Pith. sign in

REVIEW 4 major objections 5 minor 2 cited by

Signatures of warm dark matter in the cosmological density fields extracted using Machine Learning

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · 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 Lyman-alpha simulations reconstructs the intergalactic density field and sets warm dark matter lower bounds of 3.8 and 2.2 keV at 2σ using roughly 40 times less observational data than power-spectrum fits.

desk verdict Promising reconstruction pipeline, but the WDM bounds are not yet supported because the PDF comparison treats reconstructed densities as ground truth without an end-to-end mock recovery test. read the letter →

arxiv 2411.17853 v1 pith:KTYD36FT submitted 2024-11-26 astro-ph.CO

Ander Artola , Sarah E. I. Bosman , Prakash Gaikwad , Frederick B. Davies , Fahad Nasir , Emanuele P. Farina , Klaudia Protušová , Ewald Puchwein
show 1 more author
Benedetta Spina
This is my paper · ORCID
classification astro-ph.CO
keywords Lyman-alphaforestwarmdarkmatterBayesianneuralnetworkintergalacticmediumdensityfieldcosmologicalsimulationsmachinelearninginferencethermalstate
topics Dark Matter
open problems Dark Matter
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 attempts to show that the full intergalactic density field, not just the Lyman-α flux, can be reconstructed pixel-by-pixel with a Bayesian neural network and that this reconstruction carries enough information about warm dark matter (WDM) to rival the best existing constraints. The authors train on the Sherwood-Relics simulation suite, which spans several WDM masses and IGM thermal histories, validate the network on independent Nyx simulations, and then infer WDM masses from the probability distribution function (PDF) of the reconstructed density fields. Applied to six UVES/SQUAD sightlines at $z=4.4$ and two GHOST segments at $z=4.9$, the pipeline yields $2\sigma$ lower bounds of $m_{\mathrm{WDM}} \geq 3.8$ keV and $m_{\mathrm{WDM}} \geq 2.2$ keV. The paper's headline comparison is that these bounds match current power-spectrum results with roughly 40 times less observational data. If that holds, WDM searches could be carried out with much shorter, easier-to-obtain spectra.

What carries the argument

The load-bearing object is the optical depth-weighted density contrast $\Delta_\tau$, defined as the neutral-hydrogen column-density-weighted overdensity seen through the Lyman-α absorption cross-section; the network receives a flux skewer and outputs a Gaussian mean and standard deviation for $\Delta_\tau$ at every pixel, trained with a negative log-likelihood loss. The inference step then uses the PDF of the predicted $\Delta_\tau$ field as its summary statistic: the observed PDF is compared to forward-modelled PDFs from Sherwood-Relics runs via a $\chi^2(t,m)$ fit, with confidence regions set by $\Delta\chi^2 = 1,4$, and with error bars from bootstrapping the sightlines plus a covariance matrix built from the network's prediction residuals. A key design choice is retraining the network per sightline with matching resolution, noise, and pixel binning, which the paper shows is stable under roughly 20% resolution changes.

What would settle it

Run the full pipeline on a simulated Lyman-α skewer with a known WDM mass, say 3 keV, drawn from a simulation excluded from training, and check whether the $2\sigma$ interval from the PDF fit contains the injected mass; if the pipeline instead returns a CDM-like bound or the coverage is badly off, the central claim fails.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the optical depth-weighted density field $\Delta_\tau$ is a viable inference target for warm dark matter: a Bayesian neural network trained only on Sherwood-Relics simulations recovers $\geq 85\%$ of validation density pixels within $1\sigma$ (and $\geq 75\%$ on the held-out Nyx runs), and the PDF of the recovered field, fit to observed spectra with a $\chi^2$ statistic, separates CDM-like models from WDM suppression strongly enough to give $m_{\mathrm{WDM}} \gtrsim 3.8$ keV (SQUAD, $z=4.4$) and $m_{\mathrm{WDM}} \gtrsim 2.2$ keV (GHOST, $z=4.9$) at $2\sigma$. The authors present this as matching state-of-the-art Lyman-α power-spectrum constraints with up to ~40 times less observational path length. They also state that the best-fit thermal model is always the coldest Sherwood-Relics run and that the quoted bounds may weaken with a more thorough treatment of the IGM thermal state.

