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On the nature of LOFAR RMs and new constraints on magnetic fields in cosmic filaments and on magnetogenesis scenarios

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read LOFAR rotation measures, cleaned of Galactic contamination, show filament magnetic fields of 11–15 nG that grew as (1+z)^{2.3–2.6}, favouring primordial magnetogenesis.

desk verdict Solid, transparent extension of Paper II whose headline field values rest on an uncalibrated astrophysical RRM shape; worth refereeing, but treat the central numbers as provisional. read the letter →

arxiv 2411.13499 v1 pith:MYREU7JD submitted 2024-11-20 astro-ph.CO astro-ph.GA

E. Carretti , F. Vazza , S. P. O'Sullivan , V. Vacca , A. Bonafede , G. Heald , C. Horellou , S. Mtchedlidze
show 1 more author
T. Vernstrom
This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords cosmicwebfilamentsintergalacticmagneticfieldsFaradayrotationmeasuremagnetogenesisLOFARradiopolarizationlarge-scalestructurecircumgalacticmedium
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

This paper tries to determine the strength and redshift evolution of magnetic fields in the cosmic web's filaments, and through them the origin of cosmic magnetism. Using Faraday rotation measures of 653 low-Galactic-contamination background sources at 144 MHz, it argues that residual RMs are dominated by filaments and that their dispersion grows steeply with redshift. The resulting field at z=0 is 11–15 ± 4 nG, scaling as (1+z)^{2.3–2.6 ± 0.5}, which means the comoving field is roughly constant — a steeper evolution than earlier analyses found. The data favour primordial magnetogenesis over fields injected later by galaxies and AGNs. The paper also quantifies the contaminating cluster and galaxy-halo contribution at about 21% and proposes that background-source polarization fraction can trace the circumgalactic medium.

What carries the argument

The central quantity is the residual rotation measure, $\mathrm{RRM} = \mathrm{RM} - \mathrm{GRM}$, obtained after subtracting a Galactic RM map and keeping only sources with $|\mathrm{GRM}|<14\,\mathrm{rad\,m^{-2}}$, which removes most Milky Way contamination. The fitted model is $\sqrt{\langle\mathrm{RRM}^2\rangle} = A_{\rm rrm}/(1+z)^2 + \sqrt{\langle\mathrm{RRM}_f^2\rangle}$, where the filament term is $\mathrm{RRM}_f = 0.812\int n_e B_\parallel (1+z)^{-2}\,dl$ and the filament field is assumed to follow $B_f = B_{f,0}(1+z)^\alpha$, with comoving slope $\beta=\alpha-2$. The filament term is evaluated along 100 mock lines of sight through magneto-hydrodynamic cosmological simulations of each magnetogenesis scenario, with dense cluster regions excised by a density-contrast cutoff and 120 random field-direction realisations per line of sight. The results rest on the assumed shape of the astrophysical term $A_{\rm rrm}/(1+z)^k$; the preferred $k=1$ shape, corresponding to an astrophysical RRM that grows with redshift, is what produces the $11{-}15\,\mathrm{nG}$ and $\alpha\approx2.5$ outcome.

What would settle it

Stack the RRM of background sources behind known galaxy groups and clusters in narrow redshift bins at a frequency where the filament contribution is small, and measure how the astrophysical RM per halo changes with redshift. The paper's preferred $A_{\rm rrm}/(1+z)$ shape predicts the astrophysical RRM contribution roughly doubles from $z\approx0$ to $z\approx1$, while the $A_{\rm rrm}/(1+z)^3$ shape predicts it shrinks; a higher-frequency RM sample with spectroscopic redshifts could distinguish these and settle whether $B_{f,0}$ is near $11{-}15\,\mathrm{nG}$ or $20{-}40\,\mathrm{nG}$.

Watch

Extended reading notes

Core claim

Using the 653-source subsample of the LOFAR 144-MHz RM catalogue with $|\mathrm{GRM}|<14\,\mathrm{rad\,m^{-2}}$, the paper finds that the residual rotation-measure dispersion rises with redshift with slope $0.25\pm0.08\,\mathrm{rad\,m^{-2}}$ per unit redshift, 3$\sigma$ away from flat. A Bayesian fit of $\sqrt{\langle\mathrm{RRM}^2\rangle}=A_{\rm rrm}/(1+z)^2+\sqrt{\langle\mathrm{RRM}_f^2\rangle}$ with the filament term drawn from magneto-hydrodynamic simulations gives $B_{f,0}=11{-}15\pm4\,\mathrm{nG}$ and $\alpha=2.3{-}2.6\pm0.5$, i.e. a comoving-field slope $\beta=[0.3,0.6]\pm0.5$ consistent with no evolution. Decomposing the signal, the paper attributes about 21% of the RRM rms to galaxy clusters and galaxy CGM and the rest to cosmic filaments. Comparisons with simulations favour primordial magnetogenesis models over astrophysical injection, because primordial fields already produce significant rotation at high redshift whereas the astrophysical models flatten there. A secondary finding is that the fractional polarization of background sources may trace the CGM, with a tentative shock signature near the virial radius of massive galaxies.

Load-bearing premise

The headline field strength and redshift slope assume the contaminating signal from galaxies and clusters grows with redshift as (1+z); if it instead stays constant or shrinks, the same data give a field at z=0 of 20–40 nG with a flatter slope, and the preferred growing shape disagrees at 2–4 sigma with the separately measured 21% cluster-plus-CGM contribution.

