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REVIEW 4 major objections 5 minor 65 references

Empirical estimation of host galaxy dispersion measure towards well localized fast radio bursts

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that the host galaxies of fast radio bursts contribute, on average, a rest-frame dispersion measure of about 80 pc cm^-3, not the fixed 50 pc cm^-3 widely assumed, and that this contribution scales with host stellar mass…

desk verdict A transparent, useful pilot measurement of host-galaxy DMs for 12 FRBs, but the headline precision is overstated and the mass correlation is partly built into the method. read the letter →

arxiv 2501.14063 v1 pith:FSVXYHTQ submitted 2025-01-23 astro-ph.GA

Lucas Bernales--Cortes , Nicolas Tejos , J. Xavier Prochaska , Ilya S. Khrykin , Lachlan Marnoch , Stuart D.Ryder , Ryan M. Shannon This is my paper · ORCID
classification astro-ph.GA
keywords fastradioburstsdispersionmeasurehostgalaxyH-alphaemissioncircumgalacticmediumMacquartrelationVLT/MUSEstarformationrate
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 tries to establish that the host galaxies of fast radio bursts contribute an average rest-frame dispersion measure of about 80 pc $cm^{-3}$, not the fixed 50 pc $cm^{-3}$ commonly assumed. The authors estimate this directly for twelve well-localized FRB hosts using VLT/MUSE spectroscopy, separating the interstellar gas contribution from the halo gas contribution and using H-$\alpha$ surface brightness near the burst to trace the ISM. They report that the host contribution grows with stellar mass and star formation rate and does not evolve strongly with redshift out to roughly z = 0.5. A sympathetic reader should care because the correlations give a physically motivated prior for DM_host in future FRB cosmology, and the average value, if right, corrects a standard assumption used across many FRB analyses.

What carries the argument

The central object is the decomposition DM_host^direct = DM_host^ISM + DM_host^halo. DM_ISM is obtained from observed H-$\alpha$ surface brightness via Reynolds' emission-measure relation, converted to DM with an equation that assumes Milky Way values for cloud volume-filling factor, internal density variation, inter-cloud contrast, and path length L_kpc = 0.15. DM_halo is obtained by converting stellar masses to halo masses with the Moster abundance-matching relation, placing the gas in a modified NFW profile with an assumed ionized baryon fraction f_hot = 55%, and integrating along the line of sight from the FRB's projected offset to the halo boundary r_200. This two-term sum carries the argument because it turns galaxy photometry and spectroscopy into a physical prediction for the host contribution to the dispersion measure.

What would settle it

Measure electron columns along the same FRB sightlines with an independent tracer, such as Faraday rotation measure combined with a magnetic-field estimate from the burst environment; if the resulting DM_ISM values disagree systematically with the H-alpha-based values beyond the stated ~30% systematic budget, the assumed clumpiness and path-length calibration fails.

Watch

Extended reading notes

Core claim

The paper reports an average host dispersion measure of <DM_host> = 80 +/- 11 pc $cm^{-3}$ with a standard deviation of 38 pc $cm^{-3}$ in the rest frame, obtained by summing an ISM term and a halo term for each of twelve host galaxies. It reports positive correlations of DM_host with stellar mass and star formation rate, with Pearson coefficients of 0.73 and 0.85 respectively, and a flat redshift evolution with power-law index $\alpha$ ~ 0.3 +/- 1.7. The direct estimates do not correlate with the indirect Macquart-relation estimates, even though the ensemble averages agree within uncertainties; the paper interprets this as evidence of additional DM contributions not captured by the model.

Load-bearing premise

The result depends on converting H-alpha brightness to a gas column using a Milky Way-calibrated relation that assumes dense, turbulent, clumpy gas with a fixed 0.15 kpc path length; if the ionized gas in FRB-host galaxies is smoother, thinner, or differently clumped, or if bursts lie outside the disks, the average DM_host could shift by factors of two to three.

