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REVIEW 3 major objections 5 minor 5 references

If this paper is right, the Rubin Observatory's LSST alone will identify most fast radio burst host galaxies, and photometric redshifts will barely hurt Hubble-constant measurements.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-02 19:55 UTC pith:HH7AAYMP

load-bearing objection Useful planning forecast with reproducible code; treat 65%/81% as upper limits on host identification until association bias is folded in. the 3 major comments →

arxiv 2603.00371 v2 pith:HH7AAYMP submitted 2026-02-27 astro-ph.HE astro-ph.CO

Fast Radio Bursts in the Era of the Vera C. Rubin Observatory's Legacy Survey of Space and Time

C.W. James , B. Smith , K. Dage , A.L. Chies Santos , K.W. Bannister , M. Caleb , J.F. Crenshaw , A.T. Deller
show 7 more authors
K.G. Lee L. Marnoch K.M. Rajwade S.D. Ryder R.M. Shannon B. Stappers T. Zhang
This is my paper
classification astro-ph.HE astro-ph.CO
keywords fast radio burstsFRB host galaxiesLSSTRubin Observatoryphotometric redshiftsHubble constantdispersion measureASKAP / MeerKAT
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tries to predict how often the Vera C. Rubin Observatory's LSST will identify the host galaxies of fast radio bursts (FRBs) detected in coherent modes by ASKAP and MeerKAT, and whether LSST photometric redshifts will suffice for cosmology. The authors combine a model of FRB host galaxy r-band brightness as a function of redshift with simulated redshift–dispersion measure distributions for the two radio surveys. They find that a single LSST visit should reveal 65% of ASKAP/CRACO hosts, and the full 10-year co-added images 81% of MeerKAT coherent hosts. They also argue that photometric redshift errors cost only about 7% in H0 precision for CRACO, while missing faint high-redshift hosts is the larger problem, degrading precision by roughly 47% (and 62% when combined). The point is to show that dedicated optical follow-up may become largely unnecessary for FRB cosmology.

Core claim

The paper claims that LSST images alone will identify most FRB host galaxies detected by coherent radio surveys, without dedicated optical follow-up. Combining a model of host galaxy r-band magnitudes with predicted redshift–dispersion measure distributions for ASKAP's CRACO and MeerKAT's coherent mode, the authors estimate that a single LSST visit (r-band limit 24.7) will reveal 65% of CRACO hosts, and that 10-year co-added images (r-band limit 27.5) will reveal 81% of MeerKAT coherent hosts. They further claim that using LSST photometric redshifts instead of spectroscopic ones inflates H0 uncertainty by only about 7% for CRACO (3% for MeerKAT), whereas missing faint, high-redshift hosts is

What carries the argument

The central object is a model of the FRB host galaxy r-band magnitude distribution m_r(z), taken to be Gaussian with redshift-dependent mean and scatter derived from 23 hosts with optical spectra, extrapolated without evolution out to z ~ 2. This is convolved with the FRB redshift–dispersion measure distribution predicted for each radio survey (the paper's simulation of the z–DM relation), and the resulting m_r distribution is compared against LSST r-band magnitude limits of 24.7 (single visit) and 27.5 (10-year co-add). For the H0 analysis, the same machinery is extended with a model of photometric redshift errors (Gaussian, sigma_z = 0.035) and the single-visit magnitude limit.

Load-bearing premise

The visibility fractions rest on the assumption that the r-band magnitudes of FRB host galaxies follow a normal distribution with the mean and scatter measured from 23 mostly low-redshift hosts, unchanged out to z ~ 2; if real hosts are fainter or evolve with redshift, the 65% and 81% figures are too optimistic.