Editorial extensions

If this is right

  • If the filament field today is 11–15 nG and grows as (1+z)^{2.3–2.6}, the comoving magnetic field is roughly constant, so the field does not dilute as the cosmic web expands.
  • A primordial origin for the filament fields would mean the Universe was magnetised before galaxy formation, and that galaxy and AGN feedback only adds a subdominant contribution.
  • Residual Galactic RM contamination, if not controlled, can hide real redshift evolution; better Galactic RM maps will sharpen or shift these measurements.
  • The roughly 21% cluster-plus-CGM fraction is separable, so future higher-frequency RM surveys can directly measure the astrophysical term that currently limits the filament-field fit.
  • Polarization fraction of background radio sources may become a practical tracer of the circumgalactic medium and of shocks at the virial radius of massive galaxies.

Reading between the lines

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

  • The 2–4 sigma tension between the preferred rising astrophysical term and the directly measured 21% cluster-plus-CGM fraction is the softest point; a dedicated measurement of per-halo RRM versus redshift would decide whether the true field is the 11–15 nG or the 20–40 nG family.
  • The wiggles in RRM rms versus redshift anticorrelate with galaxy number density on roughly 800–900 Mpc scales; if physical, they are a large-scale structure signal that the 42.5 Mpc simulation boxes cannot reproduce, and tests would need larger volumes or line-of-sight stacking.
  • Applying the same Bayesian machinery to higher-frequency RM catalogues, where the filament term is suppressed, would measure the astrophysical redshift dependence directly instead of inferring it from a fit — a testable extension of the paper's approach.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper uses the LOFAR LoTSS DR2 RM catalogue to investigate the origin of extragalactic rotation measures at 144 MHz and to constrain magnetic fields in cosmic filaments. The authors select a subsample of sources with low Galactic RM (|GRM| < 14 rad/m^2), compute the residual RM (RRM) rms as a function of redshift, and fit a model that combines an astrophysical RRM term with a filament term computed from cosmological MHD simulations. They also compare the observed RRM-redshift relation with predictions from several magnetogenesis scenarios. Based on the fits, the paper reports a filament magnetic field strength at z=0 of B_f0 = 11-15 +/- 4 nG with a redshift slope alpha = 2.3-2.6 +/- 0.5, and concludes that primordial magnetogenesis scenarios are favoured over purely astrophysical injection.

Significance. If the quoted values are robust, the paper would provide an important direct measurement of magnetic fields in cosmic filaments and a discriminator between magnetogenesis scenarios, building on earlier work by the same group. The analysis has clear strengths: the sample selection is carefully documented with multiple sanity checks for residual Galactic contamination; the origin of the RRM is investigated with several independent methods (cluster impact parameters, galaxy CGM stacking, source spectral index, and source linear size); and the comparison uses a suite of cosmological MHD simulations with parameters reported in Table 1. The paper also gives an independent estimate of the astrophysical RRM fraction (21 +/- 4 percent) from clusters and CGM, which is a useful cross-check. However, the headline numerical claims are currently tied to model choices that are not independently calibrated, and one of the central equations appears to add rms values linearly rather than in quadrature. As a result, the significance as a measurement of B_f0 and alpha is not yet established in the present form.