Load-bearing premise

The load-bearing premise is that the neural network's reconstructed density field is unbiased and its pixel uncertainties calibrated well enough that the observed PDF can be compared to simulation PDFs with honest error bars, which the paper's own validation only partially supports (85% of pixels within 1σ rather than 68%, and no end-to-end recovery test with a known WDM mass).

Editorial extensions

If this is right

  • Short sightline samples, such as six UVES skewers or two GHOST segments, can produce WDM lower bounds comparable to those from many hundreds of spectra, greatly lowering the observational cost of such constraints.
  • The method yields a full pixel-by-pixel density field, making the small-scale suppression of structure by WDM visible directly rather than only through flux statistics.
  • Heterogeneous datasets can be combined because each sightline gets its own retrained network with matching noise, resolution, and binning.
  • Because both observed samples prefer the coldest simulated thermal model, the current single-parameter bounds do not yet separate WDM smoothing from IGM temperature; the authors caution that the constraints may weaken under a more complete thermal treatment.

Reading between the lines

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

  • If the density-PDF statistic truly contains the same WDM information as the flux power spectrum, then the practical bottleneck for Lyman-α WDM constraints may be the number of independent short sightlines rather than total path length, so many moderate-SNR spectra could push the bound above the ~4 keV limit that power-spectrum noise floors appear to impose.
  • The reported 85%-within-1σ rate (instead of ~68% for calibrated Gaussian errors) suggests the network's uncertainties are overconfident; recalibrating the predictive variances before building the covariance matrix could move the $\chi^2$ curves and the inferred mass floors.
  • A clean information-content test would compare the Fisher information of the reconstructed $\Delta_\tau$ PDF with that of the flux power spectrum on the same simulation realizations; without such a test, the 'up to 40 times less data' claim is a comparison of two different summary statistics rather than a proven gain.
  • Because the best-fit thermal model is always the coldest run, the residual PDF mismatch likely arises from unmodelled parameters such as the slope $\gamma$ of the temperature-density relation; extending the training grid to include $\gamma$ is the natural follow-up and would allow joint WDM-thermal constraints.
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

4 major / 5 minor

Summary. This paper trains a Bayesian neural network on the Sherwood-Relics simulation suite to reconstruct the optical depth-weighted density field Delta_tau from Lyman-alpha flux skewers, and then uses the PDF of the reconstructed field to constrain the warm dark matter mass. The network is validated on held-out Sherwood-Relics skewers (85% of pixels within 1 sigma) and on independent Nyx simulations (>=75% within 1 sigma). Applying the reconstruction to observed spectra from SQUAD/UVES at z=4.4 and GHOST at z=4.9, the authors fit the reconstructed Delta_tau PDFs to ground-truth Sherwood-Relics PDFs and report 2-sigma lower bounds of m_WDM >= 3.8 keV and >= 2.2 keV, claiming to match state-of-the-art power-spectrum constraints with up to ~40 times less observational data.

Significance. If the inference pipeline were fully validated, this would be a significant methodological advance: it introduces a new density-field-level summary statistic that appears to extract WDM information from very small observational samples, and the public code plus cross-simulation validation (Nyx) are concrete strengths. However, the central inference step lacks an end-to-end recovery test, and the uncertainty calibration shows signs of being incorrect, so the headline constraints are not yet established.

