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CRexit: how different cosmic ray transport modes affect thermal instability in the circumgalactic medium

T0 review · 4 major / 3 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Cosmic rays escape collapsing CGM clouds before they can block condensation.

desk verdict Solid, useful 3D CRMHD comparison whose qualitative conclusion — CRs escape collapsing clouds under 2-moment transport — likely holds, but the quantitative κ_eff rests on an untested Alfvén-wave seed and an under-resolved cold phase. read the letter →

arxiv 2501.18678 v2 pith:GDWW45GB submitted 2025-01-30 astro-ph.GA astro-ph.HE

Matthias Weber , Timon Thomas , Christoph Pfrommer , Ruediger Pakmor This is my paper · ORCID
classification astro-ph.GAastro-ph.HE
keywords cosmicraysthermalinstabilitycircumgalacticmediumCRtransportdiffusionstreamingmagnetohydrodynamicscoldcloudcollapse
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 asks whether cosmic rays (CRs) can prevent cold clouds in the circumgalactic medium from condensing out of hot gas via thermal instability. Using three-dimensional CR-magnetohydrodynamic simulations with several transport prescriptions, the authors show that CRs only suppress collapse in the idealized limit of purely advective transport, in which CRs are frozen into the gas. Once CRs are allowed to stream and diffuse along magnetic field lines, they escape the collapsing regions on timescales shorter than the cloud collapse time, so the cold-gas morphology ends up nearly identical to a simulation without CRs. The study also shows that numerical resolution controls this outcome, because the CR escape time grows with the square of the cloud radius, so under-resolved, artificially large clouds trap CRs and exaggerate their pressure support. Why this matters: the cold gas reservoir of galaxy halos is shaped by thermal instability, and this result changes whether CR pressure is a leading regulator of that reservoir.

What carries the argument

The load-bearing object is the effective CR diffusion coefficient $\kappa_{\rm eff} = f_{\rm cr}/(\mathbf{b}\cdot\nabla\varepsilon_{\rm cr})$, which condenses CR streaming and diffusion into a single number; in the purely diffusive limit it equals the intrinsic diffusion coefficient $\kappa_{\rm cr}$. It is paired with the escape-time ratio $t_{\rm cr}/t_{\rm collapse}$, where $t_{\rm cr} = r_{\rm cloud}^2/\kappa_{\rm eff}$ and the collapse time is approximated by the cloud's cooling time $t_{\rm cool}$. The two-moment transport scheme with the $P_1$ Eddington closure carries the argument: it evolves CR energy density and flux, letting the scattering rate emerge from gyroresonant Alfvén-wave generation balanced against non-linear Landau damping. From this scheme $\kappa_{\rm eff}$ spans roughly $10^{25}$ to $10^{35}\,\mathrm{cm^2\,s^{-1}}$, with CR-energy-weighted medians of $1.3\times10^{29}$ to $8\times10^{29}\,\mathrm{cm^2\,s^{-1}}$ depending on the initial CR pressure fraction, and the same machinery produces the open CR-motorway field topology that lets CRs drain out of clouds.

What would settle it

Rerun the highest-resolution run with $X_{\rm cr,0}=3$ at twice the resolution and track cloud radii and $\kappa_{\rm eff}$; if the escape-time ratio $t_{\rm cr}/t_{\rm collapse}$ moves above unity or the CR-energy-weighted $\kappa_{\rm eff}$ falls below roughly $3\times10^{28}\,\mathrm{cm^2\,s^{-1}}$, the claim that CRs always escape before collapse would fail. Observationally, gamma-ray or radio signatures of CRs trapped in cold CGM clouds, or absorption-line measurements implying sustained CR pressure support, would provide the same test.