major comments (4)
  1. [Sect. 5.1, Eq. (8)] Equation (8) models the total RRM rms as the linear sum of the astrophysical term A_rrm/(1+z)^2 and the filament term <RRM_f^2>^{1/2}. If the astrophysical and filament contributions are independent, their variances add, so the total rms should be the quadrature sum sqrt( A_rrm^2/(1+z)^4 + <RRM_f^2> ). The linear form is exact only for perfectly correlated components, which is not the case for unrelated foreground objects and filaments. Because this equation is the basis for all fits in Tables 3-5 and B.1, the reported B_f0 and alpha values are affected; the inferred filament amplitude and the tension with the independent astrophysical fraction would both change. Please justify the linear addition or correct it and re-run the fits.
  2. [Sect. 5.1, Tables 3-5; Sect. 6.2] The headline values B_f0 = 11-15 nG and alpha = 2.3-2.6 are obtained only for the A_rrm/(1+z) shape of the astrophysical component (Table 5). With the A_rrm/(1+z)^2 and A_rrm/(1+z)^3 shapes (Tables 3 and 4), the same data give B_f0 = 20-27 nG with alpha = 1.7-2.1, and B_f0 = 28-40 nG with alpha = 1.2-1.6, respectively. These shifts are 2-5 times the quoted 4 nG error on B_f0. The choice of the A_rrm/(1+z) shape is motivated by the better visual match of the residual filament component to the simulated curves in Fig. 14, but this same shape yields an astrophysical variance fraction of 46-49 +/- 4 percent, in 4-5 sigma tension with the independent 21 +/- 4 percent estimate from clusters and CGM in Sect. 4.2. The paper proposes an additional ~25 percent local component to reconcile this, but the null dependence of RRM on source spectral index and linear size (Sects. 4.3-4.4) provides no support for such a component. The systematic from the uncalibrated astrophysical redshift dependence is therefore larger than the quoted statistical error and directly controls the headline field strength and slope.
  3. [Sect. 3 and Sect. 5.2] The stochastic primordial model with alpha_s = -1.0 is downscaled by construction: Section 3 states that its normalization is reduced from B_1Mpc = 1.87 nG (the CMB limit) to 0.37 nG specifically to produce a reasonable match to LOFAR RRMs. Section 5.2 then reports this model as favoured by the data. This is circular for the amplitude: the model is adjusted to the data and then found to agree with them. The comparison can test only the shape of the RRM-redshift relation, not the normalization, unless the amplitude is fixed a priori by CMB or other independent constraints. Please reframe the conclusion so that the amplitude is treated as a fitted or externally constrained quantity, and report the goodness of fit of the CMB-consistent normalization for alpha_s = -1.0.
  4. [Sect. 2.3, Fig. 4; Sect. 4.2] The RRM rms-redshift relation used for the fits contains wiggles with peaks at z ~ 0.15, 0.36, and 0.56 (Fig. 4, right panel), which the paper finds anti-correlated with the galaxy number density and leaves unexplained. Since the model of Eq. (8) is a smooth function of redshift, these bin-to-bin fluctuations can bias the inferred slope alpha. Please quantify the sensitivity of alpha and B_f0 to the wiggles, for example by fitting with and without the affected bins or by including a wiggle nuisance term in the likelihood.
minor comments (5)
  1. [Sect. 2.2, Fig. 1] The choice of GRMth = 14 rad/m^2 is made by trading off the RRM rms minimum (at 7 rad/m^2) against sample size, and the selected threshold differs from the minimum by only 1.2 sigma. Since the same RRM data are then used for the scientific analysis, please discuss explicitly how this data-driven selection could affect the inferred rms values and the subsequent fits.
  2. [Fig. 9, right panel] The y-axis label '< p > [rad m^-2]' should be '[%]' (or dimensionless if p is a fraction), since p is the fractional polarization expressed in percent.
  3. [Sect. 4.2] The 'hint of a shock at the virial radius' is presented as intriguing but without a quantitative significance in the text; please state the significance of the 0.8 r_v excess explicitly, in the same way the cluster excess in Sect. 4.1 is reported.
  4. [Sect. 5.1] The sentence 'The term Arrm/(1+z)^2 accounts for an astrophysical component constant with redshift' is confusing because the observed RRM contribution decreases with redshift under this shape; please reword to specify that the rest-frame astrophysical RM is constant.
  5. [Sect. 4.3] The spectral index analysis uses a cross-match radius of 22 arcsec; please state how many of the 576 matched sources fall within the GRMth = 14 rad/m^2 sample used for the main analysis, since the two samples are not identical.

Circularity Check

2 steps flagged · score 6.0 of 10

Favoring of the αs=-1.0 primordial model rests on an amplitude manually downscaled to match LOFAR RRMs; the headline B_f0 and α also follow from a data-selected A_rrm/(1+z) shape.

  1. fitted input called prediction [Section 3, bullet 2; used in Sections 5.2 and 6.2]
    "As will be discussed in Sec. 5.2, unlike in all other cases we found that downscaling the amplitude of the last, αs =−1.0 model, to B1 Mpc = 0.37 nG can produce a reasonable match to LOFAR RRMs."

    The normalization of the only primordial stochastic model that is later declared favoured is reduced from the CMB-based 1.87 nG to 0.37 nG for the explicit purpose of matching the LOFAR RRM data. Section 5.2 then reports that this same model 'looks to match the observed RRM rms', and Section 6.2 concludes that 'primordial models ... with a power spectrum slope of αs =−1.0' are favoured. The amplitude is therefore a fitted input, not an independent prediction; the match cited as evidence is partly constructed from the very data it is used to constrain.

  2. other [Section 5.2 (Fig. 14) and Section 6.2, with Tables 3-5]
    "The comparison of the two panels of Fig. 14 shows a better match between the observed data and the simulations for the model with astrophysical RRM increasing with redshift, that is the shape Arrm/(1+z), especially at low redshift. ... Assuming this astrophysical term, the best-fitting results are pretty independent of the magnetogenesis scenario used to draw the gas density. The average physical magnetic field strength in filaments at z=0 is of Bf,0 =11–15±4 nG."

    The A_rrm/(1+z) shape is selected by comparing the residual of the same LOFAR RRM data to simulation curves whose amplitude was itself tuned to those data. The headline B_f0=11–15 nG and α=2.3–2.6 are then quoted from Table 5 for this data-selected shape, while Tables 3 and 4 show the identical fit with A_rrm/(1+z)^2 or A_rrm/(1+z)^3 gives B_f0=20–27 nG or 28–40 nG. Thus the central numerical claim is not an assumption-independent measurement; it is the output of a self-referential shape choice, and the paper's own tables show how strongly the choice controls the result.

full rationale

The RM-to-field-strength part of the paper is largely self-contained: the RRM rms versus redshift (Eq. 3) comes from the public LoTSS catalogue with external GRM subtraction; the Bayesian fit of Eqs. (8)-(10) uses gas densities from new MHD simulations and external cluster mass/gas profiles (Appendix A); the 21% cluster+CGM census is an independent stacking analysis. No self-citation chain is load-bearing here. The circularity is concentrated in the magnetogenesis comparison and in the choice of astrophysical redshift shape. The αs=-1.0 model is downscaled from the CMB-based 1.87 nG to 0.37 nG specifically to match the LOFAR RRM data and is then reported as favoured by those data (Sections 3, 5.2, 6.2). And the headline B_f0=11–15 nG, α=2.3–2.6 is quoted for the A_rrm/(1+z) shape that was selected by matching the same data after subtracting the fitted A_rrm; Tables 3-5 show the same data yield B_f0=20–27 nG or 28–40 nG for the alternative shapes. This is a partial, not complete, circularity: the RRM slope comparison with red-spectrum models has some independent content, and the paper is transparent about the alternative fits. Score 6.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central measurement depends on several modeling choices: the shape of the astrophysical term, the gas density from one simulation suite, the random-field-direction recipe, and the ad hoc downscaling of the favored primordial model. None of these is independently verified by data outside this paper, so the net contribution is a conditional measurement.

