Pith. sign in

REVIEW 4 major objections 4 minor 3 cited by

Prospects of a statistical detection of the 21-cm forest and its potential to constrain the thermal state of the neutral IGM during reionization

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read A statistical detection of the 21-cm forest is within reach of uGMRT and SKA1-low, and a null detection would tighten constraints on the thermal state of the neutral IGM at the end of reionization.

desk verdict A genuinely useful forecast paper whose headline P21 detection claim is not backed by a detection statistic; the null-detection constraints are the stronger and more interesting result. read the letter →

arxiv 2412.06879 v2 pith:NB2J6DMT submitted 2024-12-09 astro-ph.CO

Tomáš Šoltinský (TIFR , INAF-OATs) , Girish Kulkarni (TIFR) , Shriharsh P. Tendulkar (TIFR) , James S. Bolton (University of Nottingham) This is my paper · ORCID
classification astro-ph.CO
keywords 21-cmforestEpochofReionizationintergalacticmediumpowerspectrumneutralhydrogenradio-loudquasarsuGMRTSKA1-low
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 21-cm forest, the absorption lines imprinted on radio spectra of distant quasars by neutral hydrogen, can be detected statistically rather than feature-by-feature at the end of reionization ($z\approx 6$). Forward-modelling semi-numerical reionization simulations with realistic telescope noise, it finds that the one-dimensional power spectrum of the forest rises above the noise at large scales: $k \lesssim 8.5\,\mathrm{MHz}^{-1}$ with 500 h of uGMRT time and $k \lesssim 32.4\,\mathrm{MHz}^{-1}$ with 50 h of SKA1-low, provided the IGM is 25% neutral and the neutral gas has spin temperature $\lesssim 30$ K. It then shows that a measurement of ten such spectra can jointly constrain the X-ray heating efficiency and neutral fraction, and that a null detection would disfavour cold, significantly neutral IGM models more strongly than current Ly$\alpha$ forest and 21-cm tomography limits. The practical pay-off is that an outcome of such a modest observing campaign, whether a detection or a null, would teach us about the thermal state of the IGM that other probes cannot see.

What carries the argument

The load-bearing object is the one-dimensional power spectrum $P_{21}(k)$ of the normalized 21-cm forest flux $F_{21}=e^{-\tau_{21}}$, computed from mock spectra drawn from 21cmfast semi-numerical simulations of a $(50\,\mathrm{cMpc})^3$ volume at $z=6$. The power spectrum converts the forest from a set of rare individual absorption features into a statistically measurable quantity; the paper uses it to define detectability against the noise power of the telescope and to build a Gaussian likelihood whose covariance matrix includes both telescope noise and, dominantly, sample variance. The spin temperature is assumed equal to the kinetic temperature, which is valid when the Ly$\alpha$ background is strong at this redshift, and the telescope noise is modelled from the sensitivity curves of uGMRT and SKA1-low.

What would settle it

Take a 500-h uGMRT band-2 spectrum (or 50-h SKA1-low spectrum) of a z≈6 quasar with S147 ≈ 64 mJy in the cold, 25%-neutral IGM model; if the measured 1D power spectrum shows no excess above the noise-only power at k < 8.5 $MHz^{-1}$ (or k < 32.4 $MHz^{-1}$ for SKA1-low), the central detectability claim is falsified. A cleaner test is to run the same forward model in a simulation box larger than 50 cMpc, or with lightcone effects, and check whether the low-k power spectrum changes by more than the claimed noise margin.

Watch

Extended reading notes

Core claim

The central claim is that the 21-cm forest should be pursued as a statistical signal measured through the one-dimensional power spectrum of the transmitted flux, $P_{21}(k)$, rather than through direct detection of individual absorption lines. In the authors' forward model, for an IGM that is $\langle x_{\mathrm{HI}}\rangle = 0.25$ neutral and preheated so that the spin temperature is $\lesssim 30$ K, the signal exceeds the noise level at $k \lesssim 8.5$ MHz$^{-1}$ for 500 h of uGMRT band-2 observations and $k \lesssim 32.4$ MHz$^{-1}$ for 50 h of SKA1-low observations of a bright $z \approx 6$ radio-loud quasar. The paper further claims that sample variance, not telescope noise, dominates the uncertainty in parameter inference, so SKA1-low does not dramatically outperform uGMRT for the same integration, and that a null detection of the power spectrum from ten such spectra would place upper limits on the neutral fraction and lower limits on X-ray heating that are competitive with or tighter than current Ly$\alpha$ forest and 21-cm tomography constraints.

Load-bearing premise

The forecasts assume that the 50 cMpc simulation box contains all the neutral-hydrogen structure that produces the 21-cm forest power at the claimed detection scales; the paper itself notes that larger neutral islands, which late-reionization models predict, lie outside the box and could change both the predicted signal and the null-detection limits.

Editorial extensions

If this is right

  • With 500 h of uGMRT on one bright $z\approx 6$ quasar, the 1D power spectrum is detectable at $k \lesssim 8.5$ MHz$^{-1}$ if the IGM is 25% neutral and colder than roughly 30 K.
  • SKA1-low achieves a similar or better detection window, $k \lesssim 32.4$ MHz$^{-1}$, in only 50 h per source.
  • Ten measured spectra of 22.1 MHz bandwidth can jointly constrain $\log_{10} f_X$ and $\langle x_{\mathrm{HI}}\rangle$, and for cold, neutral models these constraints are tighter than current Ly$\alpha$ forest and 21-cm tomography measurements.
  • A null detection from ten 500-h uGMRT spectra would rule out $T_{\mathrm{HI}} \lesssim 54$ K at 25% neutral fraction, with correspondingly higher temperature limits at higher neutral fractions, a region of parameter space currently unconstrained.
  • Even 50 h of uGMRT on ten quasars, if null, would disfavour very cold IGMs with neutral fraction $\gtrsim 0.18$, already competitive with existing probes.

