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Misty, patchy, and turbulent: constraining the cool circumgalactic medium with mCC

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

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Pith's one-line read The cool circumgalactic medium around Milky Way-like galaxies can be described as roughly 1,000 ten-kiloparsec cloud complexes, each a mist of tiny cloudlets, holding about 10^10 solar masses of gas.

desk verdict A useful, computationally cheap toy model for the clumpy cool CGM, but the headline mass is calibrated on the same data it claims to infer. read the letter →

arxiv 2411.17173 v3 pith:KXG6VNJJ submitted 2024-11-26 astro-ph.GA

Mukesh Singh Bisht , Prateek Sharma , Alankar Dutta , Biman B. Nath This is my paper · ORCID
classification astro-ph.GA
keywords circumgalacticmediumcloudcomplexesMgIIabsorptionquasarlinescoveringfractionmultiphasegasturbulentbroadeningcoolmass
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 argues that the ~$10^{4}$ K gas in the circumgalactic medium of Milky Way-like galaxies is not spread uniformly but is gathered into 'cloud complexes'—clumps a few to tens of kiloparsec across, each containing a mist of tiny cloudlets. Treating each complex in the mist limit (so a sightline through it always sees cool gas) and placing ~$10^{3}$ such complexes by Monte Carlo, the authors find they can reproduce the MgII column densities, equivalent widths, and covering fractions measured by the COS-Halos and M3 surveys. Their fiducial solution is a power-law radial distribution dN_CC/dR ∝ $R^{{-1}}$ with ~$10^{3}$ complexes of radius ~10 kpc and a total cool-gas mass of ~$10^{10}$ M_sun. A second result is that the product of the area-averaged MgII column density and the area covering fraction is an observationally accessible proxy for the cool CGM mass, nearly independent of how the mass is split into complexes. If correct, the model would turn large sightline-to-sightline scatter in absorption from a nuisance into a diagnostic of cloud structure.

What carries the argument

The load-bearing object is the misty cloud complex: a spherical region of radius R_CC that contains cool cloudlets dense enough that any sightline through the complex intersects cool gas (unit area covering fraction), allowing the complex to be smoothed into an average density ⟨n_gas⟩ = 3M_CC/($4πR_CC^{3}$ μ m_p). The model's second mechanism is a Kolmogorov turbulent broadening ansatz, σ_turb,CC = σ_turb,CGM (R_CC/R_CGM)^{1/3}, anchored to a hot-phase Mach number ~0.5, which sets the Doppler parameter b_tot and therefore the equivalent width from the curve of growth. The primary observable identity is ⟨N_MgII⟩ × f_CC^A ≈ const for fixed cool mass, which connects the data products to M_cool. The advanced model replaces identical complexes with power-law distributions of complex size, mass, and radius, but preserves the same mass-proxy relation.

What would settle it

Measure the line-of-sight velocity dispersion of the hot CGM around a Milky Way-mass galaxy at ~10 kpc scales with high-resolution X-ray spectroscopy; if the one-dimensional dispersion is much larger or smaller than the assumed ~20 km/s, the predicted MgII equivalent-width distribution from the fiducial mCC model will not match the observed spectra, falsifying the turbulent-broadening ansatz.

Watch

Extended reading notes

Core claim

The central discovery is that the observed clumpiness of the cool CGM can be captured by a two-level 'misty cloud complex' (mCC) model: within each complex, tiny cloudlets are so numerous that the area covering fraction is unity, while the complexes themselves cover only a small fraction of the CGM volume. Monte Carlo realizations with $10^{3}$ complexes of radius 10 kpc, a power-law radial distribution with index α=1, and total cool mass $10^{10}$ M_sun match the MgII column density and equivalent-width trends with impact parameter in the COS-Halos sample and the covering fraction data of Huang et al. 2021. The authors further show that ⟨N_MgII⟩ × f_CC^A, the average column density times the area covering fraction, is nearly constant for a fixed cool-gas mass across wide variations in complex number, size, and mass, so the pair of observables can be used to weigh the cool CGM. Directly placing parsec-scale cloudlets inside complexes shows that absorption from many cloudlets blends to reproduce the same turbulent broadening that the analytic mist prescription assumes.

Load-bearing premise

The equivalent widths and covering fractions that validate the model depend on an assumed internal velocity spread for each cloud complex, estimated from hot-gas turbulence whose strength has not been directly measured; if that spread is wrong, the inferred complex size and number change.

Editorial extensions

If this is right

  • For Milky Way-like galaxies, the cool CGM mass is constrained to ~10^10 M_sun, with about 10^3 cloud complexes of radius ~10 kpc distributed with a dN/dR ∝ R^{-1} profile.
  • Large intrinsic scatter in MgII column density at fixed impact parameter is a natural prediction of the patchy CC distribution, not an observational artifact.
  • The product of average MgII column density and covering fraction can be used as a mass estimator for cool CGM without needing to know the detailed cloud geometry.
  • Spectra toward a single complex should show blended absorption that mimics turbulent broadening even if individual cloudlets are tiny, implying that resolved components do not directly map to 3D structures.
  • The same machinery applied to OVI reproduces the COS-Halos OVI column spread, suggesting the CC picture extends to warm gas.

Reading between the lines

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

  • Future surveys with many quasar–galaxy pairs could map cool CGM mass as a function of stellar mass and environment simply by binning average column density and covering fraction without velocity-resolved fits; this extension is not developed in the paper.
  • The turbulent broadening ansatz is the least protected link: a direct measurement of hot CGM turbulence around Milky Way-mass halos would either validate the ~20 km/s CC-scale dispersion or require re-fitting the CC size and number, since EW and column density are degenerate there.
  • The two-level mist picture suggests that unresolved multiphase gas in cosmological simulations could be represented statistically by CCs rather than resolved cloudlets, a subgrid scheme the paper mentions but does not implement.
  • The same ⟨N⟩ × f_cov proxy could in principle be applied to FRB dispersion measures to cross-check the cool-mass estimate against the total electron column, since dispersion measure is insensitive to metallicity and ionization corrections.
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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 introduces the 'mCC' (misty cloud complex) model, in which the cool (~10^4 K) circumgalactic medium around L* galaxies is not a uniform mist but is concentrated in ~10^3 clumpy complexes of radius ~10 kpc, each internally in the mist limit with a large number of tiny cloudlets. Analytic expressions are derived for the mean MgII column density and its variance along lines of sight for uniform and power-law radial distributions of complexes, and Monte Carlo realizations are used to generate predicted distributions of column density, equivalent width, and covering fraction. Comparing with COS-Halos and M3/Huang et al. data, the authors conclude that M_cool ~ 10^10 M_sun, N_CC ~ 10^3, R_CC ~ 10 kpc, and alpha = 1 reproduce the observed trends; they further propose that the product of area-averaged MgII column density and area covering fraction is a robust mass proxy. The paper also presents an 'advanced' model with distributed complex sizes and masses, direct cloudlet-level modeling over the full CGM, and an analogous OVI prediction for warm gas.