free parameters (5)
  • B_f0 (filament magnetic field at z=0) = 11-15 nG (A_rrm/(1+z) shape); 20-27 nG ((1+z)^2); 28-40 nG ((1+z)^3)
    Central fitted parameter in Eq. (10), Tables 3-5.
  • alpha (redshift slope) = 2.3-2.6 +/- 0.5 ((1+z) shape); 1.7-2.1 ((1+z)^2); 1.2-1.6 ((1+z)^3)
    Fitted slope of B_f in Eq. (10).
  • A_rrm (astrophysical RRM amplitude) = 1.01-1.08 rad/m^2 for (1+z) shape; 1.02-1.14 for (1+z)^2; 1.04-1.20 for (1+z)^3
    Fitted amplitude in Eq. (8).
  • B_1Mpc normalization of stochastic alpha_s=-1.0 model = 0.37 nG (downscaled from 1.87 nG CMB limit)
    Ad hoc downscale to match LOFAR RRMs, Section 3 and 5.2.
  • GRM threshold GRMth = 14 rad/m^2
    Chosen to minimize RRM rms while retaining 653 sources, Section 2.2.
assumptions (6)
  • domain assumption Standard flat Lambda CDM cosmology (Planck 2016)
    Assumed throughout for distances and densities.
  • standard math Faraday rotation formalism: RRM = RM - GRM, with noise subtracted in quadrature
    Used to define the observable, Eqs. (1)-(3).
  • domain assumption Gas density along LOS is represented by a 42.5 Mpc ENZO box replicated 153 times, with density contrast threshold delta_g > 1 for filaments
    Used to compute RRM_f in the fit, Section 3 and 5.1.
  • ad hoc to paper Magnetic field direction is random and changes at each filament crossing
    A modeling assumption to compute RRM_f statistics, Section 5.1.
  • ad hoc to paper Astrophysical RRM component follows A_rrm/(1+z)^n for n=1,2,3, with n=1 favored
    Three shapes tested; n=1 selected based on simulation comparison, Tables 3-5.
  • domain assumption Cluster flagging uses the Roncarelli et al. (2006) broken power-law gas density profile (Eq. A.1) and a 3 cMpc exclusion radius
    Used to mimic the observed avoidance of clusters by polarized sources, Sect. 5.1 and Appendix A.

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Pith. "Pith review of On the nature of LOFAR RMs and new constraints on magnetic fields in cosmic filaments and on magnetogenesis scenarios." pith.science (2026). https://pith.science/paper/MYREU7JD

@misc{pith2026241113499,
  author       = {Pith},
  title        = {Pith review of: On the nature of LOFAR RMs and new constraints on magnetic fields in cosmic filaments and on magnetogenesis scenarios},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MYREU7JD}},
  note         = {Machine review of arXiv:2411.13499}
}
abstract

The measurement of magnetic fields in cosmic web filaments can be used to reveal the magnetogenesis of the Universe. In previous work, we produced first estimates of the field strength and its redshift evolution using the Faraday Rotation Measure (RM) catalogue of extragalactic background sources at low frequency obtained with LOFAR observations. Here we refine our analysis by selecting sources with low Galactic RM, which reduces its residual contamination. We also conduct a comprehensive analysis of the different contributions to the extragalactic RMs along the line of sight, and confirm that they are dominated by the cosmic filaments component, with only 21 percent originating in galaxy clusters and the circumgalactic medium (CGM) of galaxies. We find a possible hint of a shock at the virial radius of massive galaxies. We also find that the fractional polarization of background sources might be a valuable CGM tracer. The newly selected RMs have a steeper evolution with redshift than previously found. The field strength in filaments ($B_f$) and its evolution are estimated assuming $B_f$ evolves as a power-law $B_f=B_{f,0}\,(1+z)^\alpha$. Our analysis finds an average strength at $z=0$ of $B_{f,0} =11$--15~nG, with an error of 4 nG, and a slope $\alpha=2.3$--$2.6 \pm 0.5$, which is steeper than what we previously found. The comoving field has a slope of $\beta=$ [0.3, 0.6$]\pm 0.5$ that is consistent with being invariant with redshift. Primordial magnetogenesis scenarios are favoured by our data, together with a sub-dominant astrophysical-origin RM component increasing with redshift.

Figures

Figures reproduced from arXiv: 2411.13499 by the authors.