Reading between the lines

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

  • If late-reionization neutral islands extend beyond the 50 cMpc simulation box, the large-scale (low-$k$) power could be larger than forecast, making detection easier while strengthening the null-detection limits; the paper's convergence tests address resolution but not box size.
  • The Gaussian likelihood used may be suboptimal for the non-Gaussian 21-cm forest, so likelihood-free inference could extract more information from the same ten spectra and sharpen the thermal constraints.
  • The same $P_{21}$ statistic could also probe small-scale structure such as minihaloes or dark-matter candidates, since those imprint on the high-$k$ end of the power spectrum beyond the scales used here for the IGM constraints.
  • The requirement of a single ultra-bright quasar could be relaxed by targeting the predicted population of roughly 50 radio-loud quasars brighter than 100 mJy at $z>5.5$, if such a population is confirmed by forthcoming surveys.
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 / 4 minor

Summary. The paper forward-models 21-cm forest spectra at z=6 using 21cmfast semi-numerical simulations, adds telescope noise and spectral resolution for uGMRT and SKA1-low, and studies two statistical observables: the differential number density of the transmitted flux and the 1D power spectrum P21. The authors claim that a statistical detection of P21 is possible with 500 hr of uGMRT or 50 hr of SKA1-low time under a late-end reionization model with <xHI> = 0.25 and spin temperature < 30 K, and that a null detection of 10 such spectra would constrain the neutral fraction and X-ray heating of the IGM more tightly than current Ly-alpha forest and 21-cm tomography limits. The paper also presents Bayesian MCMC forecasts for joint constraints on log10 fX and <xHI>.

Significance. If the detection claim holds, the paper would provide a timely and observationally actionable forecast: the 21-cm forest 1D power spectrum could be probed with existing or near-future telescopes at z=6, and a null detection would place competitive limits on a cold, neutral IGM. The forward-modelling is careful in several respects: the code is public, a resolution convergence test is included in Appendix A, the covariance matrix is estimated from 10^4 mock realizations, and the authors explicitly discuss omitted effects such as minihaloes, RFI, mode-mixing, and redshift evolution. The main shortfall is that the headline detectability statement is not backed by a detection significance computed from the total variance, and the null-detection criterion is not defined at the data level.

major comments (4)
  1. [§3.2 and abstract] The detectability claim for P21 is based on comparing the median signal power with the noise-only power in Fig. 6, but §4.1 and Fig. 8 show that sample variance is orders of magnitude stronger than telescope noise and dominates the covariance. For a band-power measurement, the relevant uncertainty is the total variance, not the noise floor. The paper reports no detection S/N, no detection probability, and no false-detection rate for P21 itself: the KS-test fractions in §3.1 and the per-channel SNR in Appendix C concern different statistics, and the MCMC recovery in §4.2 starts from mock observations that already contain the signal. The abstract's statement that it is 'possible to detect the 1D power spectrum ... k < 8.5 MHz^{-1}' is therefore under-supported as written. Please compute a detection significance using the full covariance (e.g. S/N per k-bin or a detection fraction over mock realizations) or soften the claim accordingly.
  2. [§2.1 and Figs. 6, 10] The simulation volume is (50 cMpc)^3, and §2.1 explicitly states that coherent HI islands on scales larger than the box are not captured. This is a direct limitation for the low-k part of P21, where the claimed detection is strongest: a 50 cMpc spectrum corresponds to a fundamental mode around k ≈ 1 MHz^{-1}, so bins below this scale are influenced by modes outside the box. Late-end reionization models, which motivate the paper, predict large neutral islands. Please quantify how much of the low-k signal in Fig. 6 could come from scales larger than the box, for example by comparing with a larger-volume simulation at lower resolution or by estimating the contribution of modes k < 2π/L_box. Without this, both the detection forecast and the null-detection constraints in Figs. 10, 12, and 13 are uncertain.
  3. [§4.2 (null-detection definition, Fig. 10)] The null-detection criterion is defined as the model power <P21_sim>10 falling below the noise-only power in at least one bin below 8.5 MHz^{-1}, and the 32% threshold is used to draw the solid black curves in Figs. 10, 12, and 13. This is not a data-level detection criterion: it does not include the sample-variance fluctuations of an actual mock observation, and it does not correspond to a likelihood-based or hypothesis-test decision. Please define a null detection operationally (for example, the fraction of mock observations for which the posterior excludes a nonzero signal, or a detection S/N threshold), and calibrate that criterion on mock realizations. The current definition makes the claimed null-detection constraints difficult to interpret.
  4. [§4.1, Eq. (6)] The multivariate Gaussian likelihood is used for all parameter inference, and the conclusions themselves note that the 21-cm forest is expected to be non-Gaussian (citing Wolfson et al. 2023). Because sample variance dominates the covariance matrix, the shape of the likelihood is what sets the credible intervals in Figs. 9–13. Please add a validation of the likelihood, for example a coverage test on mock observations across the parameter grid, to verify that the reported 1σ and 2σ contours are calibrated. Without such a test, the joint constraints and the comparisons with Ly-alpha forest and HERA limits are not fully supported.
minor comments (4)
  1. [Fig. 6 caption vs. §2.3] The Fig. 6 caption says the power spectrum is from a single 50 cMpc spectrum, while §2.3 constructs spliced 200 cMpc spectra and states that they are used in Section 3.2 and Section 4; please clarify which spectrum length underlies the detectability statement, since the longer spectra lower the noise floor and add low-k bins.
  2. [§3.1 and Fig. 5 caption] The text says the solid curves are 'mean values over 1000 LOS', while the Fig. 5 caption says 'solid lines correspond to the median values'; please make the terminology consistent.
  3. [Eq. (6)] The Gaussian likelihood as written omits the (2π)^{n/2} normalization factor; this is irrelevant for the MCMC implementation, but it would be clearer to state that the normalization is dropped.
  4. [Conclusions, first bullet] The statement that 'the SNR achieved by the uGMRT (SKA1-low) ... is 0.37 (1.2)' refers to the per-channel absorption SNR from Appendix C, not to a detection significance of P21; please rename this quantity to avoid confusion with the power-spectrum detection claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a forward-model forecast with self-consistency tests, not a derivation that reduces to its inputs.

full rationale

The paper's central claims are forecasts from a forward model: 21cmfast simulations are used to generate the IGM fields, the 21-cm optical depth is computed from a standard formula (Eq. 2), instrumental noise and resolution are added, and the resulting mock spectra are used to estimate the detectability of the 1D power spectrum and to test Bayesian parameter recovery. None of these steps identifies the predicted quantity with an input by construction. The detection claim is conditional on a specified fiducial model (x_HI = 0.25, log10 fX = -2, THI ~ 30 K) and compares the simulated signal to a separately modeled noise power spectrum; this is a sensitivity forecast, not a circular reduction. The Bayesian recovery uses mock observations drawn from the same simulation model, which is a self-consistency check of the inference pipeline rather than an independent empirical validation, but it is not circular in the sense of deriving the target result from its own assumption. Self-citations to Soltinsky et al. (2021) are methodological or motivational (e.g., the form of the optical depth calculation, the claim that the 21-cm forest is strongest in cold neutral gas) and are not used as a load-bearing uniqueness theorem; the optical depth formula is standard and the cited prior work is not invoked to forbid alternatives. The paper's own caveats, such as the limited simulation box size, missing HI islands on large scales, neglect of minihaloes, and the use of a Gaussian likelihood, are acknowledged limitations and do not constitute circularity. The skeptic's concern that the P21 detection claim compares median signal to the noise floor rather than to the total variance is a statistical-correctness issue, not a circularity issue, and is therefore outside the scope of this circularity analysis.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on simulation inputs, fiducial fX and xHI values, and modeling choices such as spin temperature coupling, excursion-set bimodality, Gaussian noise, Gaussian likelihood, and box size. No new physical entities are introduced. The paper's own limitations section acknowledges omitted minihaloes, RFI, lightcone effects, and large-scale islands.