Watch

Extended reading notes

Core claim

The central claim is that active cosmic-ray transport, not CR pressure itself, determines whether CRs affect thermally unstable circumgalactic gas. In the two-moment CR-magnetohydrodynamic model, CRs stream along magnetic field lines and diffuse with an effective coefficient $\kappa_{\rm eff} = f_{\rm cr}/(\mathbf{b}\cdot\nabla\varepsilon_{\rm cr})$, so the rate at which they leave a cloud is set by the escape time $t_{\rm cr} = r_{\rm cloud}^2/\kappa_{\rm eff}$. The open, flux-frozen field lines that pierce a collapsing cloud act as escape routes, which the authors call CR motorways, so CRs drain out before their pressure can build up. Comparing this escape time with the cloud collapse time, approximated by the cooling time $t_{\rm cool}$, gives median ratios around $6\times10^{-2}$ in the fast-transport runs, and the relative CR pressure inside clouds stays too low to halt contraction. The same ratio explains the apparent contradiction with earlier work: with slower diffusion ($\kappa_0 = 3\times10^{27}$ or $3\times10^{28}\,\mathrm{cm^2\,s^{-1}}$) or with the artificially large clouds of low-resolution runs, $t_{\rm cr}/t_{\rm collapse}$ rises toward or above unity and CR pressure support becomes significant. The authors conclude that in realistic CR transport models the cold phase of the CGM forms essentially as if CRs were absent.

Load-bearing premise

The conclusion relies on the two-moment cosmic-ray transport model, with non-linear Landau damping as the only wave-damping mechanism and a reduced speed of light of 3000 km/s, predicting the correct effective diffusion coefficient inside and around condensing clouds; if real clouds diffuse cosmic rays an order of magnitude more slowly, their pressure support could again become significant.

Editorial extensions

If this is right

  • In the two-moment model, the cold-gas morphology and cold-mass fraction barely depend on the initial CR pressure, from purely thermal ($X_{\rm cr,0}=0$) to strongly CR-dominated ($X_{\rm cr,0}=30$) atmospheres.
  • A diffusion-only model with $\kappa_0 = 3\times10^{29}\,\mathrm{cm^2\,s^{-1}}$ reproduces the two-moment results, whereas slower diffusion ($3\times10^{27}$ or $3\times10^{28}$) delays or suppresses condensation.
  • The onset of collapse is governed by the CR transport speed rather than by the CR pressure fraction, so conclusions about CR stabilization must be tied to a specific transport model.
  • Numerical resolution changes cloud radii and therefore $t_{\rm cr}$ quadratically; low-resolution runs produce clouds up to about ten times larger, with relative CR pressures of $X_{\rm cr}\sim10$-$22$, while high-resolution runs yield $X_{\rm cr}\sim4$-$8$ and mostly escape-dominated clouds.
  • Purely advective CR transport, which blocks escape, can suppress thermal instability entirely for $X_{\rm cr,0}=3$, but the authors identify this as an idealized limiting case rather than a realistic outcome.

Reading between the lines

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

  • This suggests that galaxy-scale simulations that cannot resolve the cooling length should adopt CR transport with an effective diffusivity comparable to the two-moment values, otherwise they will overstate CR pressure support for cold clouds.
  • The CR-motorway picture implies a testable condition: clouds whose internal magnetic field reconnects into closed loops should confine CRs longer and collapse more slowly, which could be checked with simulations using different initial magnetic topologies.
  • Fast CR escape also weakens CR heating inside cold clouds, so the thermal balance of the cold CGM may be closer to radiative equilibrium than CR-regulated models assume; line-ratio observations sensitive to CR heating could constrain this.
  • If observed CGM clouds sit near the cooling-length scale, they would belong to the fast-escape end of the distribution shown here, making CR pressure support small in real systems.
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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 / 3 minor

Summary. This paper uses idealized 3D CRMHD simulations of a stratified, isocooling CGM box to study how cosmic ray transport affects thermal instability and cold cloud formation. It compares purely advective CR transport, constant-diffusion transport with three different diffusion coefficients, and a 2-moment streaming/diffusion model, across initial CR pressure fractions from X_cr,0 = 0 to 30. The main finding is that with 2-moment transport, the effective CR diffusion coefficient is large, the CR escape time from collapsing clouds is short compared with the cloud collapse time, and TI proceeds almost as if CRs were absent. Purely advective CRs, by contrast, strongly suppress or delay collapse. The paper also argues that numerical resolution controls cloud sizes and therefore strongly affects the CR escape time because t_cr ~ r_cloud^2.