Editorial extensions

If this is right

  • The commonly used fixed prior DM_host = 50 pc cm^-3 should give way to a broader distribution centered near 80 pc cm^-3 with a scatter of about 38 pc cm^-3 for FRB hosts in this redshift range.
  • New FRB hosts with high stellar mass or high star formation rate should be assigned larger DM_host priors, following the reported relations of roughly 43 pc cm^-3 per decade in stellar mass and 36 pc cm^-3 per decade in star formation rate.
  • If the flat redshift trend holds out to z ~ 0.5, no extra redshift-dependent host correction is needed for cosmological DM estimates in this range.
  • The lack of correlation between the direct and Macquart-based estimates implies that some FRB sightlines carry additional DM from the progenitor environment or intervening large-scale structure that the current two-term model does not capture.
  • Comparing the reported correlations with theoretical FRB population models can discriminate among progenitor scenarios, since different models predict different DM_host scaling with galaxy properties.

Reading between the lines

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

  • These twelve hosts are mostly star-forming galaxies near the main sequence; if future samples include quiescent early-type hosts, the DM_host versus star-formation-rate correlation could steepen or flatten, and the average near 80 pc cm^-3 might not generalize to all FRB environments.
  • A natural extension the paper does not build is to convert the reported DM_host(M_star, SFR) fits into a ready-made Bayesian prior for FRB cosmology, which is the direct use case implied by the conclusions.
  • The apparent low-redshift versus high-redshift discrepancy between direct and Macquart estimates could be tested with a larger sample; if it persists, it would argue for a redshift-dependent unmodeled contribution rather than small-number statistics.
  • The halo term could be validated independently by comparing its predictions with X-ray or absorption-line measurements of warm-hot halo gas in the same hosts.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents an empirical estimate of the host-galaxy dispersion measure (DM_host) for 12 well-localized fast radio bursts, using VLT/MUSE H-alpha observations for the interstellar medium contribution and abundance matching plus a modified Navarro-Frenk-White profile for the halo contribution. The authors report a rest-frame average of <DM_host> = 80 +/- 11 pc cm^-3 with a standard deviation of 38 pc cm^-3 and a claimed systematic uncertainty of ~30%. They also report positive correlations between DM_host and host stellar mass and star-formation rate, no strong correlation with redshift or projected offset, and no significant correlation between the direct and Macquart-based DM_host estimates. The paper is clearly written, uses a homogeneous observational sample, and includes detailed Monte Carlo uncertainty propagation.

Significance. If the central estimate were robust, this would be a valuable result: it would replace the commonly assumed fixed value of 50 pc cm^-3 with a homogeneous, observationally grounded distribution, and the reported correlations could improve priors for individual FRB analyses and constrain progenitor models. The paper's strengths include transparent Monte Carlo error propagation for both the H-alpha-based and halo-based components, a well-defined sample with public data, and an explicit comparison against an independent Macquart-relation estimate. The reported absence of redshift evolution and the weak offset dependence are also useful falsifiable statements. However, the systematic uncertainty treatment and the handling of non-detections currently prevent the quantitative headline claims from being accepted at face value.