free parameters (3)
  • Fiducial X-ray efficiency log10 fX = -2 = -2.0 (fiducial)
    Chosen to represent a cold, weakly preheated IGM. The detectability forecast and null-detection constraints depend on this value; no external fit is performed.
  • Fiducial mean neutral fraction <xHI> = 0.25 = 0.25 (fiducial)
    Chosen as representative of late-end reionization at z=6. The predicted P21 amplitude scales strongly with this input.
  • Quasar flux density S147 and spectral index alpha_R = S147 = 64.2 mJy, alpha_R = -0.44
    Properties of PSO J0309+27 assumed for all ten background sources. This is optimistic because most z > 5.5 radio-loud quasars are fainter, and the noise level and accessible k-range depend on source brightness.
assumptions (5)
  • domain assumption Spin temperature is fully coupled to gas kinetic temperature, Ts = TK, at z=6
    Invoked in Sec 2.2 when computing optical depth. If Ly-alpha coupling is weaker than assumed, the absorption strength changes; the paper notes the difference at z=6 is small.
  • domain assumption The 21cmfast excursion-set model with bimodal xHI, equal to 0 or about 1, represents the reionization-era IGM
    Used throughout Sec 2.1. The model cannot produce partially neutral gas, and missing partial ionization or small-scale structure could alter the 21-cm forest power spectrum.
  • domain assumption A 50 cMpc simulation box contains the relevant large-scale modes for the power spectrum
    Sec 2.1 admits the simulations do not capture HI islands larger than the box; late-end reionization may produce such islands, and the claimed detection window is at large scales.
  • domain assumption Telescope noise is Gaussian white noise with Aeff/Tsys from Braun et al. 2019
    Used in Sec 2.3 to add noise. The authors note their noise estimate differs from official calculators by -22 percent to +30 percent, and RFI and mode-mixing are not modeled.
  • ad hoc to paper A multivariate Gaussian likelihood is adequate for P21 parameter inference
    Adopted in Sec 4.1, Eq. 6. The authors acknowledge in Sec 5 that the 21-cm forest is non-Gaussian and a Gaussian likelihood may not be optimal.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Prospects of a statistical detection of the 21-cm forest and its potential to constrain the thermal state of the neutral IGM during reionization." pith.science (2026). https://pith.science/paper/NB2J6DMT

@misc{pith2026241206879,
  author       = {Pith},
  title        = {Pith review of: Prospects of a statistical detection of the 21-cm forest and its potential to constrain the thermal state of the neutral IGM during reionization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NB2J6DMT}},
  note         = {Machine review of arXiv:2412.06879}
}
abstract

The 21-cm forest signal is a promising probe of the Epoch of Reionization complementary to other 21-cm line observables and Ly$\alpha$ forest signal. Prospects of detecting it have significantly improved in the last decade thanks to the discovery of more than 30 radio-loud quasars at these redshifts, upgrades to telescope facilities, and the notion that neutral hydrogen islands persist down to $z\lesssim 5.5$. We forward-model the 21-cm forest signal using semi-numerical simulations and incorporate various instrumental features to explore the potential of detecting the 21-cm forest at $z=6$, both directly and statistically, with the currently available (uGMRT) and forthcoming (SKA1-low) observatories. We show that it is possible to detect the 1D power spectrum of the 21-cm forest spectrum, especially at large scales of $k\lesssim8.5\,\rm MHz^{-1}$ with the $500\,\rm hr$ of the uGMRT time and $k\lesssim32.4\,\rm MHz^{-1}$ with the SKA1-low over $50\,\rm hr$ if the intergalactic medium (IGM) is $25\%$ neutral and these neutral hydrogen regions have a spin temperature of $\lesssim30\,\rm K$. On the other hand, we infer that a null-detection of the signal with such observations of 10 radio-loud sources at $z\approx6$ can be translated into constraints on the thermal and ionization state of the IGM which are tighter than the currently available measurements. Moreover, a null-detection of the 1D 21-cm forest power spectrum with only $50\,\rm hr$ of the uGMRT observations of 10 radio-loud sources can already be competitive with the Ly$\alpha$ forest and 21-cm tomographic observations in disfavouring models of significantly neutral and cold IGM at $z=6$.

Figures

Figures reproduced from arXiv: 2412.06879 by the authors.