Significance. If validated, the mCC framework would be a useful and computationally cheap bridge between analytic misty-CGM models and cloudlet-resolved models such as CloudFlex. The paper's strengths include publicly released code and data, closed-form analytic expressions (Eqs. 6 and 9, and the variance estimate in Appendix C), a careful Monte Carlo description, a concrete mist-limit criterion (Appendix A), and an explicit demonstration that line blending of small cloudlets converges to the misty-CC absorption profile (Section 4). The main physical claims, however, rest on two load-bearing choices that the manuscript itself flags as uncertain: the turbulent broadening ansatz of Section 3.4.2, which has no direct observational calibration for L* halos, and the calibration of the fiducial model on the same COS-Halos data that are later used to 'infer' the cool CGM mass. These issues do not invalidate the framework as a phenomenological tool, but they currently prevent the paper from delivering a robust quantitative constraint on the cool CGM mass.

major comments (4)
  1. [Section 3.4.2 and Section 3.6] The turbulent broadening across a cloud complex is the main link between the model's column densities and the observed equivalent widths, yet it is set by an unvalidated ansatz: a hot-phase Mach number of 0.5 at ~2x10^6 K, scaled to the CC scale with a Kolmogorov factor. Footnote 6 concedes that no direct observations of hot CGM turbulence in Milky Way-mass halos exist, and the only empirical anchor cited (Hitomi) gives ICM Mach ~0.2. Because MgII column densities near 10^13 cm^-2 lie on the flat part of the curve of growth (Fig. 3), the predicted EW and the EW-threshold covering fraction (Fig. 6, bottom panel; also the recommendation in Section 6.3.1) depend directly on the adopted broadening. If the true hot-phase Mach number were 0.2 instead of 0.5, the 1D turbulent dispersion across a 10 kpc CC would drop from 20 to ~8 km/s and b_turb,CC from ~28 to ~11 km/s; the saturated-component EWs would shrink and the EW>0.3 A covering fraction would fall, so matching the observed covering fraction would require a different M_cool and/or different CC parameters. The claimed ~10^10 M_sun mass and the statement that the model 'reproduces' the observations are therefore contingent on a factor-of-2.5 uncertainty in the broadening parameter that is not propagated into the quoted constraint.
  2. [Section 3.5 and Section 3.7] The inference of M_cool ~ 10^10 M_sun is circular as presented. The fiducial mass is selected in the top panels of Fig. 5 by matching the COS-Halos column density distribution, and the same COS-Halos data are then placed on the mass-proxy diagram of Fig. 7 to conclude that the cool CGM mass is ~10^10 M_sun. The green point in Fig. 7 thus confirms a value that was already built into the model through Fig. 5, not an independent measurement. The mass-proxy relation itself is a model prediction, but its application to COS-Halos is not a test. To support the title's 'constraining' claim, the model should be calibrated on one sample (or on a subset of sightlines) and validated on an independent one (for example, the M3/Huang et al. EW data, or a different ion such as OVI with an independent mass estimate), or the manuscript should be reframed as a demonstration of the framework rather than a measurement of the cool CGM mass.
  3. [Section 3.5 and Fig. 5] The claim that the fiducial parameter set 'best matches' or 'most consistent with' the COS-Halos data is based on qualitative, by-eye comparison of scatter plots with highly heterogeneous data (detections, upper limits, and lower limits). No quantitative goodness-of-fit statistic, likelihood, or sensitivity of the visual ranking to the adopted binning is provided. Given the large intrinsic scatter and the presence of censored measurements, a quantitative comparison (for instance, a two-dimensional Anderson-Darling or KS test with proper handling of limits, or a likelihood over the impact-parameter distribution) is needed before 'reproduces' can be taken as a rigorous claim. This is fixable and would materially strengthen the paper's central conclusion.
  4. [Section 3.7 and Section 6.3.1] The advertised robustness of the mass proxy is overstated. Fig. 7 shows that the product of the detection-averaged column density and the covering fraction is approximately constant for different N_CC and R_CC at fixed M_cool, which is a useful degeneracy-breaking result. However, the product is not independent of all other model parameters: it depends on the assumed cool-gas density normalization (Eq. 13), metallicity (0.3 Z_sun), and the MgII ion fraction model (Appendix B), all of which are fixed assumptions rather than fitted quantities. Moreover, Section 6.3.1 recommends defining the covering fraction with an EW threshold of 0.3 A, which reintroduces the dependence on the turbulent broadening ansatz discussed above. The text should state explicitly that the proxy is independent only of the CC configuration parameters (N_CC, R_CC, alpha) conditional on the assumed gas physics, and that the EW-threshold version is sensitive to b_turb.
minor comments (5)
  1. [Section 3.4.2] The notation for the turbulent broadening parameter is confusing: the text gives sigma_3D,turb,CC = 35 km/s, then sigma_1D = 20 km/s, and then defines b_turb,CC = sqrt(2) sigma_1D, which gives b_turb,CC ~ 28 km/s, not 20 km/s. Please define a single symbol for the Doppler broadening parameter and use it consistently in Figs. 3 and 4 and in the text.
  2. [Fig. 2, bottom panel] The dotted lines representing the expected standard deviation around the mean column density are not labeled in the legend; please add an explicit legend entry and state in the caption whether the spread is computed from Eq. C2 or from the Monte Carlo realizations.
  3. [Section 4] The cloudlet-generation cylinder in Section 4 has a height of 20 kpc while the CC radius is 10 kpc; please justify why the cylinder extends beyond the CC, or state explicitly that this is a numerical convenience for the line-blending test.
  4. [Eq. (10)] The volume-fraction estimate in Eq. (10) uses n_cool = 10^-2 cm^-3, while the adopted density profile in Eq. (13) is radius-dependent and gives n_cool = 10^-3 cm^-3 at R_CGM = 280 kpc. Please clarify which density is used for the fiducial numerical values in Eq. (10).
  5. [Section 2] The description of the M3 sample states a stellar mass range of 2x10^8 - 4x10^11 M_sun with median 4x10^10 M_sun, but the comparison in Figs. 6 and 12 treats the sample as comparable to COS-Halos; a brief statement on the stellar-mass and redshift matching between the two samples would help the reader assess the validity of the joint comparison.