major comments (4)
  1. [Section 2.1, Eq. (2)] The negative log-likelihood in Eq. (2) is not the correct Gaussian NLL: for predicted mean mu_i and variance sigma_i^2, the NLL contains +log(sigma_i^2) (up to constants), not log(1/sigma_i^2). As written, the loss decreases as sigma_i increases, which would encourage the network to inflate uncertainties without bound. This may be a typographical error, but if implemented as written it would directly produce the over-coverage seen in Sec. 4.1 (85% within 1 sigma) and would corrupt the covariance matrix in Eq. (3) used to set the error bars in Eq. (4). Please correct the equation and verify the implementation.
  2. [Section 2.3 and Section 4] The inference step compares the reconstructed Delta_tau PDF from observed spectra to the ground-truth Delta_tau PDFs from the same Sherwood-Relics simulations used to train the network. No end-to-end injection-recovery test is presented: a simulated spectrum with a known WDM mass is never passed through the full pipeline (reconstruction, PDF construction, chi-square fit) to check that the input mass is recovered within the quoted confidence. The per-pixel validation in Sec. 4.1 (85% within 1 sigma, Fig. 4) does not rule out a density-dependent bias in the reconstructed PDF, and the low-density tail of the PDF in Fig. 5 is exactly the region where the fit is most sensitive. Without such a test, the reported 3.8 and 2.2 keV bounds could be dominated by reconstruction bias. We recommend adding mock-recovery tests using a held-out WDM model or an independent simulation.
  3. [Section 4.1 and Eq. (3)] The uncertainty propagation relies on a covariance matrix computed from residuals r_i on the reference Sherwood-Relics run. Because these residuals are not Gaussian (the paper reports 85% of pixels within 1 sigma and 97% within 2 sigma), the resulting epsilon(j) in Eq. (4) do not represent Gaussian errors, and the Delta-chi^2 thresholds in Eq. (6) (1, 4) are not strictly valid. The authors should recalibrate the network's predictive uncertainties (e.g., by isotonic regression or an empirical CDF on a validation set) or use a likelihood that accounts for the non-Gaussianity, and quantify how the WDM bounds shift.
  4. [Sections 4.2 and 4.3] The best-fit thermal model is in both cases the Sherwood-Relics cold run, which the paper itself notes has T0 already below current measurements of the IGM temperature. Since the inference effectively marginalizes over the thermal state by picking the coldest model, the resulting lower bounds on m_WDM are likely biased upward. We ask the authors to test the sensitivity of the bounds to the thermal treatment: for example, excluding the cold run, or imposing a prior on T0 based on measured values, and reporting the resulting constraints.
minor comments (5)
  1. [Throughout] The unit of particle mass is written as 'KeV'; the standard SI unit is 'keV' (also in the abstract and Table 4).
  2. [Figure 1 caption] The caption states '336 residual layers', which appears inconsistent with the architecture described in Sec. 2.1 and Table 1 (layer layout [1,2,4,4]); please clarify.
  3. [Section 5 vs. Abstract] Section 5 states the SQUAD comparison uses '~60 times less data' than the power-spectrum constraints, while the abstract says 'up to ~40 times'; please reconcile these numbers.
  4. [Section 4.1] The phrase 'the recovered fractions behave in a more gaussian fraction when approaching the 2 sigma limit' is unclear; consider rephrasing.
  5. [Section 2.3] The saturation mask threshold '3/SNR' is introduced without justification; a brief rationale or reference would help.

Circularity Check

0 steps flagged · score 0.0 of 10

No construction-level circularity: the observed-data PDF is external and no fitted parameter is renamed as a prediction, so the WDM inference is not forced by definition.

full rationale

The central inference chain compares a histogram of observed, network-reconstructed Delta_tau values (PDF_obs) with ground-truth PDFs computed directly from Sherwood-Relics simulations via Eq. (4). The observed sightlines are external data, not outputs of the network or of the simulations, and no free parameter is adjusted against the observed PDF in a way that would force agreement with a particular WDM mass. The neural network is trained once on Sherwood-Relics flux-to-density pairs; the simulation PDFs used as models are not generated by that network, so Eq. (4) is not an identity. The Nyx validation provides independent, out-of-training-set support for the reconstruction, and the paper's own limitations section acknowledges the unphysical coldest-model preference and the need for finer model grids. The 85% within-1-sigma calibration result and the absence of an end-to-end WDM injection-recovery test are genuine accuracy and robustness concerns that could bias the quoted lower bounds, but they do not make the derivation circular under the definitions used here. Minor self-citations (e.g., Nasir et al. 2024) supply implementation details and are not load-bearing uniqueness claims, so no circular step can be exhibited.

