Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

A Neural Network Model for the Cosmic Dispersion Measure in the CAMELS Simulations

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read A neural network trained on the full CAMELS simulation suite emulates FRB dispersion-measure distributions and reveals that the scatter parameter F depends non-monotonically on supernova and AGN feedback strength.

desk verdict A useful but incremental emulator paper whose headline F result rests on an unvalidated independence assumption in the sightline construction. read the letter →

arxiv 2501.16709 v1 pith:URN3HLTO submitted 2025-01-28 astro-ph.CO astro-ph.GA

Qi Guo (1) , Khee-Gan Lee (1 , 2)((1) Kavli IPMU , (2) CD3 , IPMU) This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords fastradioburstsdispersionmeasureCAMELSsimulationsgalaxyfeedbackneuralnetworkemulatorMacquartrelationFparametercosmological
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 a neural network trained on the full Latin Hypercube of the CAMELS simulation suite can emulate the probability distribution of cosmic dispersion measures (DM) at any redshift up to z = 1, as a function of the cosmological and feedback parameters that control the simulations. Using this emulator, the authors study the parameter F = σ_DM z^(1/2), the standard summary of the scatter in p(DM), and find that F does not respond monotonically to any single feedback knob: the joint state of supernova and AGN feedback sets the effective scatter. They also find that the largest F values in the CAMELS parameter space fall short of current observational constraints on F from fast radio bursts, which they attribute to the small simulation boxes missing the most extreme overdensities and voids. The intended payoff is a tool that turns the thousands of CAMELS simulations into a continuous map from feedback parameters to FRB dispersion statistics, usable for future inference with larger simulation suites.

What carries the argument

The machinery is a feedforward neural network with five to fifteen hidden layers and fifteen to thirty-five neurons per layer, rectified-linear activations, mean-square-error loss, and hyperparameters chosen by an automatic search; its input is the parameter vector (Ωm, σ8, A_SN1, A_AGN1, A_SN2, A_AGN2) together with redshift z, and its output is the set (⟨DM⟩, σ_DM, α, β) that describes p(DM) through a quasi-universal fitting function. Each snapshot's p(DM) is first compressed into those four numbers by a Markov-chain fit, so the network never sees the raw histograms. To reach redshifts beyond a single box, the paper assembles sightlines by summing 100 h^(-1) Mpc segments whose DM values are drawn independently from CDFs linearly interpolated in redshift between snapshots; this is the assumption that ultimately sets σ_DM. The scalar that carries the astrophysical claim is F = σ_DM z^(1/2), the redshift-scaled scatter, which is also the quantity compared with observed FRB samples.

What would settle it

Ray-trace a continuous sightline through a sequence of concatenated CAMELS boxes (or through a larger-volume counterpart such as TNG-300) out to z = 1, measure the resulting p(DM), and compare its σ_DM with the CDF-sampling method: if the independently-sampled variance differs from the directly-simulated variance at the level that would change F by more than the network's test-set error, the paper's core assumption is falsified.

Watch

Extended reading notes

Core claim

The central claim is that the effect of galaxy feedback on the FRB dispersion-measure distribution p(DM) can be captured by a feedforward neural network whose inputs are the six CAMELS parameters plus redshift, and whose outputs are four summary parameters of a standard fitting form for p(DM): the mean DM, the scatter σ_DM, and two shape parameters. With the network trained on the thousand Latin Hypercube realizations for both the SIMBA and TNG variants of CAMELS, the paper reports good predictive performance on held-out boxes. From the network's predicted σ_DM it computes F = σ_DM z^(1/2) over a dense grid of feedback parameters, and finds that F has interior extrema: in the SIMBA variant the maximal F occurs at low supernova and quasar-mode AGN strength with relatively strong jet-mode AGN, while in the TNG variant the maximum occurs at maximal supernova wind energy; setting every feedback parameter to its maximum does not maximize F, and some one-dimensional trends reverse direction relative to earlier one-at-a-time analyses. The paper interprets these non-monotonicities as the joint action of feedback channels (for instance, supernova feedback suppressing black-hole growth and thus weakening the effective AGN feedback), and it cautions that the low overall F values compared with the observed value reflect the limited dynamic range of the 25 h^(-1) Mpc boxes.