Circularity Check

1 steps flagged · score 6.0 of 10

The kinematic 'verification' of the misty CC turbulent broadening in §4 is tautological: cloudlet velocities are drawn from the same σ_turb,CC that defines b_turb,CC, so the emergent broadening is input, not prediction.

  1. self definitional [Section 4 (line blending with smaller cloudlets) vs Section 3.4.2]
    "These assumptions give σ3Dturb,CGM≈107 km/s. For RCC=10 kpc and RCGM=280 kpc, we get σ3Dturb,CC=35 km/s... σturb,CC=σ3Dturb,CC/√3=20 km/s. In §4: 'The velocity field has zero mean and standard deviation σ3Dturb,CC=σ3Dturb,CGM×(RCC/RCGM)^{1/3}=35 km/s ... estimated in the previous section.' ... 'This signifies that the turbulent velocity of individual tiny cloudlets results in the turbulent broadening across a CC, well modeled by our misty CC ansatz.'"

    The cloudlet velocities in §4 are drawn from a Kolmogorov field whose standard deviation is set to the very σ3D,turb,CC=35 km/s used in §3.4.2 to define b_turb,CC=20 km/s. With enough cloudlets, the blended absorption profile must have a width equal to that input velocity dispersion, so matching the misty CC profile is tautological. The test therefore does not validate the turbulent-broadening ansatz; it re-inserts it. Because EW and EW-threshold covering fraction depend on b_turb, the ansatz remains unvalidated by an external constraint, and the 'convergence' claim in the abstract is an input-output identity.

full rationale

Most of the paper is a legitimate model-fitting exercise: M_cool, N_CC, R_CC, and α are constrained by matching COS-Halos column densities and EWs, and the Fig. 7 mass proxy is a mass-conservation relation whose contours are independent of R_CC and N_CC; the Huang+21 covering fraction provides a partially independent check. I find no circularity in those steps. The one genuinely circular step is the §4 'verification' of the misty-CC turbulent broadening. The cloudlet velocity field is generated with the identical σ3D,turb,CC=35 km/s that defines b_turb,CC=20 km/s in the ansatz, so the blended profile is guaranteed to reproduce the misty-CC profile in the many-cloudlet limit. This is input-output identity, not an emergent result. The score is 6 because one advertised prediction reduces by construction, while the rest of the derivation is self-contained.

Assumptions & free parameters 9 free parameters · 5 assumptions · 0 invented entities

The model introduces no new physical entities; cloud complexes are a phenomenological grouping of known cool gas, adopted from prior work. The main free parameters are the geometric and mass parameters of the CC distribution plus the turbulent velocity normalization, all of which are set by hand or by matching observations.

free parameters (9)
  • R_CGM = 280 kpc
    Chosen CGM boundary for a Milky Way-like halo; not fitted to data but sets the integration volume.
  • M_cool = 1e10 M_sun
    Total cool gas mass; fiducial value chosen to match COS-Halos column density distributions.
  • N_CC = 1e3
    Number of cloud complexes; fiducial value chosen to match observed scatter and covering fraction.
  • R_CC = 10 kpc
    Cloud complex radius; chosen to match coherence length estimates and the observed column density levels.
  • alpha = 1
    Power-law index of the CC radial distribution; fiducial value selected because it best matches the mean MgII column density trend.
  • sigma_3D,turb,CGM = 107 km/s
    Assumed 3D turbulent velocity dispersion of the hot CGM, derived from Mach 0.5 at 2e6 K; sets the turbulent broadening of CCs.
  • n_cool normalization = 1e-3 cm^-3 at R_CGM, slope -1
    Physical density profile of cool gas, adopted from Stern et al. 2016; directly sets the MgII ion fraction profile.
  • Z = 0.3 Z_sun
    Assumed constant metallicity for the cool CGM, following Prochaska et al. 2017.
  • f_MgII = 0.2
    Used as a constant ion fraction in the analytic estimate (Eq. 14) for the contour plot; in the full Monte Carlo the ion fraction varies with density via CLOUDY.
assumptions (5)
  • domain assumption Cool gas in the CGM is confined to cloud complexes (CCs) with a unit area covering fraction within each CC (mist limit).
    Justified by the observed unity area covering fraction and small volume fraction, but it is a structural assumption of the model (Section 3).
  • domain assumption Turbulence cascades from the CGM scale to CC scales via Kolmogorov scaling with no energy loss.
    Invoked in Section 3.4.2 to relate the turbulent velocity dispersion of a CC to the global CGM turbulence.
  • domain assumption The cool gas density profile follows n_cool ∝ R^{-1}.
    Adopted from Stern et al. 2016 and used to compute ionization fractions as a function of radius (Section 3.2).
  • domain assumption The MgII ion fraction is computed in photoionization equilibrium at T = 10^4 K with the KS18 background and 0.3 solar metallicity.
    Used throughout to convert cool gas density to MgII column density; the choice of background and metallicity affects the normalization (Appendix B).
  • domain assumption The CGM and cloud complexes are spherically symmetric.
    Used in the analytical estimates and Monte Carlo setup; real CGM geometries may be aspherical, but this simplifies the model (Sections 3.1, 3.4).