major comments (4)
  1. [Section 4.5] The systematic uncertainty estimate samples the mean of DM_ISM uniformly in [0.5, 1.5] times the fiducial value, but Section 3.1.4 states that the parameters of Equation 6 can make DM_ISM 'up to 2 (3) times larger (smaller)'. The [0.5, 1.5] range does not cover the admitted factor-of-2-to-3 spread. Since DM_ISM dominates DM_direct (Table 4), the quoted ~30% systematic uncertainty is not a bound on the model assumptions, and the headline <DM_host> = 80 +/- 11 pc cm^-3 is not established at the stated precision. Please expand the systematic exploration to the full admitted range or revise the claimed precision accordingly.
  2. [Section 3.1.4] FRB20190611B and FRB20210117A have local H-alpha non-detections reported as 2-sigma upper limits in Table 3, yet they are included in the sample average as actual measurements, with DM_ISM values of approximately 32 and 20 pc cm^-3 (Table 4). Treating upper limits as detections can bias the mean upward, and the justification that systematic uncertainties are larger is not a statistical substitute for a proper treatment. Please provide a sensitivity test (e.g., setting DM_ISM to zero or to the upper limit bound for these objects) or use a survival-analysis approach, and discuss how the reported average changes.
  3. [Section 4.2] The reported positive correlation between DM_direct and stellar mass is largely built into the method: DM_halo is derived from stellar mass through abundance matching and a monotonic mNFW profile (Section 3.1.3, Equation 7), so a positive correlation is guaranteed by construction. The independent empirical content is in DM_ISM (Pearson coefficient 0.64, p = 0.03), which is only marginally significant. The manuscript should either explicitly frame the DM_host-M* correlation as a consequence of the assumed halo model rather than an independent empirical finding, or provide a test that removes the mechanical contribution (e.g., by examining the residual after subtracting the model expectation).
  4. [Section 3.1.3] The halo component adopts f_hot = 55% as a fixed fiducial value, and the systematic uncertainty analysis in Section 4.5 does not vary f_hot or the mNFW profile parameters. DM_halo contributes 14-44 pc cm^-3 across the sample (Table 4), so an uncertainty in f_hot of, say, +/-10-20% could shift the ensemble average by several pc cm^-3. Please include f_hot (and, if feasible, the profile parameters) in the systematic budget, or justify quantitatively why their effect is negligible compared with the DM_ISM uncertainties.
minor comments (5)
  1. [Abstract] The abstract in the posted version gives the mean as '80+/-11 pc/cc'; the rest of the paper uses 'pc cm^-3'. Please use consistent units throughout.
  2. [Figure 2] The caption states that the trend line 'has a slope of 1 by construction'; please clarify what parameter is fitted (e.g., a multiplicative offset) and how the 19% systematic difference is derived.
  3. [Section 3.1.4] The phrase 'these can make the DM_ISM up to 2 (3) times larger (smaller)' is ambiguous; please specify which combinations of f_f, zeta, tau, and L_kpc produce the larger and smaller extremes.
  4. [Table 3] For FRB20190711A, the global H-alpha flux is 16.1 +/- 16.6, which is consistent with zero. Please add a note explaining how this non-detection-level global value is handled and whether it affects the global/local comparison in Figure 2.
  5. [References] The entry for Prochaska et al. (2023) contains a garbled author name ('almannin'); please correct the citation.

Circularity Check

2 steps flagged · score 4.0 of 10

Correlations with stellar mass and SFR are partly built into the adopted mappings; the average DM_host estimate itself is not circular.

  1. self definitional [Section 4.2, after Eq. (12); method in Section 3.1.3]
    "A correlation of DMhost with stellar mass is expected, given that the larger the stellar mass, the larger the halo mass used to estimate DMhalo host (see Section 3.1.3). Indeed, we also see a positive correlation between DMhalo host and stellar mass with Pearson coefficient of 0.89 (with p-value of 1×10−3)."

    DMhalo host is not measured; it is computed from the host stellar mass through a monotonic abundance-matching relation (Moster et al. 2013) and an mNFW halo profile (Eq. 7). Any sample spanning a range of stellar masses therefore yields a positive DMhalo–M* correlation by construction. Reporting this correlation as an empirical result is a restatement of the adopted M*→Mhalo→DMhalo mapping, not a discovery about FRB hosts. The overall DMdirect–M* correlation inherits this built-in term, so the component correlation is definitional even though the ISM component is independently measured.

  2. renaming known result [Section 4.2, SFR correlation paragraph and SFMS substitution experiment]
    "A correlation with SFR could be expected given that galaxies with higher star formation activity should also have larger S(Hα), which is directly proportional to DMISM host in our estimations (Equations 5 and 6). ... This indicates that indeed, the SFMS can account for all the correlation observed between DMISM host and DMhalo host, and hence both DMISM host and stellar mass, and DMhalo host and SFR."