Load-bearing premise

The construction of long sightlines assumes that the DM contribution of each successive 100 h^(-1) Mpc segment is an independent random draw from the redshift-interpolated CDF, so any correlation in gas density between adjacent segments is ignored; if that independence fails, the scatter σ_DM and therefore the F parameter would be systematically wrong.

Editorial extensions

If this is right

  • With the trained emulator, p(DM) at any redshift up to z = 1 and any combination of the six simulation parameters is available without running a new hydrodynamical simulation.
  • The non-monotonicity of F in the feedback parameters implies that a single measured F value cannot be mapped uniquely back to a feedback strength; inference must treat the four feedback parameters jointly.
  • The maximal F values from the emulator are still smaller than the current observed central value, so the paper concludes that CAMELS' small boxes cannot yet be used to fit FRB DM observations directly.
  • The same emulation strategy is transferable to future CAMELS-like suites with larger boxes, which the paper identifies as the route to constraining feedback from growing FRB samples.

Reading between the lines

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

  • If the independence of adjacent 100 h^(-1) Mpc CDF draws is violated by large-scale structure coherence, the true p(DM) scatter in CAMELS volumes would differ from the paper's construction; a direct ray-trace through concatenated CAMELS boxes would settle this, and the paper's own caveat about cosmic variance suggests it is a real risk.
  • The non-monotonic F(θ) surface suggests that the shape parameters α and β, which the paper's emulator also predicts, may carry complementary information that breaks some of the degeneracies among feedback parameters; the paper does not explore this, but its own network makes it straightforward.
  • The discrepancy in mean DM at z = 1 between this work and earlier 1P analyses (by roughly 10–20% depending on the DM computation code) implies the emulator's calibration inherits a systematic from the chosen ray-tracing or gridding method; a comparison project across codes would be needed before applying the network to real FRB data.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper presents a neural-network emulator for the cosmic dispersion measure (DM) distribution in the CAMELS simulation suite, trained on the Latin Hypercube (LH) set of SIMBA and TNG simulations. The authors compress each snapshot's p(DM) using the Macquart fitting function into four parameters (⟨DM⟩, σ_DM, α, β), then train a NN to map the six CAMELS parameters plus redshift to these summary statistics. Using the trained NN, they explore the F ≡ σ_DM(Δ) z^{1/2} parameter across the feedback parameter space at fixed cosmology, reporting non-monotonic behavior and identifying F extrema at parameter combinations not present in the LH set. They compare their F values with the observational constraint from Baptista et al. (2024) and argue that CAMELS box sizes limit the achievable scatter.

Significance. If validated, the NN emulator would provide a fast and flexible tool for exploring how multidimensional feedback parameters shape the FRB DM distribution, going beyond the one-at-a-time 1P analyses. The paper is honest about several limitations, particularly the small CAMELS box size and the resulting insufficiency for direct observational comparison. A key strength is the use of the full LH suite, which allows the exploration of joint parameter dependencies. However, the central claims—especially the non-monotonic F behavior and the F extrema—rest on an unvalidated line-of-sight construction and on NN extrapolation at sparsely sampled parameter boundaries, so the results are not yet established at the level claimed.