What would settle it

Take FRBs localized by ASKAP/CRACO over the first few years of LSST, count the fraction with a host galaxy brighter than r = 24.7 within the localization region, and compare to the predicted 65%; a similar check at r = 27.5 for MeerKAT hosts, or a measurement of the m_r distribution at z > 0.5, would confirm or refute the model.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • A single LSST visit should remove the need for dedicated optical follow-up for 65% of ASKAP/CRACO FRB hosts, freeing telescope time.
  • The 10-year LSST co-adds should identify 81% of MeerKAT coherent hosts, with similar expectations for SKA-Mid.
  • Photometric redshifts from LSST are nearly as good as spectroscopic redshifts for H0 from FRBs: only ~7% worse precision for CRACO.
  • The main enemy is not photo-z noise but missing faint hosts: H0 precision degrades by ~47% for CRACO when faint hosts are lost, and ~62% with photo-z errors combined.
  • The prediction method is general and can be applied to any optical follow-up survey.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the host magnitude model is even approximately correct, the LSST–CRACO combination will yield a nearly complete low-redshift host sample, allowing the community to directly measure the m_r(z) distribution and test the no-evolution assumption within a few years.
  • Because missing high-redshift hosts cost far more than photo-z noise, a mixed strategy — LSST identification for most hosts plus targeted deep imaging of the faintest candidates — may be the most efficient path to FRB cosmology.
  • The photo-z treatment ignores catastrophic outliers; if LSST photo-zs produce a significant tail of large errors, the small H0 degradation quoted here would be an underestimate.
  • The same LSST maps could power field-level baryon inference along FRB sight lines, but only if photo-z accuracy at z < 1.5 is sufficient; that application is flagged by the paper as needing further work.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper forecasts how many FRB host galaxies detected by ASKAP's CRACO and MeerKAT's coherent mode will be observable in LSST, using a model of the r-band host magnitude distribution from Marnoch et al. (2023) convolved with redshift distributions from the ZDM code. It predicts that 65% of CRACO hosts and 81% of MeerKAT coherent hosts will be detectable in single-visit and co-added LSST images respectively. The second part simulates a simplified H0 analysis with photometric redshifts, finding that the adopted photo-z error (sigma_z=0.035) degrades H0 precision by ~7% for CRACO, while missing faint hosts degrades it by ~47%, or ~62% combined.

Significance. If the headline numbers hold, the paper provides a strong practical argument that LSST can relieve the optical follow-up bottleneck for FRB cosmology. The approach is transparent: it uses publicly available code (ZDM), observational inputs, and explicitly states its model assumptions. However, the central claim rests on a magnitude-limit calculation presented as a host-identification forecast, while the association step is not modeled. The host-magnitude distribution is extrapolated from 23 low-redshift hosts with no evolution, and the photo-z simulation uses one algorithm's error excluding outliers. The paper is therefore a useful planning estimate, but the quantitative headline figures should be treated as upper limits until the association step and systematic uncertainties are incorporated.

major comments (3)
  1. [§2.4 / Abstract / Table 1] The abstract and §2.4 state that LSST will 'identify' 65% of ASKAP/CRACO hosts and 81% of MeerKAT hosts, but the calculation in §2.4 counts only hosts with m_r below the LSST 5σ limits (24.7 and 27.5). The association step is not modeled. The paper itself notes in §2.2 (citing B. Anderson et al., in prep) that requiring a 90% posterior association probability is significantly biased against hosts with m_r > 22. At the co-add limit of 27.5, the field-galaxy surface density is far higher than at 22, so a non-negligible fraction of detected galaxies will be chance coincidences, especially for MeerKAT's larger localization uncertainties. Thus Table 1 gives upper limits on photometric detection, not the fraction of uniquely identified hosts. The 81% co-add figure is especially exposed. Please either revise the claims to 'photometrically detectable' or include a host-association model (e.g., P
  2. [§2.2 / Figure 1 / Table 1] The host magnitude model is built from 23 FRB hosts with z ≲ 0.5, extrapolated unchanged to z ~ 2 with no galaxy evolution, and assumes a normal distribution in m_r at each z. The paper acknowledges these limitations, but the headline fractions in Table 1 and the abstract are point estimates with no systematic uncertainty. Since the 65% and 81% figures are direct outputs of this assumed distribution, the manuscript should quantify how these fractions vary under plausible perturbations: e.g., adopting a different scatter, including a simple luminosity-evolution term, or using the empirical step-function counting used in earlier work (Marnoch et al.; Caleb et al.). Without such a robustness check, the abstract's precise percentages overstate the confidence of the forecast.
  3. [§3.1 / Table 2] The photo-z simulation adopts sigma_z = 0.035 from the kNN algorithm in the DP1 study (T. Zhang et al. 2025) after excluding outliers, while the same study reports 10–20% outliers with large redshift errors. The 6.8% (CRACO) and 3.3% (MeerKAT) precision losses in Table 2 therefore assume that outliers are perfectly removed by quality cuts. Since the abstract states 'only 7%' without this caveat, the simulation should either include a realistic outlier fraction (with a statement of how quality cuts would remove them) or the abstract should be qualified. The simplified likelihood also omits selection biases and host-DM systematics noted in §3.1; this is acceptable for an order-of-magnitude estimate, but the abstract's precision claim should reflect that caveat.
minor comments (5)
  1. [General] The text uses 'dim' in the abstract but 'faint' in the body; please be consistent. Also, 'MeerKA T' is split across line breaks in several places (e.g., Section 2.3, 2.4, Table 2), which should be fixed.
  2. [Table 2] The column header 'ASKAPO/CRACO' contains a typo ('ASKAPO'). Also, the abstract rounds 46.8% to 47% and 62.4% to 62%, which is fine, but the table values should be consistent in the text.
  3. [Table 1 / Figure 2] Table 1 gives no uncertainties, and Figure 2 shows only the model curve without indicating the 23-host sample scatter. Adding error bands or a shaded region would help readers assess the robustness of the 65% and 81% numbers.
  4. [Figure 3] The caption says 'solid curves' for the redshift distributions and 'dashed'/'dotted' for the LSST-accessible fractions, but the figure legend is not reproduced here. Please ensure the line styles are clearly labeled and the dashed/dotted description matches the visible plot.
  5. [References] The paper relies on several 'in prep' or 'in prep., 2026' works for key assumptions (B. Anderson et al.; Yuanming Wang et al.; L. Spitler, E. Keane et al.). While this is acceptable for a forward-looking planning paper, please check that the most recent public versions are cited where possible, and consider adding a short description of the B. Anderson et al. association-bias result in the text, since it directly motivates the major concern in §2.4.