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Cite this review

Pith. "Pith review of Misty, patchy, and turbulent: constraining the cool circumgalactic medium with mCC." pith.science (2026). https://pith.science/paper/KXG6VNJJ

@misc{pith2026241117173,
  author       = {Pith},
  title        = {Pith review of: Misty, patchy, and turbulent: constraining the cool circumgalactic medium with mCC},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KXG6VNJJ}},
  note         = {Machine review of arXiv:2411.17173}
}
abstract

The circumgalactic medium (CGM) is the largest baryon reservoir around galaxies, but its extent, mass, and temperature distribution remain uncertain. We propose that cool gas ($\sim 10^4$ K) in the CGM resides in clumpy structures referred to as cloud complexes (CCs) rather than uniformly filling the entire CGM volume. Each CC contains a mist of tiny cool cloudlets dispersed in a warm/hot medium ($\sim 10^5$ - $10^6$~K). Modeling CCs in the mist limit (unit area covering fraction within a CC) simplifies the calculation of observables like ion absorption columns, equivalent widths, compared to modeling individual cloudlets from first principles. Through Monte Carlo realizations of CCs, we explore how CC properties affect the observed variation in observables. We find that a power-law distribution of CCs ($dN_{\rm CC}/dR \propto R^{-1}$) with a total of $\sim 10^3$ CCs each with a radius of $\sim 10$ kpc and total cool gas mass of $\sim 10^{10} M_\odot$ reproduces MgII column density and equivalent width distribution trends with impact parameter for the COS-Halos sample (Werk+ 2013). We further show that the area-averaged MgII column density, combined with the area covering fraction, provides a robust proxy for estimating the cool CGM mass, independent of other model parameters. Modeling a larger number of (smaller size) cloudlets within a CC shows that line blending from individual cloudlets results in turbulent broadening on the CC scale. This work presents a practical framework for linking CGM models with observations of a multiphase CGM, illuminating the distribution of cool gas in galaxy halos.

Figures

Figures reproduced from arXiv: 2411.17173 by the authors.

Figure 1
Figure 1. The line of sight (LOS) projected distribution of 103 CCs each of radius 10 kpc in the CGM of radius 280 kpc with a power-law CC distribution of index 𝛼 = 1 (Eq. 7). Notice that there are numerous empty regions towards the outer regions of CGM and comparatively fewer empty regions in the center. The LOSs passing through CGM outskirts will, therefore, not produce strong ion absorption in contrast with the central sig… view at source ↗
Figure 2
Figure 2. The top panel shows the mean MgII column density as a function of normalized impact parameter. The black line shows the baseline column density from D24. The blue line shows the mean MgII column density for uniform distribution, while magenta and orange lines show the same for power-law distribution of CCs in the CGM with power-law index of 1 and 2 respectively (Eqs. 6, 9). The baseline column density predicted by D… view at source ↗
Figure 3
Figure 3. Variation in MgII EW with MgII column density. The black lines show the EW for various 𝑏turb values. The solid blue, red, and magenta lines show the analytical relation between EW and column density in the optically thin limit, and in the flat and damped portions of the curve of growth. These analytic relations are from Draine 2011 (see Chapter 9). We assume thermal broadening at 𝑇 = 104 K for MgII, which gives 𝑏the… view at source ↗
Figures from the paper (10 more)
Figure 5
Figure 5. Figure 5: Variation in the MgII column density along 104 sightlines with the cool gas mass (top panels), and number and radius of CCs (bottom 3 × 3 panels) for a power-law (𝛼 = 1) distribution of CCs in the CGM. Only non-zero values are shown with circles, resulting in a smaller…
Figure 7
Figure 7. Figure 7: The average MgII column density as a function of the area covering fraction of CCs for various combinations of the cool gas mass, 𝑁CC and 𝑅CC (see [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 6
Figure 6. Figure 6: The top panel shows the MgII EW distribution as a function of the normalized impact parameter for our fiducial mCC model. The grey points (note that empty sightlines are not shown) show the total MgII EW as a function of the impact parameter for 104 sightlines. The bla…
Figure 8
Figure 8. Figure 8: The grey and blue points show the MgII column density for the ‘basic’ and ‘advanced’ (considering the size, mass, and realistic radial distribution of CCs in the CGM) models, respectively, along a total of 104 sightlines (empty sightlines are not shown). The solid line…
Figure 9
Figure 9. Figure 9: shows the impact of smaller cloudlets and, therefore, a larger number of cloudlets in a CC on the overall absorption profile along a LOS. Using total broadening (thermal and turbulent), MgII column density and LOS velocity (𝑧 component of velocity) of the individual in…
Figure 10
Figure 10. Figure 10: In the top panel, the lines in different colors (total 16) show the MgII absorption profile from individual cloudlets intersected along a LOS through a CC (same as top panel of [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: LOS projected distribution of CCs and cloudlets in the CGM for 𝑀cool = 1010 M⊙, 𝑁cc = 103 , and 𝛼 = 1. Due to the power-law nature of the distribution of CCs and the projection effect, more CCs are found in the central region than in the outskirts. The inset shows the…
Figure 12
Figure 12. Figure 12: The top panel shows the normalized histogram of the number of intersected cloudlets, log10 𝑁MgII, and MgII EW along 104 LOSs (empty sightlines are not shown for number of intersected cloudlets and column density although they are taken into account for normalization).…
Figure 13
Figure 13. Figure 13: This figure shows the histograms of the number of intersected cloudlets, MgII column density, MgII EW, and MgII covering fraction (same as the top left panels in [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Column density distribution of OVI along 104 sightlines, as predicted by our fiducial CC model with a simple prescription for warm gas (see section 6.2). Moreover, we assume the OVI ion fraction from photo+collisional ionization equilibrium at 105.5 K, and assume simi…

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Reference graph

Works this paper leans on

89 extracted references · 10 canonical work pages · cited by 2 Pith papers

  1. [1]

    Afruni A., Fraternali F., Pezzulli G., 2019, @doi [ ] 10.1051/0004-6361/201835002 , https://ui.adsabs.harvard.edu/abs/2019A&A...625A..11A 625, A11

    doi:10.1051/0004-6361/201835002 2019

  2. [2]

    Afruni A., et al., 2023, @doi [ ] 10.1051/0004-6361/202347867 , https://ui.adsabs.harvard.edu/abs/2023A&A...680A.112A 680, A112

    doi:10.1051/0004-6361/202347867 2023

  3. [3]

    Anand A., Nelson D., Kauffmann G., 2021, @doi [ ] 10.1093/mnras/stab871 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504...65A 504, 65

    doi:10.1093/mnras/stab871 2021

  4. [4]