major comments (3)
  1. [Section 2.2, Eq. (3)] The sightline construction assumes that the DM contribution at each path segment is an independent random draw from the redshift-interpolated CDF, and the paper does not test this independence assumption against directly ray-traced long sightlines or against a larger-volume simulation. Since F is defined as the standard deviation of the summed DM, any covariance between adjacent segments would directly change σ_DM and hence F. The text also leaves ambiguous whether the CDF used in Eq. (3) corresponds to a single 25 h^-1 Mpc CAMELS box or to the full 100 h^-1 Mpc segment, and how the periodic tiling of the small box is handled. This is load-bearing because the non-monotonic F trends in Figure 3 and the extrema in Table 1 are derived from this assumed covariance structure.
  2. [Section 3.3, Table 1] The F extrema in Table 1 are NN predictions at parameter combinations that are not present in the LH training set; for example, the SIMBA maximum-F point (A_SN1, A_AGN1, A_SN2, A_AGN2) = (0.26, 0.26, 1.71, 1.01) sits at the boundary of the A_SN1 and A_AGN1 ranges. With only 1000 LH points in a six-dimensional space, such corner regions are sparsely sampled, and the paper provides no validation of the NN predictions at these locations against direct simulations or against the CAMELS EX/CV sets. The central claim that these extrema are properties of the simulations rather than NN artifacts therefore needs a dedicated test.
  3. [Section 3.2, Figure 2] The authors concede that the NN predictions for α and β exhibit high scatter. Because the stated goal is to emulate p(DM), and α and β jointly control the shape of the Macquart fitting function, poor α/β performance means the NN does not reliably reproduce the full p(DM), even if ⟨DM⟩ and σ_DM are well predicted. The paper should quantify the impact of α/β scatter on the reconstructed p(DM) (e.g., by comparing predicted and true CDFs or by reporting the R^2 of the predicted p(DM)), and should state whether the F results depend only on σ_DM, which would mitigate this issue.
minor comments (4)
  1. [Eq. (5)] The definition y = e^{log(DM)}/⟨DM⟩ is just DM/⟨DM⟩, and the notation f(x) introduces an undefined variable x; the transformation to the logarithmic distribution should be written more clearly.
  2. [Section 3.3, Eq. (10)] The notation σ_DM(Δ) is used for the standard deviation of the normalized quantity Δ = DM/⟨DM⟩, while σ_DM in Eq. (4) is used for the same quantity in the Macquart fit; the paper should explicitly distinguish the normalized and unnormalized standard deviations to avoid confusion with the observational F parameter.
  3. [Section 3.1, Figure 1] Typographical issues include 'Meahwhile' in Section 2.1 and 'balck dotted line' in the Figure 4 caption; these should be corrected.
  4. [Section 3.3, Figure 3] The comparison with Medlock et al. (2024) is presented only qualitatively; showing the actual Medlock et al. curves or a quantitative measure of the difference would strengthen the discussion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the neural network is a standard emulator trained on CAMELS LH simulations and validated against held-out LH and 1P simulations, and F is a direct transform of the emulated sigma_DM rather than a fitted input.

full rationale

The paper's derivation chain is self-contained. Section 2.2 computes DM maps and p(DM) directly from CAMELS electron densities, and Eq. (3) assembles long sightlines by sampling the redshift-interpolated CDFs; this is a physical modeling assumption, not a circular reduction. Section 2.3 trains the NN on the LH set to predict summary statistics (langle DM rangle, sigma_DM, alpha, beta) and evaluates it on a held-out 5% test split (Fig. 2), with additional comparison against the separately computed 1P set (Fig. 3). F equiv sigma_DM z^{1/2} (Eq. 10) is obtained from the NN's output sigma_DM, which is itself computed directly from the simulated p(DM) rather than fitted through Eq. (4); therefore the F trends are an emulated interpolation of the simulation suite, which is the standard, non-circular use of an emulator. The comparison to Baptista et al. (2024) is an external observational benchmark, and the paper explicitly cautions that CAMELS box sizes limit the applicability of that comparison. Self-citations (Lee et al. 2022; Khrykin et al. 2024a,b) are contextual and do not carry the load-bearing argument, and no uniqueness theorem or ansatz is imported from the authors' prior work. The segment-independence assumption in Eq. (3) could affect sigma_DM and hence F, but that is a correctness and validation concern rather than an input-output identity, so it does not constitute circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The analysis rests on standard simulation and emulation assumptions, plus a specific independence assumption in the sightline construction. No new physical entities are introduced. The main free parameters are the Macquart fitting parameters and the NN hyperparameters.