Figure 1
Figure 1. Left: RRM rms of the spectroscopic redshift sample filtered by different GRM limits. The orange circle highlights the case with a limit of 14 rad m−2 . Right: RRM rms as a function of Galactic latitude |b| for the case filtered with GRMth = 14 rad m−2 . 4 6 8 10 12 14 |GRM| [rad m 2 ] 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 R R Mrms [ra d m 2 ] [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. RRM rms as a function of |GRM| of the spectroscopic redshift sample filtered by |GRM| < 14 rad m−2 . 20 10 0 10 20 RRM [rad m 2 ] 0.00 0.05 0.10 0.15 0.20 0.25 fraction of sources no filter GRMth=14 rad/m2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. RRM distribution of the spectroscopic redshift sample filtered by |GRM| < 14 rad m−2 . The case with no GRM filter is also shown for comparison. The extragalactic term consists of an RM of astrophysical origin, either local to the source, mostly produced in the environment surrounding the source (Laing et al. 2008), or objects interven￾ing along the sight line such as galaxy clusters or galaxies, and an RM generated… view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: RRM rms in redshift bins of 20 (left) and 40 (right) sources each of the spectroscopic redshift sample filtered by |GRM| < 14 rad m−2 . The linear fit (dashed line) and its uncertainty (shaded area) are also reported. the GRM value. We thus filtered the sample for diff…
Figure 5
Figure 5. Figure 5: RRM rms (top-left), distribution (top-right), and mean fractional polarization (bottom-left) of the full spectroscopic sample at 144 MHz in bins of the separation from the nearest galaxy cluster of mass M > 1.0 × 1014 M⊙ along the LOS. Bottom right: Same as the for the…
Figure 6
Figure 6. Figure 6: Mean fractional polarization (p) at 144 MHz in bins of the impact parameter from the nearest galaxy cluster of mass M > 1.0 × 1014 M⊙ along the LOS. The case of the no GRM filter sample restricted to the redshift range z = 0.5–0.75 is shown. Article number, page 6 of 2…
Figure 7
Figure 7. Figure 7: RRM rms at 144 MHz in bins of the impact parameter from the nearest galaxy cluster of mass M > 1.0 × 1014 M⊙ along the LOS. The full spectroscopic redshift sample and an RRM sample filtered by a GRMth limit are shown. The cases are shifted in separation for clarity. so…
Figure 8
Figure 8. Figure 8: RRM rms (top-left) and < p > (top-right) of the LoTSS RRM sample with a GRM limit of 14 rad m−2 versus source separation in kpc from galaxies of stellar mass M∗ > 1011 M⊙. The bin size is of 50 kpc. Bottom: as for top panels, except the separation is in virial radii, w…
Figure 9
Figure 9. Figure 9: RRM rms (left) and < p > (right) of the LoTSS RRM sample with a GRM limit of 14 rad m−2 versus LOS projected separation in virial radii from galaxies of the three samples of stellar mass M∗ > 1011 M⊙, M∗ = 1010–1011 M⊙, and M∗ = 109–1010 M⊙. The bin size is of 0.15 vir…
Figure 10
Figure 10. Figure 10: Galaxy number density as a function of redshift from the sam￾ple of galaxies with M∗ > 1011 M⊙ obtained from the DESI Legacy Surveys photometric galaxy catalogue. The CGM extension depends on the halo mass (Mh) and red￾shift of the galaxy and these stacking plots mix …
Figure 11
Figure 11. Figure 11: RRM rms of the LoTSS RRM sample as a function of spectral index obtained as described in the main text (top), polarization fraction (mid), and spectral index distribution of the sources we used for this analysis (bottom). We used bins of a width of 0.2. especially of …
Figure 12
Figure 12. Figure 12: RRM rms of the LoTSS RRM sample versus source linear size (top), mean fractional polarization (mid), and linear size distribution of the sources we used for this analysis (bottom). We used bins of a width of 400 kpc. and its extension. However, further investigations …
Figure 13
Figure 13. Figure 13: Best-fit results of equation (8) to the RRM rms as a function of redshift computed from the GRM filtered sample. The gas density is taken from the LOS extracted from the MHD simulation of the primordial stochastic model with αs = 0.0. The case with Bf independent of δ…
Figure 14
Figure 14. Figure 14: The RRM rms measured from our sample in 40-source [PITH_FULL_IMAGE:figures/full_fig_p015_14.png]
Figure 14
Figure 14. Figure 14: Filaments RRMf rms as a function of redshift of the cosmological models used in this work. The RRMs are computed using the gas densities and magnetic fields from the MHD cosmological simulations (astroph and astroph 2 are the first and second astrophysical models disc…

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Reference graph

Works this paper leans on

95 extracted references · 44 canonical work pages · cited by 5 Pith papers

  1. [1]

    Adebahr, B., Berger, A., Adams, E. A. K., et al. 2022, A&A, 663, A103

    2022

  2. [2]

    2023, ApJ, 950, L16

    Aharonian, F., Aschersleben, J., Backes, M., et al. 2023, ApJ, 950, L16

    2023

  3. [3]

    D., Vernstrom, T., & Gaensler, B

    Amaral, A. D., Vernstrom, T., & Gaensler, B. M. 2021, MNRAS, 503, 2913

    2021

  4. [4]

    S., Heald, G

    Anderson, C. S., Heald, G. H., Eilek, J. A., et al. 2021, PASA, 38, e020

    2021

  5. [5]

    S., McClure-Gri ffiths, N

    Anderson, C. S., McClure-Gri ffiths, N. M., Rudnick, L., et al. 2024, arXiv e- prints, arXiv:2407.20325

    arXiv 2024

  6. [6]

    2022, A&A, 663, L6

    Angelinelli, M., Ettori, S., Dolag, K., Vazza, F., & Ragagnin, A. 2022, A&A, 663, L6

    2022

  7. [7]

    2023, A&A, 675, A188 Arámburo-García, A., Bondarenko, K., Boyarsky, A., et al

    Angelinelli, M., Ettori, S., Dolag, K., Vazza, F., & Ragagnin, A. 2023, A&A, 675, A188 Arámburo-García, A., Bondarenko, K., Boyarsky, A., et al. 2021, MNRAS, 505, 5038 Arámburo-García, A., Bondarenko, K., Boyarsky, A., et al. 2022, MNRAS, 515, 5673 Arámburo-García, A., Bondarenko, K., Boyarsky, A., et al. 2023, MNRAS, 519, 4030 Astropy Collaboration, Robi...