    DMISM host is defined as a monotonic function of the observed Hα surface brightness (Eqs. 5–6), and Hα luminosity is a standard SFR indicator tied to the star-forming main sequence. The reported DMISM–SFR correlation therefore largely restates the known Hα–SFR / SFMS scaling in DM units. The authors' own substitution experiment, which predicts SFR and Hα from stellar masses via the SFMS and recovers a stronger correlation, demonstrates that the claimed correlation is inherited from the input scaling relations rather than being a new empirical fact about FRB hosts. This is transparently acknowledged, but the correlation is still presented as a headline result for priors.

full rationale

The central quantitative claim, the ensemble average <DM_host> = 80±11 pc cm^-3, is not circular: it is a forward calculation from MUSE Hα surface brightnesses (Eqs. 5–6) plus stellar-mass-based halo estimates (Eqs. 7–9), with no parameter fitted to the target value. The reported correlations, however, are partly built into the method. DM_halo is a monotonic function of stellar mass through abundance matching and the mNFW profile, so the DM_halo–M* correlation (r=0.89) is definitional; the paper explicitly says it is expected. The DM_ISM–SFR correlation is likewise a recasting of the Hα/SFR and SFMS scaling relations, as the authors' own substitution experiment confirms. These reduce the evidential weight of the correlation claims but do not invalidate the average. Self-citations (Khrykin et al. 2024a for f_hot; Prochaska & Zheng 2019 for the mNFW profile) are stated model assumptions rather than circular evidence. The admitted mismatch between the [0.5,1.5] systematic sampling and the factor-2-to-3 parameter range cited in Sec. 3.1.4 is an internal robustness inconsistency, not circularity. Overall, the correlation results are partially constructed by the adopted mappings but are transparently labeled; the central average retains independent observational content.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central estimate rests on several numbers chosen by hand or taken from prior calibrations: the ISM clumpiness parameters and path length in Equation 6, the ionized baryon fraction f_hot, and the abundance matching and mNFW relations. These are not measured for the individual hosts and set the scale of DM_host.

free parameters (6)
  • f_f (volume filling factor) = 1 (assumed maximum)
    Chosen to represent a fully filled turbulent sightline in Equation 6; no measurement for these hosts.
  • zeta (intra-cloud density variation) = 1
    Assumed maximum internal turbulence in ionized clouds, following Tendulkar et al. 2017.
  • tau (inter-cloud density variation) = 2
    Assumed maximum density variation between clouds, following Tendulkar et al. 2017.
  • L_kpc (path length in host ISM) = 0.15 kpc
    Assumed FRB sightline path length similar to the Milky Way disk thickness.
  • f_hot (ionized baryon fraction in halo) = 55%
    Fiducial value taken from Khrykin et al. 2024a; directly scales the DM_halo density normalization.
  • Systematic variation range for DM_ISM = 0.5 to 1.5 times fiducial
    Ad hoc range used to quantify systematics in Section 4.5; not derived from data.
assumptions (6)
  • domain assumption Abundance matching (Moster et al. 2013) maps stellar mass to halo mass monotonically
    Used in Section 3.1.3 to infer halo mass for each host; scatter is propagated but the relation itself is assumed.
  • domain assumption mNFW density profile (Prochaska and Zheng 2019) with y0=2 and alpha=2 describes host halo gas
    Adopted in Equation 7 to compute DM_halo; alternative profiles would change the estimate.
  • domain assumption Equation 6 from Tendulkar et al. 2017, calibrated in the Milky Way, applies to FRB hosts
    Converts H-alpha emission measure to DM_ISM; the paper states this calibration may not apply generally.
  • domain assumption Reynolds (1977) relation between H-alpha surface brightness and emission measure at T = 10^4 K
    Used in Equation 5 to derive EM from S(H-alpha).
  • domain assumption The Macquart relation average gives the mean IGM DM at each redshift
    Used in Equation 10 to compute DM_Macquart; the paper acknowledges sightline scatter around the relation.
  • standard math NE2001 model for Milky Way ISM DM
    Used in Section 3.2.1 to subtract the Galactic contribution from each FRB.

