. [40]

    Condon, W.D

    J.J. Condon, W.D. Cotton, E.W. Greisen, Q.F. Yin, R.A. Perley, G.B. Taylor et al.,The NRAO VLA Sky Survey,AJ115(1998) 1693

    1998

  33. [41]

    Hammond, T

    A.M. Hammond, T. Robishaw and B.M. Gaensler,A New Catalog of Faraday Rotation Measures and Redshifts for Extragalactic Radio Sources, May, 2013. 10.48550/arXiv.1209.1438

    2013 doi arXiv

  34. [42]

    Anderson, N.M

    C.S. Anderson, N.M. McClure-Griffiths, L. Rudnick, B.M. Gaensler, S.P. O’Sullivan, S. Bradbury et al.,Probing the magnetized gas distribution in galaxy groups and the cosmic web with POSSUM Faraday rotation measures,Monthly Notices of the Royal Astronomical Society 533(2024) 4068

    2024

  35. [43]

    Bilicki, J.A

    M. Bilicki, J.A. Peacock, T.H. Jarrett, M.E. Cluver, N. Maddox, M.J.I. Brown et al.,WISE× SuperCOSMOS PHOTOMETRIC REDSHIFT CATALOG: 20 MILLION GALAXIES OVER 3πSTERADIANS,ApJS225(2016) 5

    2016

  36. [44]

    Adame, J

    A.G. Adame, J. Aguilar, S. Ahlen, S. Alam, D.M. Alexander, M. Alvarez et al.,DESI 2024 II: sample definitions, characteristics, and two-point clustering statistics, J. Cosmology Astropart. Phys.2025(2025) 017 [2411.12020]

    2024 arXiv

  37. [45]

    E.-j. Kim, A. Olinto and R. Rosner,Generation of density perturbations by primordial magnetic fields,Astrophys. J.468(1996) 28 [astro-ph/9412070]

    1996 arXiv

  38. [46]

    Adi, H.A.G

    T. Adi, H.A.G. Cruz and M. Kamionkowski,Primordial density perturbations from magnetic fields,Phys. Rev. D108(2023) 023521 [2306.11319]

    2023 arXiv

  39. [47]

    Cohn,Power spectrum and correlation function errors: Poisson vs

    J.D. Cohn,Power spectrum and correlation function errors: Poisson vs. Gaussian shot noise, New Astron.11(2006) 226 [astro-ph/0503285]

    2006 arXiv

  40. [48]

    Robitaille, E.J

    Astropy Collaboration, T.P. Robitaille, E.J. Tollerud, P. Greenfield, M. Droettboom, E. Bray et al.,Astropy: A community Python package for astronomy,Astronomy and Astrophysics558 (2013) A33

    2013

  41. [49]

    Price-Whelan, B.M

    Astropy Collaboration, A.M. Price-Whelan, B.M. Sipőcz, H.M. Günther, P.L. Lim, S.M. Crawford et al.,The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package,The Astronomical Journal156(2018) 123

    2018

  42. [50]

    Price-Whelan, P.L

    Astropy Collaboration, A.M. Price-Whelan, P.L. Lim, N. Earl, N. Starkman, L. Bradley et al., The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package,The Astrophysical Journal935(2022) 167

    2022

  43. [51]

    Harris, K.J

    C.R. Harris, K.J. Millman, S.J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau et al., Array programming with NumPy,Nature585(2020) 357

    2020

  44. [52]

    Virtanen, R

    P. Virtanen, R. Gommers, T.E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau et al., SciPy 1.0: fundamental algorithms for scientific computing in Python,Nat Methods17(2020) 261

    2020

  45. [53]

    Hunter,Matplotlib: A 2D Graphics Environment,Computing in Science & Engineering9 (2007) 90

    J.D. Hunter,Matplotlib: A 2D Graphics Environment,Computing in Science & Engineering9 (2007) 90. – 34 –

    2007

  46. [54]

    N. Hand, Y. Feng, F. Beutler, Y. Li, C. Modi, U. Seljak et al.,nbodykit: an open-source, massively parallel toolkit for large-scale structure,AJ156(2018) 160. – 35 –

    2018

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.