    2023

  8. [8]

    L., Miniati, F., & Lilly, S

    Bernet, M. L., Miniati, F., & Lilly, S. J. 2010, ApJ, 711, 380

    2010

Show all 95 references
  1. [9]

    L., Miniati, F., Lilly, S

    Bernet, M. L., Miniati, F., Lilly, S. J., Kronberg, P. P., & Dessauges-Zavadsky, M. 2008, Nature, 454, 302

    2008

  2. [10]

    2006, MNRAS, 370, 319

    Bertone, S., V ogt, C., & Enßlin, T. 2006, MNRAS, 370, 319

    2006

  3. [11]

    & Neronov, A

    Blunier, J. & Neronov, A. 2024, arXiv e-prints, arXiv:2403.13418 10 https://lofar-mksp.org/ 11 http://healpix.sf.net Article number, page 17 of 20 A&A proofs: manuscript no. B_stoch Böckmann, K., Brüggen, M., Heesen, V ., et al. 2023, arXiv e-prints, arXiv:2308.11391

    2024 arXiv

  4. [12]

    2011, A&A, 530, A24

    Bonafede, A., Govoni, F., Feretti, L., et al. 2011, A&A, 530, A24

    2011

  5. [13]

    2022, A&A, 660, A80 Bouché, N., Murphy, M

    Bondarenko, K., Boyarsky, A., Korochkin, A., et al. 2022, A&A, 660, A80 Bouché, N., Murphy, M. T., Péroux, C., et al. 2007, ApJ, 669, L5

    2022

  6. [14]

    Brentjens, M. A. & de Bruyn, A. G. 2005, A&A, 441, 1217

    2005

  7. [15]

    2017, MNRAS, 468, 4246

    Brown, S., Vernstrom, T., Carretti, E., et al. 2017, MNRAS, 468, 4246

    2017

  8. [16]

    Burn, B. J. 1966, MNRAS, 133, 67

    1966

  9. [17]

    P., Vacca, V ., et al

    Carretti, E., O’Sullivan, S. P., Vacca, V ., et al. 2023, MNRAS, 518, 2273

    2023

  10. [18]

    P., et al

    Carretti, E., Vacca, V ., O’Sullivan, S. P., et al. 2022, MNRAS, 512, 945

    2022

  11. [19]

    2015, ApJS, 219, 8

    Chang, Y .-Y ., van der Wel, A., da Cunha, E., & Rix, H.-W. 2015, ApJS, 219, 8

    2015

  12. [20]

    J., Cotton, W

    Condon, J. J., Cotton, W. D., Greisen, E. W., et al. 1998, AJ, 115, 1693 de Gasperin, F., Intema, H. T., & Frail, D. A. 2018, MNRAS, 474, 5008

    1998

  13. [21]

    M., West, J., Thomson, A

    Dickey, J. M., West, J., Thomson, A. J. M., et al. 2022, ApJ, 940, 75

    2022

  14. [22]

    H., et al

    Dietl, J., Pacaud, F., Reiprich, T. H., et al. 2024, arXiv e-prints, arXiv:2401.17281

    2024 arXiv

  15. [23]

    2009, MNRAS, 392, 1008

    Donnert, J., Dolag, K., Lesch, H., & Müller, E. 2009, MNRAS, 392, 1008

    2009

  16. [24]

    Eroshenko, Y . N. 2023, arXiv e-prints, arXiv:2311.01207

    2023 arXiv

  17. [25]

    S., O’Sullivan, S

    Farnes, J. S., O’Sullivan, S. P., Corrigan, M. E., & Gaensler, B. M. 2014, ApJ, 795, 63

    2014

  18. [26]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306

    2013

  19. [27]

    1971, Progress of Theoretical Physics Supplement, 49, 181

    Fujimoto, M., Kawabata, K., & Sofue, Y . 1971, Progress of Theoretical Physics Supplement, 49, 181

    1971

  20. [28]

    M., Landecker, T

    Gaensler, B. M., Landecker, T. L., Taylor, A. R., & POSSUM Collaboration. 2010, in American Astronomical Society Meeting Abstracts, V ol. 215, Amer- ican Astronomical Society Meeting Abstracts #215, 470.13

    2010

  21. [29]

    T., Leahy, J

    Garrington, S. T., Leahy, J. P., Conway, R. G., & Laing, R. A. 1988, Nature, 331, 147

    1988

  22. [30]

    2019, ApJ, 884, 169

    Gaspari, M., Eckert, D., Ettori, S., et al. 2019, ApJ, 884, 169

    2019

  23. [31]

    2020, A&A, 634, A135 Górski, K

    Girelli, G., Pozzetti, L., Bolzonella, M., et al. 2020, A&A, 634, A135 Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759

    2020

  24. [32]

    M., Robishaw, T., & Gaensler, B

    Hammond, A. M., Robishaw, T., & Gaensler, B. M. 2012, arXiv e-prints, arXiv:1209.1438

    2012 arXiv

  25. [33]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

    2020

  26. [34]

    P., Brüggen, M., et al

    Heesen, V ., O’Sullivan, S. P., Brüggen, M., et al. 2023, A&A, 670, L23

    2023

  27. [35]