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Cite this review

Pith. "Pith review of Empirical estimation of host galaxy dispersion measure towards well localized fast radio bursts." pith.science (2026). https://pith.science/paper/FSVXYHTQ

@misc{pith2026250114063,
  author       = {Pith},
  title        = {Pith review of: Empirical estimation of host galaxy dispersion measure towards well localized fast radio bursts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FSVXYHTQ}},
  note         = {Machine review of arXiv:2501.14063}
}
read the original abstract

Fast radio bursts (FRBs) are very energetic pulses of unknown physical origin. These can be used to study the intergalactic medium (IGM) thanks to their dispersion measure (DM). The DM has several contributions that can be measured (or estimated), including the contribution from the host galaxy itself, DM_host. In this work, we empirically estimate DM_host for a sample of 12 galaxy hosts, using a direct method based solely on the properties of the host galaxies themselves (DM_host_dir). We use VLT/MUSE observations of the FRB hosts for estimating DM_host_dir. The method relies on estimating the DM contribution of both the FRB host galaxy's interstellar medium and its halo separately. For comparison purposes, we also provide an alternative indirect method to estimate DM_host based on the Macquart relation (DM_host_mq). We find an average <DM_host> = 80+/-11 pc/cc with a standard deviation of 38 pc/cc (in the rest-frame) based on our direct method, with a systematic uncertainty of 30%. We report positive correlations between DM_host and both the stellar masses and the star-formation rates of their host galaxies. In contrast, we do not find any strong correlation between DM_host and neither redshift nor the projected distances to the FRB hosts centers. Finally, we do not find any strong correlation between DM_host_dir and DM_host_mq, although their average values are consistent. Our reported correlations could be used to improve the priors used in establishing DM_host for individual FRBs. Similarly, such correlations and the lack of a strong redshift evolution can be used to constrain models for the progenitor of FRBs. However, the lack of a DM_host_dir and DM_host_mq correlation indicates that there may still be contributions to the DM of FRBs not included in our modeling, e.g. large DMs from the FRB progenitor and/or intervening large-scale structures not accounted for in DM_host_mq.

Figures

Figures reproduced from arXiv: 2501.14063 by the authors.

Figure 1
Figure 1. Upper: FRB20191001A host emission integrated at the nar￾row band (NB) encompassing Hα using the same wavelength range as shown in the bottom panel. The blue circle is centered at the FRB local￾ization and has a radius of 0.4 ′′ and the dashed black ellipse represents the actual FRB position uncertainty. Lower: The integrated spectrum of the Hα emission within the blue circle (orange). We fit this emission with a Gau… view at source ↗
Figure 2
Figure 2. Comparison between the local and global inferred surface bright￾ness of Hα, obtained from MUSE cubes, for our sample. The trend line (dark orange) has a slope of 1 by construction. The identity line (dashed line) corresponds to the 1:1 relation. ρb = ρ 0 b y 1−α(y0 + y) 2+α (7) where y0 = 2 and α = 2, y ≡ c(r/r200), with c being the concen￾tration parameter and r200 is defined as the radius within which the average … view at source ↗
Figure 4
Figure 4. Estimates of DMhost for the 12 FRB hosts, which shows our empirical direct estimate DMdirect host (black points). The main panel shows each contribution of DMdirect host , DMhalo host in dark blue bars and DMISM host in light-blue bars. The upper panel is the histogram for DMdirect host where the mean and the standard deviation (±1σ) of the distribution are represented by the solid and dashed vertical lines, respect… view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Relation between DMdirect host and DMMacquart host (dark points), with error bars showing the statistical uncertainties of DMdirect host (we do not estimate uncertainties of our DMMacquart host values). The dashed line corresponds to the identity line (i.e. the 1:1 rel…
Figure 6
Figure 6. Figure 6: Rest-frame dispersion measure (DM) as a function of stellar mass of the host (left), star-formation rate of the host (SFR; middle), and projected offset from the center of the host (right). The black points correspond to DMdirect host , while the light-blue and the dar…
Figure 7
Figure 7. Figure 7: Top panel: DMhost as a function of redshift, black and red points correspond to DMdirect host and DMMacquart host , respectively. The black line is the tendency of DMdirect host . Negative values for DMMacquart host are nonphysi￾cal; we nevertheless report them as such…