Circularity Check

0 steps flagged

No significant circularity; the forecasts are direct applications of observationally anchored input models.

full rationale

The paper's derivation chain is a forward model, not a circular reduction. It takes the FRB host r-band magnitude distribution from Marnoch et al. (2023), which is based on 23 observed host galaxies, converts it to a normal distribution in m_r(z), and convolves it with FRB redshift distributions from the public ZDM code using survey parameters for ASKAP/CRACO and MeerKAT. The headline fractions (65%, 81%) are then simply the integrated probabilities p(m_r < 24.7) and p(m_r < 27.5) under that model. No parameter is fitted to these target fractions, and the target result is not an input to the model. The self-citations (Marnoch et al. 2023; Hoffmann et al. 2025; ZDM; T. Zhang et al. 2025) are load-bearing but independently grounded: they use external observational data, public code, or survey measurements that are falsifiable outside this paper. The paper itself acknowledges the main limitations — no galaxy evolution, possible incompleteness bias, and the host-association bias against m_r > 22 from B. Anderson et al. (in prep). These are correctness and metric-mismatch concerns (the abstract's 'identify' arguably overstates what is strictly 'photometrically visible'), not circularity of the derivation. The H0 simulation is likewise a Monte Carlo propagation of assumed photo-z errors and magnitude cuts, not a fitted-input-called-prediction. Overall, the derivation is self-contained and externally testable, so no circular step is present.

Axiom & Free-Parameter Ledger

1 free parameters · 7 axioms · 0 invented entities

The central forecast depends on several externally grounded but approximate inputs: the Marnoch host magnitude model from 23 low-redshift hosts, the Hoffmann/ZDM FRB population model, LSST depth assumptions, and a single ad hoc photo-z error. No new physical entities are introduced.

free parameters (1)
  • Photometric redshift rms error sigma_z = 0.035
    Hand-picked from the kNN algorithm in T. Zhang et al. (2025) DP1 study; directly controls the photo-z impact on H0. The authors state they 'arbitrarily use the kNN algorithm'.
axioms (7)
  • domain assumption FRB host galaxy r-band magnitudes follow a normal distribution with mean mu_r(z) and standard deviation sigma_r(z) derived from the 23 hosts in Marnoch et al. (2023).
    Core input for the visible-host fraction. Extrapolated to z~2 without evolution and with a small, low-redshift sample (Section 2.2).
  • domain assumption FRB redshift and dispersion-measure distributions are described by the ZDM code with population parameters from Hoffmann et al. (2025).
    Used to produce the redshift distributions for CRACO and MeerTRAP (Section 2.3). These are prior self-cited models.
  • domain assumption LSST 5-sigma limiting magnitudes are m_lim,r = 24.7 (single visit) and 27.5 (10-year co-add) as per Bianco et al. (2022).
    Instrument performance assumption taken from external survey design literature (Section 2.1).
  • ad hoc to paper Photometric redshift errors are Gaussian with sigma_z = 0.035 and no outliers, based on the kNN algorithm from T. Zhang et al. (2025) DP1 study.
    The authors explicitly choose kNN arbitrarily and ignore outlier populations, which they acknowledge is a simplification (Section 3.1).
  • ad hoc to paper The simplified H0 likelihood analysis uses only the z-DM relation and ignores other systematics, outliers, and selection biases (e.g., host DM vs. luminosity).
    Authors state the analysis is 'overly simplified' and list unmodeled biasing effects in Section 3.1 and Section 4.
  • domain assumption Planck cosmology is assumed (Planck Collaboration et al. 2016).
    Standard cosmological model used for redshift-DM conversions (Section 2.3).
  • ad hoc to paper Host association bias against faint hosts (m_r > 22) as per B. Anderson et al. (in prep., 2026) is accepted.
    Used to argue that existing host catalogues are incomplete above m_r > 22; this is an unpublished reference.