Figure 1
Figure 1. Left: 2D slices of the gas temperature at 𝑧 = 6 for the combination of ⟨𝑥HI⟩ = 0.11 and log10 𝑓X = −1.4 (top panel) and ⟨𝑥HI⟩ = 0.25 and log10 𝑓X = −2.0 (bottom panel). Right: From top to bottom, gas overdensity, peculiar velocity, neutral hydrogen fraction, gas temperature and normalized 21-cm forest flux along the line-of-sight indicated by the dashed fuchsia lines in the left panels. The bottom three panels are s… view at source ↗
Figure 2
Figure 2. Top panel: Redshift distribution of radio-loud quasars identified at 𝑧 ≥ 5.5 across the whole sky (solid blue curve) and in the northern hemisphere (dashed fuchsia curve). The sample is compiled from Fan et al. (2001), McGreer et al. (2006), Willott et al. (2010), Zeimann et al. (2011), Bañados et al. (2015, 2018, 2021, 2023, 2024), Belladitta et al. (2020), Liu et al. (2021), Ighina et al. (2021, 2023, 2024), Endsl… view at source ↗
Figure 3
Figure 3. The sensitivity in terms of 𝐴eff/𝑇sys for the whole array at different observed frequencies for the LOFAR (thin blue curve), uGMRT (semi-thick fuchsia curve) and SKA1-low (thick orange curve) taken from Braun et al. (2019). The sensitivities of these three telescopes are shown only in the fre￾quency range in which the 21-cm forest is expected to be currently observable, i.e. at 177.5 MHz ≲ 𝜈obs ≲ 218.5 MHz (red shad… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: The forward-modelled 21-cm forest signal (solid black curves) in the spectrum of quasars with 𝑆147 = 64.2 mJy and 𝛼R = −0.44 at 𝑧 = 6 as observed by the uGMRT over 𝑡int = 500 hr (left panel) and SKA1-low over 𝑡int = 50 hr (right panel). We also show the simulated physi…
Figure 5
Figure 5. Figure 5: Differential number density distribution of 21-cm forest transmission by frequency channel for a spectrum containing purely the signal (orange curves), purely the noise (fuchsia curves) and an observed spectrum (signal and noise, blue curves). The solid lines correspon…
Figure 6
Figure 6. Figure 6: 1D power spectrum from a single 50 cMpc long 21-cm forest spectrum in the fiducial model of the IGM with log10 𝑓X = −2 and ⟨𝑥HI⟩ = 0.25. The solid orange curve shows the median power spectrum of the signal and the orange shaded region marks 68 per cent range from 1000 …
Figure 7
Figure 7. Figure 7: Top panel: Median 21-cm forest power spectrum, 𝑃21 from 1000 realizations of LOS for fixed ⟨𝑥HI⟩ = 0.25 and varying log10 𝑓X (values shown in colorbar). Bottom panel: Same as the top panel but with fixed log10 𝑓X = −2 and varying ⟨𝑥HI⟩ . the intrinsic spectrum of the b…
Figure 8
Figure 8. Figure 8: Covariance matrix for the noise only (left panel), sample variance only (middle panel) and their combined effect on the mock observation (right panel) assuming an IGM with ⟨𝑥HI⟩ = 0.25 and log10 𝑓X = −2 and 𝑁obs = 10 observations of 200 cMpc spectra of a PSO J0309+27 l…
Figure 9
Figure 9. Figure 9: Bayesian inference of log10 𝑓X and ⟨𝑥HI⟩ from a mock observation of 𝑆147 = 64.2 mJy and 𝛼R = −0.44 quasar by uGMRT for 500 hr. The true values of the IGM parameters are log10 𝑓X = −2, ⟨𝑥HI⟩ = 0.25. Left panel: The ⟨𝑃21 ⟩10 calculated from a spectrum including signal an…
Figure 10
Figure 10. Figure 10: Posterior distributions corresponding to 1 and 2𝜎 confidence regions indicated by contours for different true values (different colours) of the log10 𝑓X and ⟨𝑥HI⟩ indicated by the crosses. This shows the con￾straining power of the 21-cm forest power spectrum for an ob…
Figure 11
Figure 11. Figure 11: Same as [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]
Figure 12
Figure 12. Figure 12: Same as [PITH_FULL_IMAGE:figures/full_fig_p011_12.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. Forest without Trees is still Fruitful: Constraints on the thermal state of the neutral IGM at $z\approx5.6$ with the 21-cm forest power spectrum

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

    A null detection of the 21-cm forest toward quasar J352–15 at z≈5.8 disfavors very cold and substantially neutral intergalactic gas, giving the first observational constraints from a 21-cm forest power spectrum.

  2. Prospects for measuring neutrino mass with 21-cm forest

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

    Using an analytic halo model, the authors forecast that 21-cm forest observations with SKA-LOW could constrain the total neutrino mass to about 0.1 eV, potentially distinguishing neutrino mass hierarchies.

  3. Probing initial isocurvature perturbation with 21cm one-point statistics

    astro-ph.CO 2025-09 conditional novelty 5.0 of 10

    A Fisher forecast from 21cmFAST simulations shows 21cm variance can constrain the CDM isocurvature fraction to about 3e-4 with SKA, while skewness is an order of magnitude weaker.

Reference graph

Works this paper leans on

113 extracted references · 3 canonical work pages · cited by 3 Pith papers

  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]

    Astropy Collaboration et al., 2013, @doi [ ] 10.1051/0004-6361/201322068 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A..33A 558, A33

    doi:10.1051/0004-6361/201322068 2013

  4. [4]

    Ba \ n ados E., et al., 2015, @doi [ ] 10.1088/0004-637X/804/2/118 , https://ui.adsabs.harvard.edu/abs/2015ApJ...804..118B 804, 118

    doi:10.1088/0004-637x/804/2/118 2015

  5. [5]

    P., Mazzucchelli C., Venemans B

    Ba \ n ados E., Carilli C., Walter F., Momjian E., Decarli R., Farina E. P., Mazzucchelli C., Venemans B. P., 2018, @doi [ ] 10.3847/2041-8213/aac511 , https://ui.adsabs.harvard.edu/abs/2018ApJ...861L..14B 861, L14

    doi:10.3847/2041-8213/aac511 2018

  6. [6]

    Ba \ n ados E., et al., 2021, @doi [ ] 10.3847/1538-4357/abe239 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909...80B 909, 80

    doi:10.3847/1538-4357/abe239 2021

  7. [7]

    Ba \ n ados E., et al., 2023, @doi [ ] 10.3847/1538-4365/acb3c7 , https://ui.adsabs.harvard.edu/abs/2023ApJS..265...29B 265, 29

    doi:10.3847/1538-4365/acb3c7 2023

  8. [8]

    Ba \ n ados E., et al., 2024, @doi [Nature Astronomy] 10.1038/s41550-024-02431-4 , https://ui.adsabs.harvard.edu/abs/2024NatAs.tmp..293B

    doi:10.1038/s41550-024-02431-4 2024

Show all 113 references
  1. [9]

    D., Bolton J

    Becker G. D., Bolton J. S., Madau P., Pettini M., Ryan-Weber E. V., Venemans B. P., 2015, @doi [ ] 10.1093/mnras/stu2646 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447.3402B 447, 3402

    2015 doi

  2. [10]

    D., D'Aloisio A., Christenson H

    Becker G. D., D'Aloisio A., Christenson H. M., Zhu Y., Worseck G., Bolton J. S., 2021, @doi [ ] 10.1093/mnras/stab2696 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.1853B 508, 1853

    2021 doi

  3. [11]

    D., Bolton J

    Becker G. D., Bolton J. S., Zhu Y., Hashemi S., 2024, @doi [ ] 10.1093/mnras/stae1918 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.533.1525B 533, 1525

    2024 doi

  4. [12]

    Belladitta S., et al., 2020, @doi [ ] 10.1051/0004-6361/201937395 , https://ui.adsabs.harvard.edu/abs/2020A&A...635L...7B 635, L7

    2020 doi

  5. [13]

    Bhagwat A., Ciardi B., Zackrisson E., Schaye J., 2022, @doi [ ] 10.1093/mnras/stac2663 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.2331B 517, 2331

    2022 doi

  6. [14]

    Bosman S. E. I., et al., 2022, @doi [ ] 10.1093/mnras/stac1046 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514...55B 514, 55

    2022 doi

  7. [15]