Significance. If the central result holds, the paper is significant: it challenges the common expectation that CR pressure stabilizes cold CGM gas, and it offers a concrete physical explanation based on escape along open magnetic field lines combined with the effective diffusion coefficient. The systematic comparison of transport models, the use of the 2-moment CRMHD framework, and the explicit resolution study are strengths. The paper is also careful to state several limitations, including the unresolved cooling length in Appendix A and the dependence on the initial Alfvén wave energy. However, the quantitative central claim is sensitive to two unconstrained choices: the seed Alfvén wave energy and the numerical resolution of cold clouds. These choices directly set the value of kappa_eff inside the clouds and hence the controlling ratio t_cr/t_collapse. The result is therefore a compelling but not yet fully robust conclusion.

major comments (4)
  1. [§4, Fig. 5; §9 point 1] The claim that purely advective CRs suppress TI is entangled with the initialization. The turbulent velocity field is normalized to X_kin,0 = 0.3 in every run regardless of X_cr,0 (Sec. 3.4), so runs with larger CR pressure have weaker initial density perturbations. The text itself states in §4 that the complete suppression in the X_cr,0 = 3 advective run arises from the method used to generate the initial density perturbations, yet the abstract and Conclusion 1 attribute this suppression to CR pressure. This should be rephrased, or a control suite with the perturbation amplitude normalized to the total pressure should be added.
  2. [§6.3, Eq. (17)] The paper does not specify how kappa_eff is assigned to each cloud when computing t_cr = r_cloud^2/kappa. If the value used is the global CR-energy-weighted median from Sec. 6.1 rather than a value evaluated inside or immediately around the cold cloud, the resulting distribution of t_cr/t_collapse and the quoted median of about 0.06 may not measure escape from collapsing clouds. Because this ratio is the controlling parameter for the central claim, the exact cell selection and weighting used in Fig. 13 should be stated, and a cloud-local variant of kappa_eff should be provided.
  3. [§3.4, Eq. (13)] The central escape-time result is sensitive to the initial Alfvén wave energy density, which is set to epsilon_a,0 = 10^-3 epsilon_cr,0 in all 2-moment runs. Since kappa_cr is proportional to B^2/epsilon_a (Eq. 13), this small seed produces very weak initial scattering and a very large initial kappa_cr. A turbulent CGM could contain a larger pre-existing wave population; for example, increasing epsilon_a by a factor of 10 lowers kappa_cr by the same factor and raises t_cr/t_collapse from about 0.06 to about 0.6, which is the difference between prompt escape and marginal confinement. The discussion in Sec. 8.2 of additional damping mechanisms does not constrain the seed wave energy. A small parameter study around epsilon_a,0 is needed to establish robustness.
  4. [Appendix A; §7.2] Appendix A shows that gas below about 10^5 K has cooling lengths smaller than the cell size, and the resolution study in Sec. 7 does not demonstrate convergence at the highest-resolution run. Because t_cr depends quadratically on r_cloud (Eq. 17) and the simulated cloud radii decrease monotonically with resolution, the reported values of t_cr/t_collapse remain resolution-dependent. Under-resolution can also smooth CR pressure gradients inside the smallest clouds, suppress gyroresonant wave generation, and artificially increase kappa_eff. The net effect on the quoted median ratio is not quantified. The authors should either add a converged resolution or bracket kappa_eff and r_cloud using a model for the unresolved small-scale structure.
minor comments (3)
  1. [Fig. 13 caption] The caption contains typographical errors: 'tcr/tcollaspe' should be 't_cr/t_collapse', and 'clouds collapse is slower' should read 'cloud collapse is slower'.
  2. [Abstract; §6.1] The abstract states that the effective CR diffusion coefficient ranges from 10^29 to 10^30 cm^2/s, while Fig. 10 shows a full distribution spanning roughly 10^25 to 10^35 cm^2/s. The abstract is presumably referring to the CR-energy-weighted median values reported in Sec. 6.1; this should be clarified to avoid an apparent inconsistency.
  3. [§6.3] The collapse time is approximated by the cooling time estimated from the mean cloud density and mean internal energy. Since t_cool varies by orders of magnitude within a single cloud, a sensitivity test using the density-weighted cooling time or the cooling time of the densest cells would clarify whether the escape-time ratio is robust to this choice.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the escape-time conclusion is a diagnostic of an evolved simulation, not a fitted or self-referential input.