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Works this paper leans on

65 extracted references · 49 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    T., et al

    Aggarwal , K., Budav \'a ri , T., Deller , A. T., et al. 2021, , 911, 95

    work page 2021

  4. [4]

    R., Macquart, J

    Arcus, W. R., Macquart, J. P., Sammons, M. W., James, C. W., & Ekers, R. D. 2020, MNRAS, 000, 1

    work page 2020

  5. [5]

    2010, in Ground-based and Airborne Instrumentation for Astronomy III, Vol

    Bacon, R., Accardo, M., Adjali, L., et al. 2010, in Ground-based and Airborne Instrumentation for Astronomy III, Vol. 7735 (SPIE), 773508

    work page 2010

  6. [6]

    W., Deller, A

    Bannister, K. W., Deller, A. T., Phillips, C., et al. 2019, Science, 365, 565

    2019

  7. [7]

    X., Mannings , A

    Baptista , J., Prochaska , J. X., Mannings , A. G., et al. 2024, , 965, 57

    work page 2024

  8. [8]

    W., Lenc, E., et al

    Bhandari, S., Bannister, K. W., Lenc, E., et al. 2020, The Astrophysical Journal Letters, 901, L20

    work page 2020

Show all 65 references
  1. [9]

    C., Scott , D

    Bhandari , S., Gordon , A. C., Scott , D. R., et al. 2023, , 948, 67

    2023

  2. [10]

    E., Aggarwal , K., et al

    Bhandari , S., Heintz , K. E., Aggarwal , K., et al. 2022, , 163, 69

    2022

  3. [11]

    2024, , 634, 1065

    Bhardwaj , M., Lee , J., & Ji , K. 2024, , 634, 1065

    2024

  4. [12]

    Brinchmann , J., Charlot , S., White , S. D. M., et al. 2004, , 351, 1151

    2004

  5. [13]

    N., Gordon , A

    Caleb , M., Driessen , L. N., Gordon , A. C., et al. 2023, [ [arXiv] 2302.09754 ]

    2023 arXiv

  6. [14]

    C., et al

    Calzetti , D., Armus , L., Bohlin , R. C., et al. 2000, , 533, 682

    2000

  7. [15]

    2015, , 219, 8

    Chang , Y.-Y., van der Wel , A., da Cunha , E., & Rix , H.-W. 2015, , 219, 8

    2015

  8. [16]

    S., Simha, S., Mannings, A., et al

    Chittidi, J. S., Simha, S., Mannings, A., et al. 2020, The Astrophysical Journal, 922, 173

    2020

  9. [17]

    Cordes , J. M. & Chatterjee , S. 2019, , 57, 417

    2019

  10. [18]

    M., Joseph, T., & Lazio, W

    Cordes, J. M., Joseph, T., & Lazio, W. 2003, arXiv e-prints, arXiv:astro

    2003

  11. [19]

    M., Wharton , R

    Cordes , J. M., Wharton , R. S., Spitler , L. G., Chatterjee , S., & Wasserman , I. 2016, arXiv e-prints, arXiv:1605.05890

    2016 arXiv

  12. [20]

    2024, arXiv e-prints, arXiv:2407.04095

    Dall'Osso , S., La Placa , R., Stella , L., Bakala , P., & Possenti , A. 2024, arXiv e-prints, arXiv:2407.04095

    2024 arXiv

  13. [21]

    K., Deller, A

    Day, C. K., Deller, A. T., Shannon, R. M., et al. 2020, Monthly Notices of the Royal Astronomical Society, 497, 3335

    2020

  14. [22]