    K., Prochaska J

    Armillotta L., Fraternali F., Werk J. K., Prochaska J. X., Marinacci F., 2017, @doi [ ] 10.1093/mnras/stx1239 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470..114A 470, 114

    doi:10.1093/mnras/stx1239 2017

  5. [5]

    J., Scott P., 2009, @doi [ ] 10.1146/annurev.astro.46.060407.145222 , https://ui.adsabs.harvard.edu/abs/2009ARA&A..47..481A 47, 481

    Asplund M., Grevesse N., Sauval A. J., Scott P., 2009, @doi [ ] 10.1146/annurev.astro.46.060407.145222 , https://ui.adsabs.harvard.edu/abs/2009ARA&A..47..481A 47, 481

    arXiv 2009

  6. [6]

    Augustin R., P \'e roux C., Hamanowicz A., Kulkarni V., Rahmani H., Zanella A., 2021, @doi [ ] 10.1093/mnras/stab1673 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.6195A 505, 6195

    doi:10.1093/mnras/stab1673 2021

  7. [7]

    arXiv:2501.06551

    Augustin R., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.06551 , https://ui.adsabs.harvard.edu/abs/2025arXiv250106551A p. arXiv:2501.06551

    arXiv doi:10.48550/arxiv.2501.06551 2025

  8. [8]

    N., Bagla J

    Bera A., Kanekar N., Chengalur J. N., Bagla J. S., 2023, @doi [ ] 10.3847/2041-8213/acf71a , https://ui.adsabs.harvard.edu/abs/2023ApJ...956L..15B 956, L15

    work page doi:10.3847/2041-8213/acf71a 2023

Show all 89 references
  1. [9]

    Bordoloi R., et al., 2011, @doi [ ] 10.1088/0004-637X/743/1/10 , https://ui.adsabs.harvard.edu/abs/2011ApJ...743...10B 743, 10

    2011 doi

  2. [10]

    N., Hodges-Kluck E., Qu Z., Pratt C., Li J.-T., Yun Y., 2022, @doi [ ] 10.3847/1538-4357/ac51de , https://ui.adsabs.harvard.edu/abs/2022ApJ...928...14B 928, 14

    Bregman J. N., Hodges-Kluck E., Qu Z., Pratt C., Li J.-T., Yun Y., 2022, @doi [ ] 10.3847/1538-4357/ac51de , https://ui.adsabs.harvard.edu/abs/2022ApJ...928...14B 928, 14

    2022 doi

  3. [11]

    N., Rubin K

    Burchett J. N., Rubin K. H. R., Prochaska J. X., Coil A. L., Vaught R. R., Hennawi J. F., 2021, @doi [ ] 10.3847/1538-4357/abd4e0 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909..151B 909, 151

    2021 doi

  4. [12]

    S., Fielding D

    Butsky I. S., Fielding D. B., Hayward C. C., Hummels C. B., Quinn T. R., Werk J. K., 2020, @doi [ ] 10.3847/1538-4357/abbad2 , https://ui.adsabs.harvard.edu/abs/2020ApJ...903...77B 903, 77

    2020 doi

  5. [13]

    S., Hummels C

    Butsky I. S., Hummels C. B., Hopkins P. F., Quinn T. R., Werk J. K., 2024, @doi [ ] 10.1093/mnras/stae2459 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.535.1672B 535, 1672

    2024 doi

  6. [14]

    C., Ding J., Zonak S

    Charlton J. C., Ding J., Zonak S. G., Churchill C. W., Bond N. A., Rigby J. R., 2003, @doi [ ] 10.1086/374353 , https://ui.adsabs.harvard.edu/abs/2003ApJ...589..111C 589, 111

    2003 doi

  7. [15]

    L., Gauthier J.-R., Helsby J

    Chen H.-W., Wild V., Tinker J. L., Gauthier J.-R., Helsby J. E., Shectman S. A., Thompson I. B., 2010, @doi [ ] 10.1088/2041-8205/724/2/L176 , https://ui.adsabs.harvard.edu/abs/2010ApJ...724L.176C 724, L176

    2010 doi

  8. [16]

    Chen H.-W., et al., 2023, @doi [ ] 10.3847/2041-8213/acf85b , https://ui.adsabs.harvard.edu/abs/2023ApJ...955L..25C 955, L25

    2023 doi

  9. [17]

    C., et al., 2024, @doi [ ] 10.3847/1538-4357/ad1406 , https://ui.adsabs.harvard.edu/abs/2024ApJ...962...98C 962, 98

    Chen M. C., et al., 2024, @doi [ ] 10.3847/1538-4357/ad1406 , https://ui.adsabs.harvard.edu/abs/2024ApJ...962...98C 962, 98

    2024 doi

  10. [18]

    Cherrey M., et al., 2025, @doi [ ] 10.1051/0004-6361/202451165 , https://ui.adsabs.harvard.edu/abs/2025A&A...694A.117C 694, A117

    2025 doi

  11. [19]

    Connor L., et al., 2025, @doi [Nature Astronomy] 10.1038/s41550-025-02566-y , https://ui.adsabs.harvard.edu/abs/2025NatAs.tmp..131C

    2025 doi

  12. [20]

    M., et al., 2023, @doi [ ] 10.3847/1538-4357/acbbd0 , https://ui.adsabs.harvard.edu/abs/2023ApJ...946...58C 946, 58

    Cook A. M., et al., 2023, @doi [ ] 10.3847/1538-4357/acbbd0 , https://ui.adsabs.harvard.edu/abs/2023ApJ...946...58C 946, 58

    2023 doi

  13. [21]

    K., Gronke M., 2024, @doi [ ] 10.1093/mnras/stad3125 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527..991D 527, 991

    Das H. K., Gronke M., 2024, @doi [ ] 10.1093/mnras/stad3125 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527..991D 527, 991

    2024 doi

  14. [22]

    Das S., Chiang Y.-K., Mathur S., 2023, @doi [ ] 10.3847/1538-4357/acd764 , https://ui.adsabs.harvard.edu/abs/2023ApJ...951..125D 951, 125

    2023 doi

  15. [23]

    Decataldo D., Shen S., Mayer L., Baumschlager B., Madau P., 2024, @doi [ ] 10.1051/0004-6361/202346972 , https://ui.adsabs.harvard.edu/abs/2024A&A...685A...8D 685, A8