    N., Brüggen, M., Zhang, X., et al

    Hoang, D. N., Brüggen, M., Zhang, X., et al. 2023, MNRAS, 523, 6320

    2023

  28. [36]

    2023, arXiv e-prints, arXiv:2306.05970

    Huang, Y .-Y ., Dai, C.-y., Zhang, H.-M., Liu, R.-Y ., & Wang, X.-Y . 2023, arXiv e-prints, arXiv:2306.05970

    2023 arXiv

  29. [37]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90

    2007

  30. [38]

    S., Betti, S., et al

    Hutschenreuter, S., Anderson, C. S., Betti, S., et al. 2022, A&A, 657, A43

    2022

  31. [39]

    & Enßlin, T

    Hutschenreuter, S. & Enßlin, T. A. 2020, A&A, 633, A150

    2020

  32. [40]

    2024, arXiv e-prints, arXiv:2402.16946

    Ilani, G., Hou, K.-C., & Keshet, U. 2024, arXiv e-prints, arXiv:2402.16946

    2024 arXiv

  33. [41]

    T., Jagannathan, P., Mooley, K

    Intema, H. T., Jagannathan, P., Mooley, K. P., & Frail, D. A. 2017, A&A, 598, A78

    2017

  34. [42]

    & Chand, H

    Joshi, R. & Chand, H. 2013, MNRAS, 434, 3566

    2013

  35. [43]

    G., Martin, C

    Kacprzak, G. G., Martin, C. L., Bouché, N., et al. 2014, ApJ, 792, L12

    2014

  36. [44]

    Kennicutt, Jr., R. C. 1998, ApJ, 498, 541

    1998

  37. [45]

    S., Lilly, S

    Kim, K. S., Lilly, S. J., Miniati, F., et al. 2016, ApJ, 829, 133

    2016

  38. [46]

    Kravtsov, A. V . 2003, ApJ, 590, L1

    2003

  39. [47]

    Kronberg, P. P. 1994, Reports on Progress in Physics, 57, 325

    1994

  40. [48]

    P., Bernet, M

    Kronberg, P. P., Bernet, M. L., Miniati, F., et al. 2008, ApJ, 676, 70

    2008

  41. [49]

    Kronberg, P. P. & Perry, J. J. 1982, ApJ, 263, 518

    1982

  42. [50]

    P., Reinhardt, M., & Simard-Normandin, M

    Kronberg, P. P., Reinhardt, M., & Simard-Normandin, M. 1977, A&A, 61, 771

    1977

  43. [51]

    Laing, R. A. 1988, Nature, 331, 149

    1988

  44. [52]

    A., Bridle, A

    Laing, R. A., Bridle, A. H., Parma, P., & Murgia, M. 2008, MNRAS, 391, 521

    2008

  45. [53]

    S., et al

    Lamee, M., Rudnick, L., Farnes, J. S., et al. 2016, ApJ, 829, 5

    2016

  46. [54]

    & Pooley, G

    Liu, R. & Pooley, G. 1991, MNRAS, 249, 343

    1991

  47. [55]

    2021, A&A, 652, A80

    Locatelli, N., Vazza, F., Bonafede, A., et al. 2021, A&A, 652, A80

    2021

  48. [56]

    F., Brammer, G., van Dokkum, P., et al

    Lundgren, B. F., Brammer, G., van Dokkum, P., et al. 2012, ApJ, 760, 49

    2012

  49. [57]

    Malik, S., Chand, H., & Seshadri, T. R. 2020, ApJ, 890, 132

    2020

  50. [58]

    C., Darvish, B., Lin, Z., et al

    Martin, D. C., Darvish, B., Lin, Z., et al. 2023, Nature Astronomy, 7, 1390

    2023

  51. [59]

    2022, ApJ, 929, 127

    Mtchedlidze, S., Domínguez-Fernández, P., Du, X., et al. 2022, ApJ, 929, 127

    2022

  52. [60]

    & V ovk, I

    Neronov, A. & V ovk, I. 2010, Science, 328, 73

    2010

  53. [61]

    2015, A&A, 575, A118

    Oppermann, N., Junklewitz, H., Greiner, M., et al. 2015, A&A, 575, A118

    2015

  54. [62]

    Oren, A. L. & Wolfe, A. M. 1995, ApJ, 445, 624

    1995

  55. [63]

    J., Andrade-Santos, F., et al

    Osinga, E., van Weeren, R. J., Andrade-Santos, F., et al. 2022, A&A, 665, A71 O’Sullivan, S. P., Shimwell, T. W., Hardcastle, M. J., et al. 2023, MNRAS, 519, 5723

    2022

  56. [64]

    & Loeb, A

    Padmanabhan, H. & Loeb, A. 2023, ApJ, 946, L18

    2023

  57. [65]

    Paoletti, D., Chluba, J., Finelli, F., & Rubiño-Martín, J. A. 2019, MNRAS, 484, 185

    2019

  58. [66]

    & Finelli, F

    Paoletti, D. & Finelli, F. 2019, J. Cosmology Astropart. Phys., 2019, 028 Planck Collaboration, Adam, R., Ade, P. A. R., et al. 2016a, A&A, 596, A103 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016b, A&A, 594, A13

    2019

  59. [67]

    P., O’Sullivan, S

    Pomakov, V . P., O’Sullivan, S. P., Brüggen, M., et al. 2022, MNRAS, 515, 256

    2022

  60. [68]