pith-pipeline@v1.3.0-alltime-deepseek · 16063 in / 12249 out tokens · 117998 ms · 2026-08-02T19:55:07.769876+00:00 · methodology

0 comments
read the original abstract

Identifying the host galaxies of fast radio bursts (FRBs), and comparing their redshifts and dispersion measures, has unlocked a new probe of the cosmological distribution of ionised gas. However the necessary optical observations to identify FRB hosts, and measure their redshifts, are becoming increasingly onerous as the detection rate of precisely localised FRBs increases. Here we analyse the ability of the Legacy Survey of Space and Time (LSST), being conducted by the Vera C. Rubin Observatory, to identify FRB host galaxies, and the utility of LSST photometric redshifts for FRB cosmology. By combining a model of FRB host galaxy r-band magnitudes, $m_r$, with predictions for the FRB z-DM distribution, we create a method to predict the $m_r(z)$ distribution for the host galaxies of FRBs detected by radio surveys. We then predict these distributions for the coherent modes of the Australian Square Kilometre Array Pathfinder (ASKAP) and MeerKAT. We find that even a single visit with Rubin will be able to identify 65% of FRB host galaxies detected by ASKAP's coherent upgrade, `CRACO'; while the final 10 year co-added images will identify 81% of those from MeerKAT's tied array beams. We also simulate the impact of using photometric redshifts for a simplified analysis to determine $H_0$, finding that estimated photo-z errors result in a decreased precision of only 7% on $H_0$ for ASKAP's CRACO system. The impact of missing faint FRB hosts, which are likely at higher redshifts, is more significant, and might degrade sensitivity to $H_0$ by 47%, or 62% when combined with photo-z errors. All told, Rubin's LSST will be an incredibly powerful survey for facilitating FRB cosmology, although supplemental observations may be useful for particularly faint and distant host galaxies.

Figures

Figures reproduced from arXiv: 2603.00371 by A.L. Chies Santos, A.T. Deller, B. Smith, B. Stappers, C.W. James, J.F. Crenshaw, K. Dage, K.G. Lee, K.M. Rajwade, K.W. Bannister, L. Marnoch, M. Caleb, R.M. Shannon, S.D. Ryder, T. Zhang.

Figure 2
Figure 2. Figure 2: Fraction of FRB hosts visible to the Rubin Observatory for optical r-band limits corresponding to single visit and 10 year co-adds observations. Both Marnoch et al. (2023) and Caleb et al. (2025) have used these data by effectively treating the m(z) distribution as a series of δ-functions at the calculated values of the 23 hosts, so that the probability p(mr < mlim r ) can be obtained by counting the fract… view at source ↗
Figure 4
Figure 4. Figure 4: r-band magnitude distribution of FRB host galaxies expected to be detected by ASKAP’s CRACO system, and MeerKAT. Also shown for comparison are the magnitude limits from LSST single visits and 10 year co-adds. We can now evaluate, for any given survey, the total mag￾nitude dependence of the FRB host galaxy distribution, by convolving the redshift distributions of [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Redshift distribution of FRBs, normalised to a peak of unity, for FRBs detected by ASKAP’s CRACO system, and MeerKAT’s coherent MeerTRAP mode. Also shown are the redshift distributions of FRB host galaxies accessi￾ble with a single LSST visit (dashed) and 10yr co-adds (dotted). 2.4 Fraction of FRB hosts detectable by LSST [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: As per [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Normalized scan of likelihoods of H0 for 100 simulated FRB detec￾tions by ASKAP CRACO, including limitations of LSST photometric z estimates (σz = 0.035); magnitude limit of mlim r = 24.7 on host observations; and both together. sponds to an uncertainty of approximately ±0.1 z given the slope of the Macquart (z–DM) relation (Macquart et al. 2020). Hence, an error of 0.035 z, added in quadrature, would naiv… view at source ↗

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Reference graph

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5 extracted references · 1 canonical work pages

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