free parameters (3)
  • alpha (Macquart fit) = MCMC-fitted per (z, theta)
    Free parameter in Eq. 5, fitted to each simulated p(DM). These values are then used as NN targets.
  • beta (Macquart fit) = MCMC-fitted per (z, theta)
    Free parameter in Eq. 5, fitted to each simulated p(DM). NN predictions for beta have high scatter.
  • NN hyperparameters = chosen by Optuna
    Number of layers, neurons per layer, learning rate, weight decay, and optimizer are tuned on the validation set. The final architecture is not reported.
assumptions (4)
  • domain assumption The Macquart functional form (Eq. 5) adequately describes p(DM) across the entire parameter space.
    The paper demonstrates R^2 > 0.8 for only a few random examples in Figure 1 and assumes the form holds for all z and theta when creating NN training targets.
  • domain assumption DM contributions from different 100 h^-1 Mpc path segments are independent and the CDF can be linearly interpolated between snapshots.
    Eq. 3 sums independent random CDF draws, ignoring spatial correlations between adjacent redshift slices along the same sightline. This directly sets sigma_DM and F.
  • standard math F = sigma_DM z^(1/2) is the correct summary statistic for feedback, with z-scaling from Poisson statistics.
    This definition is taken from the FRB literature (Macquart et al. 2020) and used without derivation in this paper.
  • ad hoc to paper The CAMELS LH parameter space is sampled densely enough for NN interpolation at the F-extremum locations.
    The extrema in Table 1 are at parameter combinations not directly present in the LH set; the NN is trusted to interpolate there without validation against simulations.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Neural Network Model for the Cosmic Dispersion Measure in the CAMELS Simulations." pith.science (2026). https://pith.science/paper/URN3HLTO

@misc{pith2026250116709,
  author       = {Pith},
  title        = {Pith review of: A Neural Network Model for the Cosmic Dispersion Measure in the CAMELS Simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/URN3HLTO}},
  note         = {Machine review of arXiv:2501.16709}
}
abstract

The probability distribution, $p(\mathrm{DM})$ of cosmic dispersion measures (DM) measured in fast radio bursts (FRBs) encodes information about both cosmology and galaxy feedback. In this work, we study the effect of feedback parameters in the $p(\mathrm{DM})$ calculated from the full Latin Hypercube of parameters sampled by the CAMELS hydrodynamical simulation suite, building a neural network (NN) model that performs well in emulating the effect of feedback on $p(\mathrm{DM})$ at arbitrary redshifts at $z\leq1$. Using this NN model, we further study the parameter $F\equiv \sigma_{\rm DM} \, z^{1/2}$, which is commonly used to summarize the scatter on $p(\mathrm{DM})$. We find that $F$ does not depend monotonically on every feedback parameter; instead each feedback mechanism jointly influences the final feedback strength in non-trivial ways. Even the largest values of $F$ that we find in our entire parameter space are small compared to the current constraints from observed FRB DMs by Baptista et al. 2024, pointing at the limitations of the CAMELS suite due to the small simulation box sizes. In the future, with larger box-sizes from CAMELS-like suites, similar models can be used to constrain the parameters governing galaxy feedback in the increasing observational samples of FRBs.

Figures

Figures reproduced from arXiv: 2501.16709 by the authors.

Figure 1
Figure 1. Histograms of 𝑝(log(DM) ) (blue) and MCMC fitting results (orange) of several simulations from the LH suite, for SIMBA (upper panels) and TNG (lower panels). For these eight panels, LH sets are randomly selected at a random redshift with different cosmological and astrophysical parameters 𝜃 = [Ω𝑚, 𝜎8, 𝐴SN1, 𝐴AGN1, 𝐴SN2, 𝐴AGN2 ], labeled in the second row of the legend in each panel. The orange curves show the fitted… view at source ↗
Figure 2
Figure 2. NN predictions of the parameters governing 𝑝(DM) for SIMBA (upper panels) and TNG (lower panels). These panels show the true value (obs) fitted from MCMC and predictions (pre) of NN. The green circles represent NN predictions on test set, with the Pearson r-coefficients and p values of predicted values and measured results for test sets showing in each panel. Though for 𝛽 the scatter is larger however for ⟨DM⟩, 𝜎𝐷𝑀 … view at source ↗
Figure 3
Figure 3. 𝐹 calculated from simulations in 1P set (solid lines) and predicted by our NN model (dotted lines) as a function of the feedback parameters in CAMELS for SIMBA (red) and TNG (blue). NN has predicted the trends of these curves with varying astrophysical parameters. The results are different from those computed directly from 1P in other work. On the one hand, we are using different methods to compute DM from simulatio… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: DM-𝑧 relation with astrophysical parameters giving minimum (blue line) and maximum (red line) values of F for SIMBA (upper panel) and TNG (lower panel) model, with fixed Ω𝑚 and 𝜎8. The balck dotted line is the DM- 𝑧 relation with fiducial 𝜃. The solid lines are the ⟨𝐷𝑀…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Constraining Baryonic Feedback Effects on the Matter Power Spectrum with Fast Radio Bursts