    2024 doi

  16. [24]

    M., 2022, @doi [ ] 10.1016/j.physrep.2022.04.005 , https://ui.adsabs.harvard.edu/abs/2022PhR...973....1D 973, 1

    Donahue M., Voit G. M., 2022, @doi [ ] 10.1016/j.physrep.2022.04.005 , https://ui.adsabs.harvard.edu/abs/2022PhR...973....1D 973, 1

    2022 doi

  17. [25]

    T., 2011, Physics of the Interstellar and Intergalactic Medium

    Draine B. T., 2011, Physics of the Interstellar and Intergalactic Medium . Princeton University Press

    2011

  18. [26]

    Dutta R., et al., 2020, @doi [ ] 10.1093/mnras/staa3147 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.5022D 499, 5022

    2020 doi

  19. [27]

    B., Fossati M., 2024a, @doi [ ] 10.1093/mnras/stae048 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.1895D 528, 1895

    Dutta R., Acebron A., Fumagalli M., Grillo C., Caminha G. B., Fossati M., 2024a, @doi [ ] 10.1093/mnras/stae048 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.1895D 528, 1895

    doi

  20. [28]

    S., Sharma P., Ghosh R., Roy M., Nath B

    Dutta A., Bisht M. S., Sharma P., Ghosh R., Roy M., Nath B. B., 2024b, @doi [ ] 10.1093/mnras/stae977 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.5117D 531, 5117

    doi

  21. [29]

    K., 2023, @doi [ ] 10.3847/1538-4357/acf217 , https://ui.adsabs.harvard.edu/abs/2023ApJ...956...92F 956, 92

    Faerman Y., Werk J. K., 2023, @doi [ ] 10.3847/1538-4357/acf217 , https://ui.adsabs.harvard.edu/abs/2023ApJ...956...92F 956, 92

    2023 doi

  22. [30]

    D., 2025, @doi [ ] 10.3847/2041-8213/adba51 , https://ui.adsabs.harvard.edu/abs/2025ApJ...982L..30F 982, L30

    Faerman Y., Zheng Y., Oppenheimer B. D., 2025, @doi [ ] 10.3847/2041-8213/adba51 , https://ui.adsabs.harvard.edu/abs/2025ApJ...982L..30F 982, L30

    2025 doi

  23. [31]

    P., 2023, @doi [ ] 10.1146/annurev-astro-052920-125203 , https://ui.adsabs.harvard.edu/abs/2023ARA&A..61..131F 61, 131

    Faucher-Gigu \`e re C.-A., Oh S. P., 2023, @doi [ ] 10.1146/annurev-astro-052920-125203 , https://ui.adsabs.harvard.edu/abs/2023ARA&A..61..131F 61, 131

    2023 doi

  24. [32]

    J., et al., 2017, @doi [ ] 10.48550/arXiv.1705.10877 , https://ui.adsabs.harvard.edu/abs/2017RMxAA..53..385F 53, 385

    Ferland G. J., et al., 2017, @doi [ ] 10.48550/arXiv.1705.10877 , https://ui.adsabs.harvard.edu/abs/2017RMxAA..53..385F 53, 385

    2017 doi arXiv

  25. [33]

    P., Ji S., Norman C., 2022, @doi [ ] 10.1093/mnras/stab3351 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..859G 511, 859

    Gronke M., Oh S. P., Ji S., Norman C., 2022, @doi [ ] 10.1093/mnras/stab3351 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..859G 511, 859

    2022 doi

  26. [34]

    Guo Y., et al., 2023, @doi [ ] 10.1038/s41586-023-06718-w , https://ui.adsabs.harvard.edu/abs/2023Natur.624...53G 624, 53

    2023 doi

  27. [35]

    Haardt F., Madau P., 2012, @doi [ ] 10.1088/0004-637X/746/2/125 , https://ui.adsabs.harvard.edu/abs/2012ApJ...746..125H 746, 125

    2012 doi

  28. [36]

    Hitomi Collaboration et al., 2016, @doi [ ] 10.1038/nature18627 , https://ui.adsabs.harvard.edu/abs/2016Natur.535..117H 535, 117

    2016 doi

  29. [37]

    F., ed., American Institute of Physics Conference Series Vol

    Holweger H., 2001, in Wimmer-Schweingruber R. F., ed., American Institute of Physics Conference Series Vol. 598, Joint SOHO/ACE workshop ``Solar and Galactic Composition''. AIP, pp 23--30 ( @eprint arXiv astro-ph/0107426 ), @doi 10.1063/1.1433974

    2001 arXiv

  30. [38]

    H., Putman M

    Hsu W. H., Putman M. E., Heitsch F., Stanimirovi \'c S., Peek J. E. G., Clark S. E., 2011, @doi [ ] 10.1088/0004-6256/141/2/57 , https://ui.adsabs.harvard.edu/abs/2011AJ....141...57H 141, 57

    2011 doi

  31. [39]

    A., Johnson S

    Huang Y.-H., Chen H.-W., Shectman S. A., Johnson S. D., Zahedy F. S., Helsby J. E., Gauthier J.-R., Thompson I. B., 2021, @doi [ ] 10.1093/mnras/stab360 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4743H 502, 4743

    2021 doi

  32. [40]

    B., Rubin K

    Hummels C. B., Rubin K. H. R., Schneider E. E., Fielding D. B., 2024, @doi [ ] 10.3847/1538-4357/ad5965 , https://ui.adsabs.harvard.edu/abs/2024ApJ...972..148H 972, 148

    2024 doi

  33. [41]

    Khaire V., Srianand R., 2019, @doi [ ] 10.1093/mnras/stz174 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4174K 484, 4174

    2019 doi

  34. [42]

    N., 1991, @doi [Proceedings of the Royal Society of London Series A] 10.1098/rspa.1991.0075 , https://ui.adsabs.harvard.edu/abs/1991RSPSA.434....9K 434, 9

    Kolmogorov A. N., 1991, @doi [Proceedings of the Royal Society of London Series A] 10.1098/rspa.1991.0075 , https://ui.adsabs.harvard.edu/abs/1991RSPSA.434....9K 434, 9

    1991

  35. [43]

    Lan T.-W., Mo H., 2019, @doi [ ] 10.1093/mnras/stz839 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486..608L 486, 608