    2015, EsoRex: ESO Recipe Execution Tool , Astrophysics Source Code Library, record ascl:1504.003

    ESO CPL Development Team . 2015, EsoRex: ESO Recipe Execution Tool , Astrophysics Source Code Library, record ascl:1504.003

    2015

  15. [23]

    Fitzpatrick , E. L. & Massa , D. 2007, , 663, 320

    2007

  16. [24]

    C., Fong , W.-f., Kilpatrick , C

    Gordon , A. C., Fong , W.-f., Kilpatrick , C. D., et al. 2023, , 954, 80

    2023

  17. [25]

    E., Xavier Prochaska, J., Simha, S., et al

    Heintz, K. E., Xavier Prochaska, J., Simha, S., et al. 2020, Host galaxy properties and offset distributions of fast radio bursts: Implications for their progenitors

    2020

  18. [26]

    W., Ghosh, E

    James , C. W., Ghosh, E. M., Prochaska, J. X., et al. 2022 a , Monthly Notices of the Royal Astronomical Society, 516, 4862

    2022

  19. [27]

    W., Prochaska , J

    James , C. W., Prochaska , J. X., Macquart , J. P., et al. 2022 b , , 509, 4775

    2022

  20. [28]

    S., Ata , M., Lee , K.-G., et al

    Khrykin , I. S., Ata , M., Lee , K.-G., et al. 2024b, , 973, 151

  21. [29]

    S., Sorini , D., Lee , K.-G., & Dav \'e , R

    Khrykin , I. S., Sorini , D., Lee , K.-G., & Dav \'e , R. 2024a, , 529, 537

  22. [30]

    2022, , 602, 585

    Kirsten , F., Marcote , B., Nimmo , K., et al. 2022, , 602, 585

    2022

  23. [31]

    O., Mao , S

    Kovacs , T. O., Mao , S. A., Basu , A., et al. 2024, arXiv e-prints, arXiv:2407.16748

    2024 arXiv

  24. [32]

    S., Simha , S., et al

    Lee , K.-G., Khrykin , I. S., Simha , S., et al. 2023, , 954, L7

    2023

  25. [33]

    2019, , 622, A180

    Logro \ n o-Garc \' a , R., Vilella-Rojo , G., L \'o pez-Sanjuan , C., et al. 2019, , 622, A180

    2019

  26. [34]

    R., Bailes, M., McLaughlin, M

    Lorimer, D. R., Bailes, M., McLaughlin, M. A., Narkevic, D. J., & Crawford, F. 2007, Science, 318, 777

    2007

  27. [35]

    Macquart, J.-P., Bailes, M., Bhat, N. D. R., et al. 2010, Publications of the Astronomical Society of Australia, Volume 27, Issue 3, pp. 272-282., 27, 272

    2010

  28. [36]

    P., Prochaska, J

    Macquart, J. P., Prochaska, J. X., McQuinn, M., et al. 2020, Nature, 581, 391

    2020

  29. [37]

    G., Fong , W.-f., Simha , S., et al

    Mannings , A. G., Fong , W.-f., Simha , S., et al. 2021, , 917, 75

    2021

  30. [38]

    W., et al

    Marcote, B., Nimmo, K., Hessels, J. W., et al. 2020, Nature, 577, 190

    2020

  31. [39]

    Mathews , W. G. & Prochaska , J. X. 2017, , 846, L24

    2017

  32. [40]

    Michilli , D., Seymour , A., Hessels , J. W. T., et al. 2018, , 553, 182

    2018

  33. [41]

    2023, , 518, 539

    Mo , J.-F., Zhu , W., Wang , Y., Tang , L., & Feng , L.-L. 2023, , 518, 539

    2023

  34. [42]

    P., Naab, T., & White, S

    Moster, B. P., Naab, T., & White, S. D. 2013, Monthly Notices of the Royal Astronomical Society, 428, 3121

    2013

  35. [43]