full rationale

The paper's derivation chain is: initialize an isocooling CGM with a stated CR pressure fraction, evolve with three transport models (advective, constant-diffusion, and 2-moment CRMHD), and diagnose cold clouds and the CR escape time t_cr = r_cloud^2/kappa_eff. No parameter is fitted to the target conclusion. kappa_eff is a model output defined by Eq. 15 (f_cr/(b·del eps_cr)), and the reported median values are computed from the simulation, not imposed. The constant-diffusion runs use independently chosen values (3e27, 3e28, 3e29 cm^2/s) that bracket the canonical ISM value and the 2-moment result, and the 2-moment run is compared with Butsky et al. (2020) and Tsung et al. (2023). The 2-moment CRMHD solver is cited from the authors' own papers (Thomas & Pfrommer 2019; Thomas et al. 2021, 2023), but the key equations are restated in the text and the method is not invoked as an external uniqueness theorem; this is ordinary method self-citation and is not load-bearing in the sense of making the conclusion true by construction. The freely chosen Alfven-wave seed eps_a,0 = 1e-3 eps_cr,0 and the under-resolution of T < 1e5 K gas noted in Appendix A are modeling uncertainties that could change the magnitude of kappa_eff, but an assumption that enters a simulation and a conclusion drawn from its output is not circularity unless the conclusion is used to justify the assumption, which does not happen here. Sec. 8.2 explicitly lists neglected damping mechanisms and positions the result as a bound; this is a stated limitation, not a hidden circularity. The central claim is therefore self-contained against the stated model and external comparisons, with no fitted parameter renamed as a prediction.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central result is a simulation outcome rather than a derivation, so the ledger records the hand-chosen initial parameters, the numerical choices, and the physical assumptions of the 2-moment CRMHD model. No new particles or forces are introduced. The load-bearing inputs are the CR transport model of Thomas & Pfrommer (2019) and the assumption that non-linear Landau damping dominates; the paper itself claims additional damping mechanisms would only strengthen the escape conclusion, which if true makes the main claim robust to that assumption.

free parameters (7)
  • Initial CR pressure fraction X_cr,0 = 0.03, 0.3, 3, 30
    Chosen by hand to span thermal to CR-dominated atmospheres; controls the entire parameter study but not fitted to data.
  • Initial kinetic-to-thermal pressure ratio X_kin,0 = 0.3
    Fixed across all runs; because it is defined relative to thermal pressure, increasing X_cr,0 weakens the initial density perturbations, partly causing the advective-suppression result in Sec. 4.
  • Initial magnetic-to-thermal pressure ratio X_mag,0 = 0.01
    Chosen to match simulated CGM values from Pakmor et al. 2020; small but affects field-line topology for anisotropic CR transport.
  • Cooling-to-free-fall time ratio tau = 0.3
    Fiducial value in the thermally unstable regime; other values are tested only without CRs in Appendix B.
  • Constant diffusion coefficient kappa_0 in diffusion-only runs = 3e27, 3e28, 3e29 cm^2/s
    Three chosen rates spanning the canonical ISM value to fast diffusion, used to bracket the 2-moment results.
  • Reduced speed of light c_red = 3000 km/s
    Numerical choice to separate CR transport speed from MHD wave speeds; its effect on kappa_eff is not convergence-tested.
  • Initial Alfven wave energy density eps_a,0 = 1e-3 eps_cr,0
    Initial condition for wave energy in 2-moment runs; sets the early scattering rate but is expected to be washed out by the dynamics.
assumptions (5)
  • domain assumption CRs are tied to magnetic field lines and transported anisotropically along them, with gyroradii much smaller than CGM scales.
    Invoked in Sec. 2.2 to justify the CRMHD framework and the use of field-aligned streaming and diffusion.
  • domain assumption Non-linear Landau damping dominates wave damping in the CGM; other damping mechanisms are neglected.
    Stated in Sec. 2.2 and revisited in Sec. 8.2; the paper argues that additional damping would only increase kappa_eff and strengthen the escape conclusion.
  • domain assumption The isocooling, hydrostatic, turbulent box with global heating balancing cooling is a representative CGM patch.
    Used to set initial conditions in Sec. 3.3 and 3.4; the idealized nature is acknowledged in Sec. 8.2.
  • domain assumption The cloud collapse time is approximately the cooling time, t_collapse ~ t_cool.
    Used in Sec. 6.3 to define the critical escape-time ratio t_cr/t_collapse; this ignores magnetic, turbulent, or CR-pressure effects on collapse times.
  • domain assumption The reduced speed of light of 3000 km/s does not alter the effective CR transport in the simulated environments.
    Assumed in Sec. 3.1 to separate signal speeds; no convergence test in c_red is provided.