    astro-ph.CO 2025-01 conditional novelty 6.0 of 10

    FRB dispersion-measure scatter (the F-parameter) correlates with baryon spread and matter power suppression across three CAMELS simulation suites, offering a possible observational proxy for baryonic feedback.

Reference graph

Works this paper leans on

62 extracted references · 4 canonical work pages · cited by 1 Pith paper

  1. [1]

    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.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    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.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  3. [3]

    N., 2013, Journal of Improbable Astronomy, 1, 1

    Author A. N., 2013, Journal of Improbable Astronomy, 1, 1

    2013

  4. [4]

    D., 2015, Journal of Interesting Stuff, 17, 198

    Jones C. D., 2015, Journal of Interesting Stuff, 17, 198

    2015

  5. [5]

    B., 2014, The Example Journal, 12, 345 (Paper I)

    Smith A. B., 2014, The Example Journal, 12, 345 (Paper I)

    2014

  6. [6]

    Ayromlou M., Nelson D., Pillepich A., 2023, @doi [ ] 10.1093/mnras/stad2046 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.5391A 524, 5391

    doi:10.1093/mnras/stad2046 2023

  7. [7]

    Baptista J., et al., 2024, @doi [ ] 10.3847/1538-4357/ad2705 , https://ui.adsabs.harvard.edu/abs/2024ApJ...965...57B 965, 57

    doi:10.3847/1538-4357/ad2705 2024

  8. [8]

    J., Duffy A

    Batten A. J., Duffy A. R., Wijers N. A., Gupta V., Flynn C., Schaye J., Ryan-Weber E., 2021, @doi [ ] 10.1093/mnras/stab1528 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.5356B 505, 5356

    doi:10.1093/mnras/stab1528 2021

Show all 62 references
  1. [9]

    J., Duffy A

    Batten A. J., Duffy A. R., Flynn C., Gupta V., Ryan-Weber E., Wijers N., 2022, @doi [ ] 10.1093/mnrasl/slac020 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.512L..49B 512, L49

    2022 doi

  2. [10]

    Bird S., Ni Y., Di Matteo T., Croft R., Feng Y., Chen N., 2022, @doi [ ] 10.1093/mnras/stac648 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.512.3703B 512, 3703

    2022 doi

  3. [11]

    Borthakur S., et al., 2016, @doi [ ] 10.3847/1538-4357/833/2/259 , https://ui.adsabs.harvard.edu/abs/2016ApJ...833..259B 833, 259

    2016 doi

  4. [12]

    CHIME/FRB Collaboration et al., 2021, @doi [ ] 10.3847/1538-4365/ac33ab , https://ui.adsabs.harvard.edu/abs/2021ApJS..257...59C 257, 59

    2021 doi

  5. [13]

    P., 2006, @doi [ ] 10.1086/506505 , https://ui.adsabs.harvard.edu/abs/2006ApJ...650..560C 650, 560

    Cen R., Ostriker J. P., 2006, @doi [ ] 10.1086/506505 , https://ui.adsabs.harvard.edu/abs/2006ApJ...650..560C 650, 560

    2006 doi

  6. [14]