    2019 doi

  36. [44]

    Lehner N., et al., 2020, @doi [ ] 10.3847/1538-4357/aba49c , https://ui.adsabs.harvard.edu/abs/2020ApJ...900....9L 900, 9

    2020 doi

  37. [45]

    P., et al., 2020, @doi [ ] 10.1038/s41586-020-2300-2 , https://ui.adsabs.harvard.edu/abs/2020Natur.581..391M 581, 391

    Macquart J. P., et al., 2020, @doi [ ] 10.1038/s41586-020-2300-2 , https://ui.adsabs.harvard.edu/abs/2020Natur.581..391M 581, 391

    2020 doi

  38. [46]

    J., 2012, @doi [ ] 10.1111/j.1365-2966.2011.19972.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.419.3319M 419, 3319

    McCourt M., Sharma P., Quataert E., Parrish I. J., 2012, @doi [ ] 10.1111/j.1365-2966.2011.19972.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.419.3319M 419, 3319

    2012

  39. [47]

    K., 2018, @doi [ ] 10.3847/1538-4357/aa9d3f , https://ui.adsabs.harvard.edu/abs/2018ApJ...852...33M 852, 33

    McQuinn M., Werk J. K., 2018, @doi [ ] 10.3847/1538-4357/aa9d3f , https://ui.adsabs.harvard.edu/abs/2018ApJ...852...33M 852, 33

    2018 doi

  40. [48]

    W.H.Freeman & Co Ltd

    Mihalas D., 1978, Stellar atmospheres . W.H.Freeman & Co Ltd

    1978

  41. [49]

    Mohapatra R., Jetti M., Sharma P., Federrath C., 2022a, @doi [ ] 10.1093/mnras/stab3429 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.2327M 510, 2327

    doi

  42. [50]

    Mohapatra R., Jetti M., Sharma P., Federrath C., 2022b, @doi [ ] 10.1093/mnras/stab3603 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.3778M 510, 3778

    doi

  43. [51]

    P., Bailin J., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.16045 , https://ui.adsabs.harvard.edu/abs/2025arXiv250116045M p

    Morgan J. P., Bailin J., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.16045 , https://ui.adsabs.harvard.edu/abs/2025arXiv250116045M p. arXiv:2501.16045

    2025 doi arXiv

  44. [52]

    Nelson D., et al., 2020, @doi [ ] 10.1093/mnras/staa2419 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.2391N 498, 2391

    2020 doi

  45. [53]

    V., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.11139 , https://ui.adsabs.harvard.edu/abs/2025arXiv250311139N p

    Ng Y. V., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.11139 , https://ui.adsabs.harvard.edu/abs/2025arXiv250311139N p. arXiv:2503.11139

    2025 doi

  46. [54]

    M., Churchill C

    Nielsen N. M., Churchill C. W., Kacprzak G. G., 2013, @doi [ ] 10.1088/0004-637X/776/2/115 , https://ui.adsabs.harvard.edu/abs/2013ApJ...776..115N 776, 115

    2013 doi

  47. [55]

    M., Fisher D

    Nielsen N. M., Fisher D. B., Kacprzak G. G., Chisholm J., Martin D. C., Reichardt Chu B., Sandstrom K. M., Rickards Vaught R. J., 2024, @doi [Nature Astronomy] 10.1038/s41550-024-02365-x

    2024 doi

  48. [56]

    Pessa I., et al., 2024, @doi [ ] 10.1051/0004-6361/202450547 , https://ui.adsabs.harvard.edu/abs/2024A&A...691A...5P 691, A5

    2024 doi

  49. [57]

    X., Zheng Y., 2019, @doi [ ] 10.1093/mnras/stz261 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485..648P 485, 648

    Prochaska J. X., Zheng Y., 2019, @doi [ ] 10.1093/mnras/stz261 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485..648P 485, 648

    2019 doi

  50. [58]

    X., et al., 2017, @doi [ ] 10.3847/1538-4357/aa6007 , https://ui.adsabs.harvard.edu/abs/2017ApJ...837..169P 837, 169

    Prochaska J. X., et al., 2017, @doi [ ] 10.3847/1538-4357/aa6007 , https://ui.adsabs.harvard.edu/abs/2017ApJ...837..169P 837, 169

    2017 doi

  51. [59]

    Qu Z., et al., 2023, @doi [ ] 10.1093/mnras/stad1886 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..512Q 524, 512

    2023 doi

  52. [60]

    Rahmani H., et al., 2018, @doi [ ] 10.1093/mnras/stx2726 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474..254R 474, 254

    2018 doi

  53. [61]

    Ramesh R., Nelson D., 2024, @doi [ ] 10.1093/mnras/stae237 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.3320R 528, 3320

    2024 doi

  54. [62]

    Ramesh R., Nelson D., Fielding D., Br \"u ggen M., 2025, @doi [ ] 10.1051/0004-6361/202451303 , https://ui.adsabs.harvard.edu/abs/2025A&A...699A..10R 699, A10

    2025 doi

  55. [63]

    M., Sardane G., Turnshek D

    Rao S. M., Sardane G., Turnshek D. A., Thilker D., Walterbos R., Vanden Berk D., York D. G., 2013, @doi [ ] 10.1093/mnras/stt417 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.432..866R 432, 866

    2013 doi

  56. [64]

    B., Faucher-Gigu \`e re C.-A., 2024, @doi [ ] 10.1093/mnras/stad3142 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527..265R 527, 265

    Roy M., Su K.-Y., Tonnesen S., Fielding D. B., Faucher-Gigu \`e re C.-A., 2024, @doi [ ] 10.1093/mnras/stad3142 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527..265R 527, 265

    2024 doi

  57. [65]

    Rubin K. H. R., Prochaska J. X., Koo D. C., Phillips A. C., 2012, @doi [ ] 10.1088/2041-8205/747/2/L26 , https://ui.adsabs.harvard.edu/abs/2012ApJ...747L..26R 747, L26

    2012 doi

  58. [66]

    Sameer et al., 2024, @doi [ ] 10.1093/mnras/stae962 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530.3827S 530, 3827

    2024 doi

  59. [67]

    D., Sembach K

    Savage B. D., Sembach K. R., 1991, @doi [ ] 10.1086/170498 , https://ui.adsabs.harvard.edu/abs/1991ApJ...379..245S 379, 245