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

The analysis depends on the Sherwood-Relics simulation grid, which simultaneously provides the training labels, the validation set, and the model PDFs for the chi-square fit. The BNN hyperparameters are tuned on that same grid. No new physical entities are introduced; the WDM particle is the hypothesis being constrained, not an ad hoc addition.

free parameters (5)
  • NN learning rate = 0.00494
    Tuned via OPTUNA at z = 4.4; changes reconstruction and thus the PDF.
  • NN batch size = 32
    Hyperparameter from OPTUNA.
  • NN layer layout = [1, 2, 4, 4]
    Residual block layout from OPTUNA.
  • NN filters per layer = [16, 32, 32, 32]
    Filter counts from OPTUNA.
  • Saturation mask threshold = 3/SNR
    Pixels below this flux threshold are masked; hand-chosen, affects the PDF shape and the fit.
assumptions (5)
  • domain assumption Sherwood-Relics simulations faithfully model the Lyman-alpha forest and the small-scale suppression from WDM
    Training data and model PDFs both come from this suite; any simulation inaccuracy propagates to the constraints.
  • domain assumption The optical depth-weighted density Delta_tau is the right summary statistic to carry WDM information
    The network predicts Delta_tau and the PDF of Delta_tau is the only statistic used for inference.
  • domain assumption The BNN predicted uncertainties are reliable enough to define the error bars in the chi-square fit
    Validation shows 85% of pixels within 1 sigma rather than 68%, indicating miscalibration; these uncertainties feed the covariance and the final error budget.
  • domain assumption The three Sherwood-Relics thermal histories bracket the true IGM thermal state, and the coldest model is an acceptable best fit
    The paper selects the cold model, which it acknowledges lies below measured IGM temperatures, so the WDM bound may be weakened by a more thorough thermal treatment.
  • standard math The chi-square confidence interval procedure (Avni 1976) applies to the PDF comparison
    Standard frequentist approximation, valid if the PDF errors are approximately Gaussian.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Signatures of warm dark matter in the cosmological density fields extracted using Machine Learning." pith.science (2026). https://pith.science/paper/KTYD36FT

@misc{pith2026241117853,
  author       = {Pith},
  title        = {Pith review of: Signatures of warm dark matter in the cosmological density fields extracted using Machine Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KTYD36FT}},
  note         = {Machine review of arXiv:2411.17853}
}
abstract

We aim to construct a machine-learning approach that allows for a pixel-by-pixel reconstruction of the intergalactic medium (IGM) density field for various warm dark matter (WDM) models using the Lyman-alpha forest. With this regression machinery, we constrain the mass of a potential WDM particle from observed Lyman-alpha sightlines directly from the density field. We design and train a Bayesian neural network on the supervised regression task of recovering the optical depth-weighted density field $\Delta_\tau$ as well as its reconstruction uncertainty from the Lyman-alpha forest flux field. We utilise the Sherwood-Relics simulation suite at $4.1\leq z \leq 5.0$ as the main training and validation dataset. Leveraging the density field recovered by our neural network, we construct an inference pipeline to constrain the WDM particle masses based on the probability distribution function of the density fields. We find that our trained Bayesian neural network can accurately recover within a $1\sigma$ error $\geq 85\%$ of the density field pixels from a validation simulated dataset that encompasses multiple WDM and thermal models of the IGM. When predicting on Lyman-alpha skewers generated using the alternative hydrodynamical code Nyx not included in the training data, we find a $1\sigma$ accuracy rate $\geq 75\%$. We consider 2 samples of observed Lyman-alpha spectra from the UVES and GHOST instruments, at $z=4.4$ and $z=4.9$ respectively and fit the density fields recovered by our Bayesian neural network to constrain WDM masses. We find lower bounds on the WDM particle mass of $m_{\mathrm{WDM}} \geq 3.8$ KeV and $m_{\mathrm{WDM}} \geq 2.2$ KeV at $2\sigma$ confidence, respectively. We are able to match current state-of-the-art WDM particle mass constraints using up to 40 times less observational data than Markov Chain Monte Carlo techniques based on the Lyman-alpha forest power spectrum.