    M., Chatterjee S., 2019, @doi [ ] 10.1146/annurev-astro-091918-104501 , https://ui.adsabs.harvard.edu/abs/2019ARA&A..57..417C 57, 417

    Cordes J. M., Chatterjee S., 2019, @doi [ ] 10.1146/annurev-astro-091918-104501 , https://ui.adsabs.harvard.edu/abs/2019ARA&A..57..417C 57, 417

    2019 doi

  7. [15]

    W., Shull J

    Danforth C. W., Shull J. M., 2008, @doi [ ] 10.1086/587127 , https://ui.adsabs.harvard.edu/abs/2008ApJ...679..194D 679, 194

    2008 doi

  8. [16]

    Dav \'e R., et al., 2001, @doi [ ] 10.1086/320548 , https://ui.adsabs.harvard.edu/abs/2001ApJ...552..473D 552, 473

    2001 doi

  9. [17]

    H., Appleby S., 2019, @doi [ ] 10.1093/mnras/stz937 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.2827D 486, 2827

    Dav \'e R., Angl \'e s-Alc \'a zar D., Narayanan D., Li Q., Rafieferantsoa M. H., Appleby S., 2019, @doi [ ] 10.1093/mnras/stz937 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.2827D 486, 2827

    2019 doi

  10. [18]

    M., et al., 2023, @doi [ ] 10.1093/mnras/stad2992 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.5306D 526, 5306

    Delgado A. M., et al., 2023, @doi [ ] 10.1093/mnras/stad2992 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.5306D 526, 5306

    2023 doi

  11. [19]

    Gebhardt M., et al., 2024, @doi [ ] 10.1093/mnras/stae817 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.4896G 529, 4896

    2024 doi

  12. [20]

    arXiv:2410.24072

    Glowacki M., Lee K.-G., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2410.24072 , https://ui.adsabs.harvard.edu/abs/2024arXiv241024072G p. arXiv:2410.24072

    2024 doi arXiv

  13. [21]

    arXiv:2408.12864

    Huang Y., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2408.12864 , https://ui.adsabs.harvard.edu/abs/2024arXiv240812864H p. arXiv:2408.12864

    2024 doi arXiv

  14. [22]

    B., Bryan G

    Hummels C. B., Bryan G. L., Smith B. D., Turk M. J., 2013, @doi [ ] 10.1093/mnras/sts702 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.430.1548H 430, 1548

    2013 doi

  15. [23]

    B., et al., 2019, @doi [ ] 10.3847/1538-4357/ab378f , https://ui.adsabs.harvard.edu/abs/2019ApJ...882..156H 882, 156

    Hummels C. B., et al., 2019, @doi [ ] 10.3847/1538-4357/ab378f , https://ui.adsabs.harvard.edu/abs/2019ApJ...882..156H 882, 156

    2019 doi

  16. [24]

    E., Pierfedereci F., Teuben P., eds, Astronomical Society of the Pacific Conference Series Vol

    Jankowski F., et al., 2022, in Ruiz J. E., Pierfedereci F., Teuben P., eds, Astronomical Society of the Pacific Conference Series Vol. 532, Astronomical Data Analysis Software and Systems XXX. p. 273 ( @eprint arXiv 2012.05173 ), @doi 10.48550/arXiv.2012.05173

    2022 doi arXiv

  17. [25]

    G., Churchill C

    Kacprzak G. G., Churchill C. W., Steidel C. C., Murphy M. T., 2008, @doi [ ] 10.1088/0004-6256/135/3/922 , https://ui.adsabs.harvard.edu/abs/2008AJ....135..922K 135, 922

    2008 doi

  18. [26]

    S., Sorini D., Lee K.-G., Dav \'e R., 2024a, @doi [ ] 10.1093/mnras/stae525 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529..537K 529, 537

    Khrykin I. S., Sorini D., Lee K.-G., Dav \'e R., 2024a, @doi [ ] 10.1093/mnras/stae525 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529..537K 529, 537

    doi

  19. [27]