    1991 doi

  60. [68]

    Schaan E., et al., 2021, @doi [ ] 10.1103/PhysRevD.103.063513 , https://ui.adsabs.harvard.edu/abs/2021PhRvD.103f3513S 103, 063513

    2021 doi

  61. [69]

    P., Grete P., 2021, @doi [ ] 10.1051/0004-6361/202140920 , https://ui.adsabs.harvard.edu/abs/2021A&A...654A.115S 654, A115

    Schmidt W., Schmidt J. P., Grete P., 2021, @doi [ ] 10.1051/0004-6361/202140920 , https://ui.adsabs.harvard.edu/abs/2021A&A...654A.115S 654, A115

    2021 doi

  62. [70]

    Shaban A., et al., 2025, @doi [ ] 10.3847/1538-4357/add0b9 , https://ui.adsabs.harvard.edu/abs/2025ApJ...986..190S 986, 190

    2025 doi

  63. [71]

    R., Field G

    Shapiro P. R., Field G. B., 1976, @doi [ ] 10.1086/154332 , https://ui.adsabs.harvard.edu/abs/1976ApJ...205..762S 205, 762

    1976 doi

  64. [72]

    J., Quataert E., 2010, @doi [ ] 10.1088/0004-637X/720/1/652 , https://ui.adsabs.harvard.edu/abs/2010ApJ...720..652S 720, 652

    Sharma P., Parrish I. J., Quataert E., 2010, @doi [ ] 10.1088/0004-637X/720/1/652 , https://ui.adsabs.harvard.edu/abs/2010ApJ...720..652S 720, 652

    2010 doi

  65. [73]

    J., Quataert E., 2012, @doi [ ] 10.1111/j.1365-2966.2012.22050.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.1219S 427, 1219

    Sharma P., McCourt M., Parrish I. J., Quataert E., 2012, @doi [ ] 10.1111/j.1365-2966.2012.22050.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.1219S 427, 1219

    2012

  66. [74]

    B., Majumdar S., Silk J., 2015, @doi [ ] 10.1093/mnras/stv155 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.2384S 448, 2384

    Singh P., Nath B. B., Majumdar S., Silk J., 2015, @doi [ ] 10.1093/mnras/stv155 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.2384S 448, 2384

    2015 doi

  67. [75]

    Srianand R., Khare P., 1993, @doi [ ] 10.1086/173018 , https://ui.adsabs.harvard.edu/abs/1993ApJ...413..486S 413, 486

    1993 doi

  68. [76]

    F., Prochaska J

    Stern J., Hennawi J. F., Prochaska J. X., Werk J. K., 2016, @doi [ ] 10.3847/0004-637X/830/2/87 , https://ui.adsabs.harvard.edu/abs/2016ApJ...830...87S 830, 87

    2016 doi

  69. [77]

    B., 2024, @doi [ ] 10.1093/mnras/stad3793 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.9683T 527, 9683

    Tan B., Fielding D. B., 2024, @doi [ ] 10.1093/mnras/stad3793 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.9683T 527, 9683

    2024 doi

  70. [78]

    S., Werk J

    Tumlinson J., Peeples M. S., Werk J. K., 2017, @doi [ ] 10.1146/annurev-astro-091916-055240 , https://ui.adsabs.harvard.edu/abs/2017ARA&A..55..389T 55, 389

    2017 doi

  71. [79]

    M., Meece G., Li Y., O'Shea B

    Voit G. M., Meece G., Li Y., O'Shea B. W., Bryan G. L., Donahue M., 2017, @doi [ ] 10.3847/1538-4357/aa7d04 , https://ui.adsabs.harvard.edu/abs/2017ApJ...845...80V 845, 80

    2017 doi

  72. [80]

    Weinberger R., Hernquist L., 2023, @doi [ ] 10.1093/mnras/stac3708 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.3011W 519, 3011

    2023 doi

  73. [81]

    K., Prochaska J

    Werk J. K., Prochaska J. X., Thom C., Tumlinson J., Tripp T. M., O'Meara J. M., Meiring J. D., 2012, @doi [ ] 10.1088/0067-0049/198/1/3 , https://ui.adsabs.harvard.edu/abs/2012ApJS..198....3W 198, 3

    2012 doi

  74. [82]

    K., Prochaska J

    Werk J. K., Prochaska J. X., Thom C., Tumlinson J., Tripp T. M., O'Meara J. M., Peeples M. S., 2013, @doi [ ] 10.1088/0067-0049/204/2/17 , https://ui.adsabs.harvard.edu/abs/2013ApJS..204...17W 204, 17

    2013 doi

  75. [83]

    K., et al., 2014, @doi [ ] 10.1088/0004-637X/792/1/8 , https://ui.adsabs.harvard.edu/abs/2014ApJ...792....8W 792, 8

    Werk J. K., et al., 2014, @doi [ ] 10.1088/0004-637X/792/1/8 , https://ui.adsabs.harvard.edu/abs/2014ApJ...792....8W 792, 8

    2014 doi

  76. [84]

    K., et al., 2016, @doi [ ] 10.3847/1538-4357/833/1/54 , https://ui.adsabs.harvard.edu/abs/2016ApJ...833...54W 833, 54

    Werk J. K., et al., 2016, @doi [ ] 10.3847/1538-4357/833/1/54 , https://ui.adsabs.harvard.edu/abs/2016ApJ...833...54W 833, 54

    2016 doi

  77. [85]

    N., Ji L., 2025, @doi [ ] 10.1093/mnras/staf407 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.538.1871Y 538, 1871

    Yang H., Qu Z., Bregman J. N., Ji L., 2025, @doi [ ] 10.1093/mnras/staf407 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.538.1871Y 538, 1871

    2025 doi

  78. [86]

    Zabl J., et al., 2021, @doi [ ] 10.1093/mnras/stab2165 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.4294Z 507, 4294

    2021 doi

  79. [87]

    Zhang H., Zaritsky D., Behroozi P., Werk J., 2019, @doi [ ] 10.3847/1538-4357/ab2761 , https://ui.adsabs.harvard.edu/abs/2019ApJ...880...28Z 880, 28

    2019 doi

  80. [88]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

  81. [89]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.