Figures

Figures reproduced from arXiv: 2411.17853 by the authors.

Figure 1
Figure 1. Network topology for the fiducial hyper-parameter search at z = 4.4 using OPTUNA and trained on the Sherwood-Relics simulation suite with varied thermal history and WDM masses, as shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Spectrum of J0306+1853 observed with the GHOST instrument. We show in blue the PCA reconstruction of the emission continuum based on the spectrum redward of the Lyman-α line and its 1σ uncertainty [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Our trained neural network on the Sherwood-Relics data predicting the ∆τ field on a 20h−1 cMpc CDM skewer from the Nyx zre6 run. The top panel shows the input Lyman-α flux with added Gaussian noise (SNR = 50) and an instrumental resolution of 6 km s−1 . The bottom panel shows the machine learning prediction, with the true ∆τ field as a solid curve, and the mean prediction as a dashed curve. The blue region shows the… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Prediction residuals, as defined in equation 3, for the 3 different validation Nyx runs. The corresponding percentages indicate the pixel fraction that is correctly recovered within 1σ. 3. Data We apply our density recovery and WDM mass inference pipeline to two observ…
Figure 5
Figure 5. Figure 5: ∆τ PDF for the SQUAD sample using our Bayesian Neural net￾work, the green shaded region represents the symmetric 1σ allowed region. The blue, black and red curves are, respectively, the PDFs computed over the whole simulation box for the CDM models of the Sherwood-Reli…

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. Ringing of the Reionization: A first direct measurement of the intergalactic pressure smoothing scale at redshift z>4.2 as imprinted onto small-scale peculiar velocities in the Lyman-alpha forest

    astro-ph.CO 2026-07 reject novelty 6.0 of 10

    A claimed first direct measurement of the IGM pressure smoothing scale at z≈4.2–5.0 from a fitted peak in the Lyman-alpha flux power spectrum, tied to an acoustic feature in the peculiar velocity gradient.

  2. Learning from galactic rotation curves: a neural network approach

    astro-ph.CO 2024-12 conditional novelty 5.0 of 10

    Neural networks trained on simulated rotation curves can infer ultra-light dark matter and baryonic parameters from SPARC dwarf galaxies, with uncertainties comparable to MCMC.

Reference graph

Works this paper leans on

39 extracted references · 31 canonical work pages · cited by 2 Pith papers

  1. [1]

    2015, TensorFlow: Large-Scale Ma- chine Learning on Heterogeneous Systems, software available from tensor- flow.org

    Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Ma- chine Learning on Heterogeneous Systems, software available from tensor- flow.org

    work page 2015

  2. [2]

    2019, Optuna: A Next- generation Hyperparameter Optimization Framework

    Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. 2019, Optuna: A Next- generation Hyperparameter Optimization Framework

    2019

  3. [3]

    S., Bell, J

    Almgren, A. S., Bell, J. B., Lijewski, M. J., Luki ´c, Z., & Van Andel, E. 2013, The Astrophysical Journal, 765, 39

    work page 2013

  4. [4]

    Becker, G. D. & Bolton, J. S. 2013, Monthly Notices of the Royal Astronomical Society, 436, 1023–1039

    work page 2013

  5. [5]

    D., Bolton, J

    Boera, E., Becker, G. D., Bolton, J. S., & Nasir, F. 2019, The Astrophysical Journal, 872, 101

    work page 2019

  6. [6]