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Cite this review

Pith. "Pith review of CRexit: how different cosmic ray transport modes affect thermal instability in the circumgalactic medium." pith.science (2026). https://pith.science/paper/GDWW45GB

@misc{pith2026250118678,
  author       = {Pith},
  title        = {Pith review of: CRexit: how different cosmic ray transport modes affect thermal instability in the circumgalactic medium},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GDWW45GB}},
  note         = {Machine review of arXiv:2501.18678}
}
abstract

The circumgalactic medium (CGM) plays a critical role in galaxy evolution, influencing gas flows, feedback processes, and galactic dynamics. Observations show a substantial cold gas reservoir in the CGM, but the mechanisms driving its formation and evolution remain unclear. Cosmic rays (CRs), as a source of non-thermal pressure, are increasingly recognized as key regulators of cold gas dynamics. This study explores how CRs affect cold clouds that condense from the hot CGM via thermal instability (TI). Using 3D CR-magnetohydrodynamic (CRMHD) simulations with AREPO, we assess the impact of various CR transport models on cold gas evolution. Under purely advective CR transport, CR pressure significantly suppresses the collapse of thermally unstable regions, altering the CGM's structure. In contrast, realistic CR transport models reveal that CRs escape collapsing regions via streaming and diffusion along magnetic fields, diminishing their influence on the thermal and dynamic structure of the cold CGM. The ratio of the CR escape time to the cloud collapse time emerges as a critical factor in determining the impact of CRs on TI. CRs remain confined within cold clouds when effective CR diffusion is slow which maximizes their pressure support and inhibits collapse. Fast effective CR diffusion, as realized in our 2-moment CRMHD model, facilitates rapid CR escape, reducing their stabilizing effect. This realistic CR transport model shows a wide dynamic range of the effective CR diffusion coefficient, ranging from $10^{29}$ to $10^{30}\,\mathrm{cm^{2}\,s^{-1}}$ for thermally- to CR-dominated atmospheres, respectively. In addition to these CR transport-related effects, we demonstrate that high numerical resolution is crucial to avoid spuriously large clouds formed in low-resolution simulations, which would result in overly long CR escape times and artificially amplified CR pressure support.

Figures

Figures reproduced from arXiv: 2501.18678 by the authors.