    D., Rogers A

    Bowman J. D., Rogers A. E. E., Monsalve R. A., Mozdzen T. J., Mahesh N., 2018, @doi [ ] 10.1038/nature25792 , https://ui.adsabs.harvard.edu/abs/2018Natur.555...67B 555, 67

    2018 doi

  8. [16]

    arXiv:1912.12699

    Braun R., Bonaldi A., Bourke T., Keane E., Wagg J., 2019, @doi [arXiv e-prints] 10.48550/arXiv.1912.12699 , https://ui.adsabs.harvard.edu/abs/2019arXiv191212699B p. arXiv:1912.12699

    2019 doi arXiv

  9. [17]

    D., 2021, @doi [ ] 10.3847/2041-8213/ac1ace , https://ui.adsabs.harvard.edu/abs/2021ApJ...917L..37C 917, L37

    Cain C., D'Aloisio A., Gangolli N., Becker G. D., 2021, @doi [ ] 10.3847/2041-8213/ac1ace , https://ui.adsabs.harvard.edu/abs/2021ApJ...917L..37C 917, L37

    2021 doi

  10. [18]

    L., Gnedin N

    Carilli C. L., Gnedin N. Y., Owen F., 2002, @doi [ ] 10.1086/342179 , https://ui.adsabs.harvard.edu/abs/2002ApJ...577...22C 577, 22

    2002 doi

  11. [19]

    L., Wang R., van Hoven M

    Carilli C. L., Wang R., van Hoven M. B., Dwarakanath K., Chengalur J. N., Wyithe S., 2007, @doi [ ] 10.1086/518436 , https://ui.adsabs.harvard.edu/abs/2007AJ....133.2841C 133, 2841

    2007 doi

  12. [20]

    K., Ben \' tez-Llambay A., Theuns T., Frenk C., Bower R., 2024, @doi [ ] 10.1093/mnras/stae114 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.1296C 528, 1296

    Chan T. K., Ben \' tez-Llambay A., Theuns T., Frenk C., Bower R., 2024, @doi [ ] 10.1093/mnras/stae114 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.1296C 528, 1296

    2024 doi

  13. [21]

    R., Paranjape A., Bosman S

    Choudhury T. R., Paranjape A., Bosman S. E. I., 2021, @doi [ ] 10.1093/mnras/stab045 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.5782C 501, 5782

    2021 doi

  14. [22]

    M., Becker G

    Christenson H. M., Becker G. D., Furlanetto S. R., Davies F. B., Malkan M. A., Zhu Y., Boera E., Trapp A., 2021, @doi [ ] 10.3847/1538-4357/ac2a34 , https://ui.adsabs.harvard.edu/abs/2021ApJ...923...87C 923, 87

    2021 doi

  15. [23]

    Ciardi B., et al., 2013, @doi [ ] 10.1093/mnras/sts156 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.1755C 428, 1755

    2013 doi

  16. [24]

    Ciardi B., et al., 2015, @doi [ ] 10.1093/mnras/stv1640 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.453..101C 453, 101

    2015 doi

  17. [25]

    K., Bharadwaj S., Choudhury T

    Datta K. K., Bharadwaj S., Choudhury T. R., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12421.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.382..809D 382, 809

    2007

  18. [26]

    Dzurov c \'i kov \'a D urov c \'i kov \'a D urov c \'i kov \'a D., et al., 2024, @doi [ ] 10.3847/1538-4357/ad4888 , https://ui.adsabs.harvard.edu/abs/2024ApJ...969..162D 969, 162

    2024 doi

  19. [27]

    Endsley R., et al., 2023, @doi [ ] 10.1093/mnras/stad266 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.4609E 520, 4609

    2023 doi

  20. [28]

    S., Mesinger A., Hewitt J., 2014, @doi [ ] 10.1093/mnras/stu666 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.441.2476E 441, 2476

    Ewall-Wice A., Dillon J. S., Mesinger A., Hewitt J., 2014, @doi [ ] 10.1093/mnras/stu666 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.441.2476E 441, 2476

    2014 doi

  21. [29]

    Fan X., et al., 2001, @doi [ ] 10.1086/324111 , https://ui.adsabs.harvard.edu/abs/2001AJ....122.2833F 122, 2833

    2001 doi

  22. [30]

    Foreman-Mackey D., 2016, @doi [The Journal of Open Source Software] 10.21105/joss.00024 , https://ui.adsabs.harvard.edu/abs/2016JOSS....1...24F 1, 24

    2016 doi

  23. [31]

    W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    2013 doi

  24. [32]

    R., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10725.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.371..867F 371, 867

    Furlanetto S. R., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10725.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.371..867F 371, 867

    2006

  25. [33]

    R., Loeb A., 2002, @doi [ ] 10.1086/342757 , https://ui.adsabs.harvard.edu/abs/2002ApJ...579....1F 579, 1

    Furlanetto S. R., Loeb A., 2002, @doi [ ] 10.1086/342757 , https://ui.adsabs.harvard.edu/abs/2002ApJ...579....1F 579, 1

    2002 doi

  26. [35]

    R., Oh S

    Furlanetto S. R., Oh S. P., Briggs F. H., 2006, @doi [ ] 10.1016/j.physrep.2006.08.002 , https://ui.adsabs.harvard.edu/abs/2006PhR...433..181F 433, 181

    2006 doi

  27. [36]

    Gaikwad P., et al., 2020, @doi [ ] 10.1093/mnras/staa907 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.5091G 494, 5091

    2020 doi

  28. [37]

    Gaikwad P., et al., 2023, @doi [ ] 10.1093/mnras/stad2566 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.4093G 525, 4093

    2023 doi

  29. [38]

    Garaldi E., et al., 2024, @doi [ ] 10.1093/mnras/stae839 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530.3765G 530, 3765

    2024 doi

  30. [39]

    J., et al., 2022, @doi [ ] 10.1051/0004-6361/202244763 , https://ui.adsabs.harvard.edu/abs/2022A&A...668A..27G 668, A27

    Gloudemans A. J., et al., 2022, @doi [ ] 10.1051/0004-6361/202244763 , https://ui.adsabs.harvard.edu/abs/2022A&A...668A..27G 668, A27

    2022 doi

  31. [40]

    J., et al., 2023, @doi [ ] 10.1051/0004-6361/202347582 , https://ui.adsabs.harvard.edu/abs/2023A&A...678A.128G 678, A128

    Gloudemans A. J., et al., 2023, @doi [ ] 10.1051/0004-6361/202347582 , https://ui.adsabs.harvard.edu/abs/2023A&A...678A.128G 678, A128

    2023 doi

  32. [41]