    S., Puchwein, E., Sijacki, D., et al

    Bolton, J. S., Puchwein, E., Sijacki, D., et al. 2016, Monthly Notices of the Royal Astronomical Society, 464, 897–914

    work page 2016

  7. [7]

    Bosman, S. E. I., Davies, F. B., Becker, G. D., et al. 2022, MNRAS, 514, 55

    2022

  8. [8]

    Bosman, S. E. I., Fan, X., Jiang, L., et al. 2018, Monthly Notices of the Royal Astronomical Society

    work page 2018

Show all 39 references
  1. [9]

    Bosman, S. E. I., ˇDurovˇcíková, D., Davies, F. B., & Eilers, A.-C. 2021, MNRAS, 503, 2077

    2021

  2. [10]

    S., & Kaplinghat, M

    Boylan-Kolchin, M., Bullock, J. S., & Kaplinghat, M. 2011, Monthly Notices of the Royal Astronomical Society: Letters, 415, L40

    2011

  3. [11]

    & Kochanek, C

    Dalal, N. & Kochanek, C. S. 2002, The Astrophysical Journal, 572, 25–33

    2002

  4. [12]

    2000, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Se- ries, V ol

    Dekker, H., D’Odorico, S., Kaufer, A., Delabre, B., & Kotzlowski, H. 2000, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Se- ries, V ol. 4008, Optical and IR Telescope Instrumentation and Detectors, ed. M. Iye & A. F. Moorwood, 534–545

    2000

  5. [13]

    J., Zehavi, I., Hogg, D

    Eisenstein, D. J., Zehavi, I., Hogg, D. W., et al. 2005, The Astrophysical Journal, 633, 560

    2005

  6. [14]

    K., Strauss, M

    Fan, X., Narayanan, V . K., Strauss, M. A., et al. 2002, The Astronomical Journal, 123, 1247–1257

    2002

  7. [15]

    2021, Proceedings of the IEEE, 109, 683–703

    Fang, C., Dong, H., & Zhang, T. 2021, Proceedings of the IEEE, 109, 683–703

    2021

  8. [16]

    G., et al

    Gaikwad, P., Rauch, M., Haehnelt, M. G., et al. 2020, MNRAS, 494, 5091

    2020

  9. [17]

    G., & Choudhury, T

    Gaikwad, P., Srianand, R., Haehnelt, M. G., & Choudhury, T. R. 2021, Monthly Notices of the Royal Astronomical Society, 506, 4389–4412

    2021

  10. [18]

    Gunn, J. E. & Peterson, B. A. 1965, ApJ, 142, 1633

    1965

  11. [19]

    & Ra ffelt, G

    Hannestad, S. & Ra ffelt, G. 2004, Journal of Cosmology and Astroparticle Physics, 2004, 008–008 Iršiˇc, V ., Viel, M., Haehnelt, M. G., et al. 2017, Physical Review D, 96 Iršiˇc, V ., Viel, M., Haehnelt, M. G., et al. 2024, Physical Review D, 109

    2004

  12. [20]

    V ., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M

    Jospin, L. V ., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M. 2022, IEEE Computational Intelligence Magazine, 17, 29–48

    2022

  13. [21]

    M., Diaz, R

    Kalari, V . M., Diaz, R. J., Robertson, G., et al. 2024, AJ, 168, 208 Luki´c, Z., Stark, C. W., Nugent, P., et al. 2015, MNRAS, 446, 3697

    2024

  14. [22]

    2024, Parameter esti- mation from Ly-alpha forest in Fourier space using Information Maximising Neural Network

    Maitra, S., Cristiani, S., Viel, M., Trotta, R., & Cupani, G. 2024, Parameter esti- mation from Ly-alpha forest in Fourier space using Information Maximising Neural Network

    2024

  15. [23]

    W., Hayes, C

    McConnachie, A. W., Hayes, C. R., Robertson, J. G., et al. 2024, PASP, 136, 035001

    2024

  16. [24]

    1994, Nature, 370, 629

    Moore, B. 1994, Nature, 370, 629

    1994

  17. [25]