Figure 1
Figure 1. Simulation domain overview. The figure shows a slice through the x-z plane at y = 0 of our tall-box setup. The box extends from −3H to +3H in the z-direction, and from −0.5H to +0.5H in the x- and y-directions, where H = 30.11 kpc. Periodic boundary conditions are applied in the x- and y-directions, while the z-direction features an outflow boundary condition. The simulation is conducted without CRs and assumes a ta… view at source ↗
Figure 2
Figure 2. The figure displays an x-z projection of the hydrogen column density, NH. Red circles represent the radii of clouds detected by the cloud-identification algorithm and red dots indicate their respective cen￾troids. Note that overlapping circles result from the projection of clouds located at different y positions and do not necessarily represent physical overlaps in three-dimensional space. 2.3. Cold-gas metrics We u… view at source ↗
Figure 3
Figure 3. Initial density profiles of the simulation box for various Xcr,0. The solid lines show the implemented isocooling density profile, the dashed lines show the corresponding isothermal density profile with T0 = 106 K for comparison. For higher initial Xcr,0, more mass is contained in the atmosphere because the additional CR pressure supports the HSE with￾out contributing to the gravitational force. Density profile. The… view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Projections of the hydrogen column density, NH, employing purely advective CR transport. The panels show the x − z plane of a section extending from z = 0 to z = 2H and the depth of the projection is 1 H. The snapshot is taken at t = 7 tcool ≈ 2 tff. All simulations ar…
Figure 5
Figure 5. Figure 5: Time evolution of cold gas metrics for simulations employing advective CR transport with varying Xcr,0. From left to right, we show density fluctuations, cold mass fraction, cold volume fraction, and cold mass flux. We evaluate each computational cell in the region 0.2…
Figure 6
Figure 6. Figure 6: Gallery showing 2D slices of various quantities from the simulations employing 2-moment CR transport with an initial CR pressure fraction of Xcr,0 = 3. All snapshots are taken at t = 7 tcool. The top row illustrates quantities of the thermal gas, while the bottom row p…
Figure 7
Figure 7. Figure 7: Slices showing the mass density in the x − z plane centred at y = 0 of simulations employing 2-moment CR transport and different relative CR pressure, Xcr,0. All snapshots are analysed at t = 7 tcool. The initial CR pressure barely influences the morphology of the cold…
Figure 8
Figure 8. Figure 8: Time evolution of cold gas metrics for simulations employing 2-moment CR transport with varying Xcr,0. From left to right, we show density fluctuations, cold mass fraction, cold volume fraction, and cold mass flux. We evaluate each computational cell in the region 0.25…
Figure 9
Figure 9. Figure 9: Intrinsic CR diffusion coefficient, κcr, for simulations utilizing 2- moment CR transport with varying initial values of Xcr,0. Scatter points represent the distribution of CRs as a function of κcr and Xcr at t = 6 tcool, with darker colours indicating higher CR-energy…
Figure 10
Figure 10. Figure 10: Same as [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: Projections of the hydrogen column density, NH, for simulations utilizing different CR transport models. All runs are initialized with Xcr,0 = 3 and the snapshots are taken at t = 6 tcool. The figure demonstrates that the onset of collapse is governed by the CR transp…
Figure 12
Figure 12. Figure 12: Time evolution of cold gas metrics for simulations with different CR transport models and Xcr,0 = 3. Metrics are evaluated for compu￾tational cells within 0.25 H ≤ |z| ≤ 2.75 H. The dashed vertical line marks the time corresponding to the snapshot shown in [PITH_FULL…
Figure 13
Figure 13. Figure 13: Ratios of the CR transport timescale to the cloud collapse timescale (left) and the relative CR pressure within cold clouds (right), depicted for various cloud radii. The 1D probability density of the corresponding variable is displayed above each axis with a linear s…
Figure 14
Figure 14. Figure 14: Visual impression of the magnetic field topology in and around one of the clouds. This cloud has formed through TI and is currently falling down. The volume rendering highlights dense gas and the mag￾netic field lines are coloured according to the local magnetic field…
Figure 15
Figure 15. Figure 15: Mass-weighted nH-T diagrams from our simulation suite. Columns represent different CR transport methods, while rows correspond to varying initial CR pressure fractions, Xcr,0. The 1D probability densities of the respective variables are shown above each axis: columns …
Figure 16
Figure 16. Figure 16: Projection showing the hydrogen column density, NH, after the simulations run for 8 tcool. From left to right, we plot the results from simulations with increasing resolution where each simulation employs 2-moment CR transport and is initialized with Xcr,0 = 3. The si…
Figure 17
Figure 17. Figure 17: Same as [PITH_FULL_IMAGE:figures/full_fig_p018_17.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cosmic ray heating of cold streams: Implications for the gas supply and growth of massive galaxies

    astro-ph.GA 2026-07 conditional novelty 7.0 of 10

    Externally entrained cosmic-web CRs weakly heat dense cold-stream cores but can strongly heat diffuse and mixed interface gas in massive haloes, adding selectivity to cold accretion.

  2. CRexit observed: probing cosmic ray transport in the circumgalactic medium with absorption line spectra

    astro-ph.GA 2026-07 conditional novelty 6.5 of 10

    Efficient cosmic-ray transport in CR-pressure-dominated CGM simulations produces stronger cool-gas absorption (MgII, SiII) and covering fractions matching star-forming galaxies, while slow transport underproduces them.

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