    Goodman J., Weare J., 2010, @doi [Communications in Applied Mathematics and Computational Science] 10.2140/camcos.2010.5.65 , https://ui.adsabs.harvard.edu/abs/2010CAMCS...5...65G 5, 65

    2010 doi

  33. [42]

    M., Barry N., Mutch S

    Greig B., Trott C. M., Barry N., Mutch S. J., Pindor B., Webster R. L., Wyithe J. S. B., 2021a, @doi [ ] 10.1093/mnras/staa3494 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5322G 500, 5322

    doi

  34. [43]

    Greig B., et al., 2021b, @doi [ ] 10.1093/mnras/staa3593 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501....1G 501, 1

    doi

  35. [44]

    Greig B., et al., 2024, @doi [ ] 10.1093/mnras/stae1080 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530.3208G 530, 3208

    2024 doi

  36. [45]

    Gupta Y., et al., 2017, @doi [Current Science] 10.18520/cs/v113/i04/707-714 , https://ui.adsabs.harvard.edu/abs/2017CSci..113..707G 113, 707

    2017 doi

  37. [46]

    HERA Collaboration 2022, @doi [ ] 10.3847/1538-4357/ac1c78 , https://ui.adsabs.harvard.edu/abs/2022ApJ...925..221A 925, 221

    2022 doi

  38. [47]

    HERA Collaboration 2023, @doi [ ] 10.3847/1538-4357/acaf50 , https://ui.adsabs.harvard.edu/abs/2023ApJ...945..124H 945, 124

    2023 doi

  39. [48]

    R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , https://ui.adsabs.harvard.edu/abs/2020arXiv200610256H 585, 357

    Harris C. R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , https://ui.adsabs.harvard.edu/abs/2020arXiv200610256H 585, 357

    2020 doi

  40. [49]

    F., Davies F

    Hennawi J. F., Davies F. B., Wang F., O \ n orbe J., 2021, @doi [ ] 10.1093/mnras/stab1883 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.2963H 506, 2963

    2021 doi

  41. [50]

    J., Prochaska J

    Hsyu T., Cooke R. J., Prochaska J. X., Bolte M., 2020, @doi [ ] 10.3847/1538-4357/ab91af , https://ui.adsabs.harvard.edu/abs/2020ApJ...896...77H 896, 77

    2020 doi

  42. [51]

    D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , https://ui.adsabs.harvard.edu/abs/2007CSE.....9...90H 9, 90

    Hunter J. D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , https://ui.adsabs.harvard.edu/abs/2007CSE.....9...90H 9, 90

    2007 doi

  43. [52]

    W., Drouart G., Moretti A., Seymour N., 2021, @doi [ ] 10.1051/0004-6361/202140362 , https://ui.adsabs.harvard.edu/abs/2021A&A...647L..11I 647, L11

    Ighina L., Belladitta S., Caccianiga A., Broderick J. W., Drouart G., Moretti A., Seymour N., 2021, @doi [ ] 10.1051/0004-6361/202140362 , https://ui.adsabs.harvard.edu/abs/2021A&A...647L..11I 647, L11

    2021 doi

  44. [53]

    W., Drouart G., Leung J

    Ighina L., Caccianiga A., Moretti A., Belladitta S., Broderick J. W., Drouart G., Leung J. K., Seymour N., 2023, @doi [ ] 10.1093/mnras/stac3668 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.2060I 519, 2060

    2023 doi

  45. [54]

    Ighina L., et al., 2024, @doi [ ] 10.1051/0004-6361/202451376 , https://ui.adsabs.harvard.edu/abs/2024A&A...692A.241I 692, A241

    2024 doi

  46. [55]

    T., Jagannathan P., Mooley K

    Intema H. T., Jagannathan P., Mooley K. P., Frail D. A., 2017, @doi [ ] 10.1051/0004-6361/201628536 , https://ui.adsabs.harvard.edu/abs/2017A&A...598A..78I 598, A78

    2017 doi

  47. [56]

    Ioka K., M \'e sz \'a ros P., 2005, @doi [ ] 10.1086/426785 , https://ui.adsabs.harvard.edu/abs/2005ApJ...619..684I 619, 684

    2005 doi

  48. [57]

    Kadota K., Villanueva-Domingo P., Ichiki K., Hasegawa K., Naruse G., 2023, @doi [ ] 10.1088/1475-7516/2023/03/017 , https://ui.adsabs.harvard.edu/abs/2023JCAP...03..017K 2023, 017

    2023 doi

  49. [58]

    J., Shibuya T., Ouchi M., Kashikawa N., 2020, @doi [ ] 10.3847/1538-4357/ab5a7d , https://ui.adsabs.harvard.edu/abs/2020ApJ...888....6K 888, 6

    Kashino D., Lilly S. J., Shibuya T., Ouchi M., Kashikawa N., 2020, @doi [ ] 10.3847/1538-4357/ab5a7d , https://ui.adsabs.harvard.edu/abs/2020ApJ...888....6K 888, 6

    2020 doi

  50. [59]

    Kawasaki M., Nakano W., Nakatsuka H., Sonomoto E., 2021, @doi [ ] 10.1088/1475-7516/2021/04/019 , https://ui.adsabs.harvard.edu/abs/2021JCAP...04..019K 2021, 019

    2021 doi

  51. [60]

    C., Weinberger L

    Keating L. C., Weinberger L. H., Kulkarni G., Haehnelt M. G., Chardin J., Aubert D., 2020, @doi [ ] 10.1093/mnras/stz3083 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.1736K 491, 1736

    2020 doi

  52. [61]

    K., Kulkarni G., Bolton J

    Khan N. K., Kulkarni G., Bolton J. S., Haehnelt M. G., Ir s i c V., Puchwein E., Asthana S., 2024, @doi [ ] 10.1093/mnras/stae662 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530.4920K 530, 4920

    2024 doi

  53. [62]

    Kondapally R., et al., 2021, @doi [ ] 10.1051/0004-6361/202038813 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A...3K 648, A3

    2021 doi

  54. [63]

    C., Haehnelt M

    Kulkarni G., Keating L. C., Haehnelt M. G., Bosman S. E. I., Puchwein E., Chardin J., Aubert D., 2019, @doi [ ] 10.1093/mnrasl/slz025 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485L..24K 485, L24

    2019 doi

  55. [64]

    Lewis J. S. W., et al., 2022, @doi [ ] 10.1093/mnras/stac2383 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.tmp.2259L 516, 3389

    2022 doi

  56. [65]

    Liu Y., et al., 2021, @doi [ ] 10.3847/1538-4357/abd3a8 , https://ui.adsabs.harvard.edu/abs/2021ApJ...908..124L 908, 124

    2021 doi

  57. [66]

    J., Wyithe J

    Mack K. J., Wyithe J. S. B., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21561.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.425.2988M 425, 2988