    T., Kacprzak, G

    Murphy, M. T., Kacprzak, G. G., Savorgnan, G. A. D., & Carswell, R. F. 2018, Monthly Notices of the Royal Astronomical Society, 482, 3458–3479

    2018

  18. [26]

    B., et al

    Nasir, F., Gaikwad, P., Davies, F. B., et al. 2024, Deep Learning the Intergalactic Medium using Lyman-alpha Forest at 4≤ z≤ 5

    2024

  19. [27]

    2024, Astronomy and Astro- physics Planck Collaboration, Ade, P

    Nayak, P., Walther, M., Gruen, D., & Adiraju, S. 2024, Astronomy and Astro- physics Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2014, AAP, 571, A16 Planck Collaboration, Aghanim, N., Akrami, Y ., et al. 2020, A&A, 641, A6

    2024

  20. [28]

    S., Keating, L

    Puchwein, E., Bolton, J. S., Keating, L. C., et al. 2023, MNRAS, 519, 6162

    2023

  21. [29]

    G., & Madau, P

    Puchwein, E., Haardt, F., Haehnelt, M. G., & Madau, P. 2019, Monthly Notices of the Royal Astronomical Society, 485, 47–68

    2019

  22. [30]

    1999, MNRAS, 310, 57

    Schaye, J., Theuns, T., Leonard, A., & Efstathiou, G. 1999, MNRAS, 310, 57

    1999

  23. [31]

    2005, Monthly Notices of the Royal Astronomical Society, 364, 1105–1134 Van Waerbeke, L., Mellier, Y ., & Hoekstra, H

    Springel, V . 2005, Monthly Notices of the Royal Astronomical Society, 364, 1105–1134 Van Waerbeke, L., Mellier, Y ., & Hoekstra, H. 2004, Astronomy and Astro- physics, 429, 75–84

    2005

  24. [32]

    G., Matarrese, S., & Riotto, A

    Viel, M., Lesgourgues, J., Haehnelt, M. G., Matarrese, S., & Riotto, A. 2005, Physical Review D, 71

    2005

  25. [33]

    2021, Frontiers in As- tronomy and Space Sciences, 8

    Villanueva-Domingo, P., Mena, O., & Palomares-Ruiz, S. 2021, Frontiers in As- tronomy and Space Sciences, 8

    2021

  26. [34]

    2023, Physical Review D, 108 V ogelsberger, M., Genel, S., Springel, V ., et al

    Villasenor, B., Robertson, B., Madau, P., & Schneider, E. 2023, Physical Review D, 108 V ogelsberger, M., Genel, S., Springel, V ., et al. 2014, Nature, 509, 177

    2023

  27. [35]

    2015, The Astrophysical Journal, 807, L9

    Wang, F., Wu, X.-B., Fan, X., et al. 2015, The Astrophysical Journal, 807, L9

    2015

  28. [36]

    2023, Tree-Structured Parzen Estimator: Understanding Its Algo- rithm Components and Their Roles for Better Empirical Performance

    Watanabe, S. 2023, Tree-Structured Parzen Estimator: Understanding Its Algo- rithm Components and Their Roles for Better Empirical Performance

    2023

  29. [37]

    Weinberg, D. H. 2003, in AIP Conference Proceedings, V ol. 666 (AIP), 157–169

    2003

  30. [38]

    H., Bullock, J

    Weinberg, D. H., Bullock, J. S., Governato, F., Kuzio de Naray, R., & Pe- ter, A. H. G. 2015, Proceedings of the National Academy of Sciences, 112, 12249–12255

    2015

  31. [39]

    F., Davies, F

    Wolfson, M., Hennawi, J. F., Davies, F. B., Luki´c, Z., & Oñorbe, J. 2023, Fore- casting constraints on the high-z IGM thermal state from the Lyman-α forest flux auto-correlation function Article number, page 8 of 12 Ander Artola et al.: Signatures of warm dark matter in the c...

    2023

Pith tools

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