    S., et al., 2024b, @doi [ ] 10.3847/1538-4357/ad6567 , https://ui.adsabs.harvard.edu/abs/2024ApJ...973..151K 973, 151

    Khrykin I. S., et al., 2024b, @doi [ ] 10.3847/1538-4357/ad6567 , https://ui.adsabs.harvard.edu/abs/2024ApJ...973..151K 973, 151

    doi

  20. [28]

    Kocz J., et al., 2019, @doi [ ] 10.1093/mnras/stz2219 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489..919K 489, 919

    2019 doi

  21. [29]

    Lee K.-G., et al., 2016, @doi [ ] 10.3847/0004-637X/817/2/160 , https://ui.adsabs.harvard.edu/abs/2016ApJ...817..160L 817, 160

    2016 doi

  22. [30]

    Lee K.-G., et al., 2018, @doi [ ] 10.3847/1538-4365/aace58 , https://ui.adsabs.harvard.edu/abs/2018ApJS..237...31L 237, 31

    2018 doi

  23. [31]

    S., Huang Y., Prochaska J

    Lee K.-G., Ata M., Khrykin I. S., Huang Y., Prochaska J. X., Cooke J., Zhang J., Batten A., 2022, @doi [ ] 10.3847/1538-4357/ac4f62 , https://ui.adsabs.harvard.edu/abs/2022ApJ...928....9L 928, 9

    2022 doi

  24. [32]

    E., et al., 2024, @doi [The Astrophysical Journal] 10.3847/1538-4357/ad3d4a , 968, 11

    Lee M. E., et al., 2024, @doi [The Astrophysical Journal] 10.3847/1538-4357/ad3d4a , 968, 11

    2024 doi

  25. [33]

    R., Bailes M., McLaughlin M

    Lorimer D. R., Bailes M., McLaughlin M. A., Narkevic D. J., Crawford F., 2007, @doi [Science] 10.1126/science.1147532 , https://ui.adsabs.harvard.edu/abs/2007Sci...318..777L 318, 777

    2007 doi

  26. [34]

    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

  27. [35]

    McConnell D., et al., 2016, @doi [ ] 10.1017/pasa.2016.37 , https://ui.adsabs.harvard.edu/abs/2016PASA...33...42M 33, e042

    2016 doi

  28. [36]

    Medlock I., Nagai D., Singh P., Oppenheimer B., Angl \'e s-Alc \'a zar D., Villaescusa-Navarro F., 2024, @doi [ ] 10.3847/1538-4357/ad3070 , https://ui.adsabs.harvard.edu/abs/2024ApJ...967...32M 967, 32

    2024 doi

  29. [37]

    Nan R., et al., 2011, @doi [International Journal of Modern Physics D] 10.1142/S0218271811019335 , https://ui.adsabs.harvard.edu/abs/2011IJMPD..20..989N 20, 989

    2011 doi

  30. [38]

    Nelson D., et al., 2018, @doi [ ] 10.1093/mnras/sty656 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477..450N 477, 450

    2018 doi

  31. [39]

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

    2020 doi

  32. [40]

    Ni Y., et al., 2023, @doi [ ] 10.3847/1538-4357/ad022a , https://ui.adsabs.harvard.edu/abs/2023ApJ...959..136N 959, 136

    2023 doi

  33. [41]

    Nicola A., et al., 2022, @doi [ ] 10.1088/1475-7516/2022/04/046 , https://ui.adsabs.harvard.edu/abs/2022JCAP...04..046N 2022, 046

    2022 doi

  34. [43]

    Pakmor R., Marinacci F., Springel V., 2014, @doi [ ] 10.1088/2041-8205/783/1/L20 , https://ui.adsabs.harvard.edu/abs/2014ApJ...783L..20P 783, L20

    2014 doi

  35. [44]

    Pakmor R., et al., 2023, @doi [ ] 10.1093/mnras/stac3620 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.2539P 524, 2539

    2023 doi

  36. [45]

    S., et al., 2019, @doi [ ] 10.3847/1538-4357/ab0654 , https://ui.adsabs.harvard.edu/abs/2019ApJ...873..129P 873, 129