    2012

  58. [67]

    D., Becker R

    McGreer I. D., Becker R. H., Helfand D. J., White R. L., 2006, @doi [ ] 10.1086/507767 , https://ui.adsabs.harvard.edu/abs/2006ApJ...652..157M 652, 157

    2006 doi

  59. [68]

    D., Mesinger A., D'Odorico V., 2015, @doi [ ] 10.1093/mnras/stu2449 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447..499M 447, 499

    McGreer I. D., Mesinger A., D'Odorico V., 2015, @doi [ ] 10.1093/mnras/stu2449 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447..499M 447, 499

    2015 doi

  60. [69]

    Meiksin A., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19362.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.417.1480M 417, 1480

    2011

  61. [70]

    G., et al., 2020, @doi [ ] 10.1093/mnras/staa327 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.1662M 493, 1662

    Mertens F. G., et al., 2020, @doi [ ] 10.1093/mnras/staa327 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.1662M 493, 1662

    2020 doi

  62. [72]

    Murray S., Greig B., Mesinger A., Mu \ n oz J., Qin Y., Park J., Watkinson C., 2020, @doi [The Journal of Open Source Software] 10.21105/joss.02582 , https://ui.adsabs.harvard.edu/abs/2020JOSS....5.2582M 5, 2582

    2020 doi

  63. [73]

    Nakane M., et al., 2024, @doi [ ] 10.3847/1538-4357/ad38c2 , https://ui.adsabs.harvard.edu/abs/2024ApJ...967...28N 967, 28

    2024 doi

  64. [74]

    Nakatani R., Fialkov A., Yoshida N., 2020, @doi [ ] 10.3847/1538-4357/abc5b4 , https://ui.adsabs.harvard.edu/abs/2020ApJ...905..151N 905, 151

    2020 doi

  65. [75]

    Naruse G., Hasegawa K., Kadota K., Tashiro H., Ichiki K., 2024, @doi [ ] 10.1088/1475-7516/2024/10/091 , https://ui.adsabs.harvard.edu/abs/2024JCAP...10..091N 2024, 091

    2024 doi

  66. [76]

    Nasir F., D'Aloisio A., 2020, @doi [ ] 10.1093/mnras/staa894 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.3080N 494, 3080

    2020 doi

  67. [77]

    arXiv:2407.18136

    Niu Q., Li Y., Xu Y., Guo H., Zhang X., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2407.18136 , https://ui.adsabs.harvard.edu/abs/2024arXiv240718136N p. arXiv:2407.18136

    2024 doi arXiv

  68. [78]

    B., Luki \'c Z., Hennawi J

    O \ n orbe J., Davies F. B., Luki \'c Z., Hennawi J. F., Sorini D., 2019, @doi [ ] 10.1093/mnras/stz984 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.4075O 486, 4075

    2019 doi

  69. [79]

    R., et al., 2015, @doi [ ] 10.1017/pasa.2015.7 , https://ui.adsabs.harvard.edu/abs/2015PASA...32....8O 32, e008

    Offringa A. R., et al., 2015, @doi [ ] 10.1017/pasa.2015.7 , https://ui.adsabs.harvard.edu/abs/2015PASA...32....8O 32, e008

    2015 doi

  70. [80]

    R., Choi J.-h., Yoshida N., Hirano S., Ahn K., 2016, @doi [ ] 10.3847/0004-637X/831/1/86 , https://ui.adsabs.harvard.edu/abs/2016ApJ...831...86P 831, 86

    Park H., Shapiro P. R., Choi J.-h., Yoshida N., Hirano S., Ahn K., 2016, @doi [ ] 10.3847/0004-637X/831/1/86 , https://ui.adsabs.harvard.edu/abs/2016ApJ...831...86P 831, 86

    2016 doi

  71. [81]

    Planck Collaboration et al., 2014, @doi [ ] 10.1051/0004-6361/201321591 , https://ui.adsabs.harvard.edu/abs/2014A&A...571A..16P 571, A16

    2014 doi

  72. [82]

    R., Loeb A., 2012, @doi [Reports on Progress in Physics] 10.1088/0034-4885/75/8/086901 , https://ui.adsabs.harvard.edu/abs/2012RPPh...75h6901P 75, 086901

    Pritchard J. R., Loeb A., 2012, @doi [Reports on Progress in Physics] 10.1088/0034-4885/75/8/086901 , https://ui.adsabs.harvard.edu/abs/2012RPPh...75h6901P 75, 086901

    2012 doi

  73. [83]

    Puchwein E., et al., 2023, @doi [ ] 10.1093/mnras/stac3761 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.6162P 519, 6162

    2023 doi

  74. [84]

    Qin Y., Mesinger A., Bosman S. E. I., Viel M., 2021, @doi [ ] 10.1093/mnras/stab1833 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.2390Q 506, 2390

    2021 doi

  75. [85]

    Semelin B., 2016, @doi [ ] 10.1093/mnras/stv2312 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.455..962S 455, 962

    2016 doi

  76. [86]

    Shao Y., Xu Y., Wang Y., Yang W., Li R., Zhang X., Chen X., 2023, @doi [Nature Astronomy] 10.1038/s41550-023-02024-7 , https://ui.adsabs.harvard.edu/abs/2023NatAs...7.1116S 7, 1116

    2023 doi

  77. [87]

    arXiv:2411.17094

    Shao Y., Sun T.-Y., Zhao M.-L., Zhang X., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2411.17094 , https://ui.adsabs.harvard.edu/abs/2024arXiv241117094S p. arXiv:2411.17094

    2024 doi arXiv

  78. [88]

    Shimabukuro H., Ichiki K., Inoue S., Yokoyama S., 2014, @doi [ ] 10.1103/PhysRevD.90.083003 , https://ui.adsabs.harvard.edu/abs/2014PhRvD..90h3003S 90, 083003

    2014 doi

  79. [89]

    Shimabukuro H., Ichiki K., Kadota K., 2020, @doi [ ] 10.1103/PhysRevD.102.023522 , https://ui.adsabs.harvard.edu/abs/2020PhRvD.102b3522S 102, 023522

    2020 doi

  80. [90]

    Shimabukuro H., Ichiki K., Kadota K., 2023, @doi [ ] 10.1103/PhysRevD.107.123520 , https://ui.adsabs.harvard.edu/abs/2023PhRvD.107l3520S 107, 123520

    2023 doi

  81. [91]

    W., et al., 2017, @doi [ ] 10.1051/0004-6361/201629313 , https://ui.adsabs.harvard.edu/abs/2017A&A...598A.104S 598, A104