    1972, A&A, 19, 104

    Reinhardt, M. 1972, A&A, 19, 104

    1972

  61. [69]

    H., Basu, K., Ettori, S., et al

    Reiprich, T. H., Basu, K., Ettori, S., et al. 2014, in Suzaku-MAXI 2014: Expand- ing the Frontiers of the X-ray Universe, ed. M. Ishida, R. Petre, & K. Mitsuda, 362

    2014

  62. [70]

    J., Galvin, T

    Riseley, C. J., Galvin, T. J., Sobey, C., et al. 2020, PASA, 37, e029

    2020

  63. [71]

    2006, MNRAS, 373, 1339

    Roncarelli, M., Ettori, S., Dolag, K., et al. 2006, MNRAS, 373, 1339

    2006

  64. [72]

    2008, Science, 320, 909

    Ryu, D., Kang, H., Cho, J., & Das, S. 2008, Science, 320, 909

    2008

  65. [73]

    Schnitzeler, D. H. F. M., Carretti, E., Wieringa, M. H., et al. 2019, MNRAS, 485, 1293

    2019

  66. [74]

    F., Zabl, J., et al

    Schroetter, I., Bouché, N. F., Zabl, J., et al. 2019, MNRAS, 490, 4368

    2019

  67. [75]

    W., Hardcastle, M

    Shimwell, T. W., Hardcastle, M. J., Tasse, C., et al. 2022, A&A, 659, A1

    2022

  68. [76]

    1979, PASJ, 31, 125

    Sofue, Y ., Fujimoto, M., & Kawabata, K. 1979, PASJ, 31, 125

    1979

  69. [77]

    2016, Reports on Progress in Physics, 79, 076901

    Subramanian, K. 2016, Reports on Progress in Physics, 79, 076901

    2016

  70. [78]

    Thomson, R. C. & Nelson, A. H. 1982, MNRAS, 201, 365

    1982

  71. [79]

    2024, ApJ, 963, 135

    Tjemsland, J., Meyer, M., & Vazza, F. 2024, ApJ, 963, 135

    2024

  72. [80]

    Turner, M. S. & Widrow, L. M. 1988, Phys. Rev. D, 37, 2743

    1988

  73. [81]

    2010, A&A, 514, A71

    Vacca, V ., Murgia, M., Govoni, F., et al. 2010, A&A, 514, A71

    2010

  74. [82]

    2018, MNRAS, 479, 776

    Vacca, V ., Murgia, M., Govoni, F., et al. 2018, MNRAS, 479, 776

    2018

  75. [83]

    2021, Reports on Progress in Physics, 84, 074901 van Haarlem, M

    Vachaspati, T. 2021, Reports on Progress in Physics, 84, 074901 van Haarlem, M. P., Wise, M. W., Gunst, A. W., et al. 2013, A&A, 556, A2

    2021

  76. [84]

    2017, Classical and Quantum Gravity, 34, 234001

    Vazza, F., Brüggen, M., Gheller, C., et al. 2017, Classical and Quantum Gravity, 34, 234001

    2017

  77. [85]

    2021, MNRAS, 500, 5350

    Vazza, F., Paoletti, D., Banfi, S., et al. 2021, MNRAS, 500, 5350

    2021

  78. [86]

    M., Brown, S., Lenc, E., & Norris, R

    Vernstrom, T., Gaensler, B. M., Brown, S., Lenc, E., & Norris, R. P. 2017, MN- RAS, 467, 4914

    2017

  79. [87]

    M., Rudnick, L., & Andernach, H

    Vernstrom, T., Gaensler, B. M., Rudnick, L., & Andernach, H. 2019, ApJ, 878, 92

    2019

  80. [88]

    M., Vacca, V ., et al

    Vernstrom, T., Gaensler, B. M., Vacca, V ., et al. 2018, MNRAS, 475, 1736

    2018

  81. [89]

    2021, MNRAS, 505, 4178

    Vernstrom, T., Heald, G., Vazza, F., et al. 2021, MNRAS, 505, 4178

    2021

  82. [90]

    2023, Science Advances, 9, eade7233 V ovk, I., Korochkin, A., Neronov, A., & Semikoz, D

    Vernstrom, T., West, J., Vazza, F., et al. 2023, Science Advances, 9, eade7233 V ovk, I., Korochkin, A., Neronov, A., & Semikoz, D. 2023, arXiv e-prints, arXiv:2306.07672

    2023 arXiv

  83. [91]

    L., Perry, J

    Welter, G. L., Perry, J. J., & Kronberg, P. P. 1984, ApJ, 279, 19

    1984

  84. [92]

    Wen, Z. L. & Han, J. L. 2015, ApJ, 807, 178

    2015

  85. [93]

    P., Han, J

    You, X. P., Han, J. L., & Chen, Y . 2003, Acta Astronomica Sinica, 44, 155

    2003

  86. [94]

    2019, The Journal of Open Source Soft- ware, 4, 1298

    Zonca, A., Singer, L., Lenz, D., et al. 2019, The Journal of Open Source Soft- ware, 4, 1298

    2019

  87. [95]

    2019, ApJS, 242, 8 Article number, page 18 of 20 E

    Zou, H., Gao, J., Zhou, X., & Kong, X. 2019, ApJS, 242, 8 Article number, page 18 of 20 E. Carretti et al.: Magnetic fields in cosmic filaments Appendix A: Gas density contrast versus distance from centre in a galaxy cluster Using galaxy cluster simulations, Roncarelli et al. ...

    2006

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