    Peeples M. S., et al., 2019, @doi [ ] 10.3847/1538-4357/ab0654 , https://ui.adsabs.harvard.edu/abs/2019ApJ...873..129P 873, 129

    2019 doi

  37. [46]

    Pillepich A., et al., 2018, @doi [ ] 10.1093/mnras/stx2656 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.4077P 473, 4077

    2018 doi

  38. [47]

    X., Lau M

    Prochaska J. X., Lau M. W., Hennawi J. F., 2014, @doi [ ] 10.1088/0004-637X/796/2/140 , https://ui.adsabs.harvard.edu/abs/2014ApJ...796..140P 796, 140

    2014 doi

  39. [48]

    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

  40. [49]

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

    2024 doi

  41. [50]

    Rauch M., et al., 1997, @doi [ ] 10.1086/304765 , https://ui.adsabs.harvard.edu/abs/1997ApJ...489....7R 489, 7

    1997 doi

  42. [51]

    P., Katz H

    Rey M. P., Katz H. B., Cameron A. J., Devriendt J., Slyz A., 2024, @doi [ ] 10.1093/mnras/stae388 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.5412R 528, 5412

    2024 doi

  43. [52]

    R \"o ttgers B., 2018, pygad: Analyzing Gadget Simulations with Python , Astrophysics Source Code Library, record ascl:1811.014

    2018

  44. [53]

    Schaye J., et al., 2023, @doi [ ] 10.1093/mnras/stad2419 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.4978S 526, 4978

    2023 doi

  45. [54]

    A., Sargent W

    Simcoe R. A., Sargent W. L. W., Rauch M., Becker G., 2006, @doi [ ] 10.1086/498441 , https://ui.adsabs.harvard.edu/abs/2006ApJ...637..648S 637, 648

    2006 doi

  46. [55]

    Sorini D., Dav \'e R., Cui W., Appleby S., 2022, @doi [ ] 10.1093/mnras/stac2214 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516..883S 516, 883

    2022 doi

  47. [56]

    Thiele L., et al., 2022, @doi [ ] 10.1103/PhysRevD.105.083505 , https://ui.adsabs.harvard.edu/abs/2022PhRvD.105h3505T 105, 083505

    2022 doi

  48. [57]

    Thom C., Chen H.-W., 2008, @doi [ ] 10.1086/591232 , https://ui.adsabs.harvard.edu/abs/2008ApJS..179...37T 179, 37

    2008 doi

  49. [58]

    J., Smith B

    Turk M. J., Smith B. D., Oishi J. S., Skory S., Skillman S. W., Abel T., Norman M. L., 2011, @doi [ ] 10.1088/0067-0049/192/1/9 , https://ui.adsabs.harvard.edu/abs/2011ApJS..192....9T 192, 9

    2011 doi

  50. [59]

    Villaescusa-Navarro F., et al., 2021, @doi [ ] 10.3847/1538-4357/abf7ba , https://ui.adsabs.harvard.edu/abs/2021ApJ...915...71V 915, 71

    2021 doi

  51. [60]

    Walker C. R. H., Spitler L. G., Ma Y.-Z., Cheng C., Artale M. C., Hummels C. B., 2024, @doi [ ] 10.1051/0004-6361/202347139 , https://ui.adsabs.harvard.edu/abs/2024A&A...683A..71W 683, A71

    2024 doi

  52. [61]

    Weinberger R., et al., 2017, @doi [ ] 10.1093/mnras/stw2944 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.3291W 465, 3291

    2017 doi

  53. [62]

    J., Yan K., Li C

    Zhang Z. J., Yan K., Li C. M., Zhang G. Q., Wang F. Y., 2021, @doi [ ] 10.3847/1538-4357/abceb9 , https://ui.adsabs.harvard.edu/abs/2021ApJ...906...49Z 906, 49

    2021 doi

  54. [63]

    P., Schaye J., Booth C

    van Daalen M. P., Schaye J., Booth C. M., Dalla Vecchia C., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18981.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.415.3649V 415, 3649

    2011

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.