    Shimwell T. W., et al., 2017, @doi [ ] 10.1051/0004-6361/201629313 , https://ui.adsabs.harvard.edu/abs/2017A&A...598A.104S 598, A104

    2017 doi

  82. [92]

    Singh S., et al., 2022, @doi [Nature Astronomy] 10.1038/s41550-022-01610-5 , https://ui.adsabs.harvard.edu/abs/2022NatAs...6..607S 6, 607

    2022 doi

  83. [93]

    Smith D. J. B., et al., 2016, in Reyl \'e C., Richard J., Cambr \'e sy L., Deleuil M., P \'e contal E., Tresse L., Vauglin I., eds, SF2A-2016: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics. pp 271--280 ( @eprint arXiv 1611.02706 )

    2016 arXiv

  84. [94]

    Spina B., Bosman S. E. I., Davies F. B., Gaikwad P., Zhu Y., 2024, @doi [ ] 10.1051/0004-6361/202450798 , https://ui.adsabs.harvard.edu/abs/2024A&A...688L..26S 688, L26

    2024 doi

  85. [95]

    arXiv:2407.14298

    Sun T.-Y., Shao Y., Li Y., Xu Y., Zhang X., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2407.14298 , https://ui.adsabs.harvard.edu/abs/2024arXiv240714298S p. arXiv:2407.14298

    2024 doi arXiv

  86. [96]

    Szoltinsk \'y S oltinsk \'y S oltinsk \'y T., et al., 2021, @doi [ ] 10.1093/mnras/stab1830 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.5818S 506, 5818

    2021 doi

  87. [97]

    S., Molaro M., Hatch N., Haehnelt M

    Szoltinsk \'y S oltinsk \'y S oltinsk \'y T., Bolton J. S., Molaro M., Hatch N., Haehnelt M. G., Keating L. C., Kulkarni G., Puchwein E., 2023, @doi [ ] 10.1093/mnras/stac3710 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.3027S 519, 3027

    2023 doi

  88. [98]

    Thyagarajan N., 2020, @doi [ ] 10.3847/1538-4357/ab9e6d , https://ui.adsabs.harvard.edu/abs/2020ApJ...899...16T 899, 16

    2020 doi

  89. [99]

    S., Hennawi J

    Tie S. S., Hennawi J. F., Wang F., Onorato S., Yang J., Ba \ n ados E., Davies F. B., O \ n orbe J., 2024, @doi [ ] 10.1093/mnras/stae2193 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.535..223T 535, 223

    2024 doi

  90. [100]

    M., et al., 2020, @doi [ ] 10.1093/mnras/staa414 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.4711T 493, 4711

    Trott C. M., et al., 2020, @doi [ ] 10.1093/mnras/staa414 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.4711T 493, 4711

    2020 doi

  91. [101]

    Villanueva-Domingo P., Ichiki K., 2023, @doi [ ] 10.1093/pasj/psab119 , https://ui.adsabs.harvard.edu/abs/2023PASJ...75S..33V 75, S33

    2023 doi

  92. [102]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , https://ui.adsabs.harvard.edu/abs/2020NatMe..17..261V 17, 261

    2020 doi

  93. [103]

    B., et al., 2015, @doi [ ] 10.1017/pasa.2015.26 , https://ui.adsabs.harvard.edu/abs/2015PASA...32...25W 32, e025

    Wayth R. B., et al., 2015, @doi [ ] 10.1017/pasa.2015.26 , https://ui.adsabs.harvard.edu/abs/2015PASA...32...25W 32, e025

    2015 doi

  94. [104]

    H., Haehnelt M

    Weinberger L. H., Haehnelt M. G., Kulkarni G., 2019, @doi [ ] 10.1093/mnras/stz481 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.1350W 485, 1350

    2019 doi

  95. [105]

    J., et al., 2010, @doi [ ] 10.1088/0004-6256/140/2/546 , https://ui.adsabs.harvard.edu/abs/2010AJ....140..546W 140, 546

    Willott C. J., et al., 2010, @doi [ ] 10.1088/0004-6256/140/2/546 , https://ui.adsabs.harvard.edu/abs/2010AJ....140..546W 140, 546

    2010 doi

  96. [106]

    Wolf J., et al., 2024, @doi [ ] 10.1051/0004-6361/202451035 , https://ui.adsabs.harvard.edu/abs/2024A&A...691A..30W 691, A30

    2024 doi

  97. [107]

    F., Davies F

    Wolfson M., Hennawi J. F., Davies F. B., Luki \'c Z., O \ n orbe J., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2309.05647 , https://ui.adsabs.harvard.edu/abs/2023arXiv230905647W p. arXiv:2309.05647

    2023 doi arXiv

  98. [108]

    Xu Y., Chen X., Fan Z., Trac H., Cen R., 2009, @doi [ ] 10.1088/0004-637X/704/2/1396 , https://ui.adsabs.harvard.edu/abs/2009ApJ...704.1396X 704, 1396

    2009 doi

  99. [109]

    Xu Y., Ferrara A., Chen X., 2011, @doi [ ] 10.1111/j.1365-2966.2010.17579.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.410.2025X 410, 2025

    2011

  100. [110]

    R., White R

    Zeimann G. R., White R. L., Becker R. H., Hodge J. A., Stanford S. A., Richards G. T., 2011, @doi [ ] 10.1088/0004-637X/736/1/57 , https://ui.adsabs.harvard.edu/abs/2011ApJ...736...57Z 736, 57

    2011 doi

  101. [111]

    W., Li W., 2020, @doi [ ] 10.1093/mnras/staa3011 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.3434Z 499, 3434

    Zheng Q., Wu X.-P., Guo Q., Johnston-Hollitt M., Shan H., Duchesne S. W., Li W., 2020, @doi [ ] 10.1093/mnras/staa3011 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.3434Z 499, 3434

    2020 doi

  102. [112]

    Zhu Y., et al., 2021, @doi [ ] 10.3847/1538-4357/ac26c2 , https://ui.adsabs.harvard.edu/abs/2021ApJ...923..223Z 923, 223

    2021 doi

  103. [113]

    Zhu Y., et al., 2022, @doi [ ] 10.3847/1538-4357/ac6e60 , https://ui.adsabs.harvard.edu/abs/2022ApJ...932...76Z 932, 76

    2022 doi

  104. [114]

    Zhu Y., et al., 2023, @doi [ ] 10.3847/1538-4357/aceef4 , https://ui.adsabs.harvard.edu/abs/2023ApJ...955..115Z 955, 115

    2023 doi

  105. [115]

    Zhu Y., et al., 2024, @doi [ ] 10.1093/mnrasl/slae061 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.533L..49Z 533, L49

    2024 doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.