    H., Aggarwal , K., Li , D., et al

    Niu , C. H., Aggarwal , K., Li , D., et al. 2022, , 606, 873

    2022

  36. [44]

    E., Burkhart , B., Lu , W., Ponnada , S

    Orr , M. E., Burkhart , B., Lu , W., Ponnada , S. B., & Hummels , C. B. 2024, , 972, L26

    2024

  37. [45]

    Osterbrock , D. E. & Ferland , G. J. 2006, Astrophysics of gaseous nebulae and active galactic nuclei

    2006

  38. [46]

    2020, Research in Astronomy and Astrophysics, 20, 027

    Ouyed , R., Leahy , D., & Koning , N. 2020, Research in Astronomy and Astrophysics, 20, 027

    2020

  39. [47]

    Petroff, E., Hessels, J. W. T., & Lorimer, D. R. 2019, The Astronomy and Astrophysics Review, 27, 4

    2019

  40. [48]

    Petroff , E., Hessels , J. W. T., & Lorimer , D. R. 2022, , 30, 2

    2022

  41. [49]

    2007, , 671, 1550

    Pflamm-Altenburg , J., Weidner , C., & Kroupa , P. 2007, , 671, 1550

    2007

  42. [50]

    2020, , 641, A6

    Planck Collaboration , Aghanim , N., Akrami , Y., et al. 2020, , 641, A6

    2020

  43. [51]

    2019, , 821, 1

    Platts , E., Weltman , A., Walters , A., et al. 2019, , 821, 1

    2019

  44. [52]

    2024, [ [arXiv] 2408.04899 ]

    Prayag , V., Levin , L., Geyer , M., et al. 2024, [ [arXiv] 2408.04899 ]

    2024 arXiv

  45. [53]

    X., Simha, S., almannin, et al

    Prochaska, J. X., Simha, S., almannin, et al. 2023, FRBs/FRB: Release to sync with Gordon et al. 2023

    2023

  46. [54]

    Prochaska, J. X. & Zheng, Y. 2019, Monthly Notices of the Royal Astronomical Society, 485, 648

    2019

  47. [55]

    M., Bezuidenhout, M

    Rajwade, K. M., Bezuidenhout, M. C., Caleb, M., et al. 2022, Monthly Notices of the Royal Astronomical Society, 514, 1961

    2022

  48. [56]

    Reynolds , R. J. 1977, 216, 433

    1977

  49. [57]

    M., Bannister , K

    Shannon , R. M., Bannister , K. W., Bera , A., et al. 2024, arXiv e-prints, arXiv:2408.02083

    2024 arXiv

  50. [58]

    W., Bhardwaj , M., et al

    Shin , K., Masui , K. W., Bhardwaj , M., et al. 2023, , 944, 105

    2023

  51. [59]

    X., et al

    Simha , S., Lee , K.-G., Prochaska , J. X., et al. 2023, , 954, 71

    2023

  52. [60]

    P., Bassa, C

    Tendulkar, S. P., Bassa, C. G., Cordes, J. M., et al. 2017, The Astrophysical Journal, 834, L7

    2017

  53. [61]

    & van Leeuwen , J

    Wang , Y. & van Leeuwen , J. 2024, , 690, A377

    2024

  54. [62]

    M., Palsa , R., Streicher , O., et al

    Weilbacher , P. M., Palsa , R., Streicher , O., et al. 2020, , 641, A28

    2020

  55. [63]

    2024, in American Astronomical Society Meeting Abstracts, Vol

    Woodland , M., Mannings , A., Prochaska , J., et al. 2024, in American Astronomical Society Meeting Abstracts, Vol. 243, American Astronomical Society Meeting Abstracts, 359.07

    2024

  56. [64]

    M., Manchester, R

    Yao, J. M., Manchester, R. N., & Wang, N. 2017, The Astrophysical Journal, 835, 29

    2017

  57. [65]

    Q., Yu , H., He , J

    Zhang , G. Q., Yu , H., He , J. H., & Wang , F. Y. 2020, , 900, 170

    2020

Pith tools

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