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REVIEW 3 major objections 5 minor 112 references

Correcting for the effects of the point spread function in intra-halo light measurements and application to deep Hyper Suprime-Cam data

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Removing telescope-scattered light first shrinks a 100-fold error in faint galaxy light to under 10%.

desk verdict Useful PSF-correction pipeline for IHL work, but the mock validation omits PSF convolution of the injected IHL, so the real-galaxy fractions are less certain than the abstract implies. read the letter →

arxiv 2505.24395 v1 pith:3T7GO4S6 submitted 2025-05-30 astro-ph.GA

L. P. Garate-Nuñez , A. S. G. Robotham , S. Bellstedt , L. J. M. Davies This is my paper
classification astro-ph.GA
keywords galaxies:evolutionhaloesclusters:intraclustermediuminstrumentation:detectorsmethods:dataanalysisintra-halolight
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 measurements of intra-halo light (IHL) — the diffuse stellar component around galaxy groups and clusters — are severely biased unless telescope-scattered light from the point spread function (PSF) is subtracted before any modelling is done. It presents two techniques that together aim to make IHL measurements robust: a PSF-scattered flux removal step, and an MCMC fit of a circular exponential profile to the remaining diffuse light. The claim is supported with 5440 mock Hyper Suprime-Cam observations of GAMA groups with injected IHL fractions from 1% to 50%. Without the PSF correction, the fitted IHL flux is overestimated by up to a factor of 100 and the effective radius by a factor of 10 at the faintest injected fraction; after correction the fitted flux is 9.6% low and the effective radius 12.2% low. Applied to the real GAMA group G400138, the method yields median IHL fractions of about 19% in g, 8% in r, and 6% in i, a redward decline consistent with earlier work.

What carries the argument

The machinery is a two-stage image-processing pipeline. Stage one estimates the intrinsic flux of every detected source by comparing its observed flux with the flux remaining inside its segmentation map after a PSF convolution, rescales each source by the ratio of those fluxes, then convolves the rescaled image with the PSF model and subtracts it from the original image outside the source segments — this yields an image whose diffuse background is nominally free of scattered light. Stage two masks all detected sources and fits the remaining light with ProFit's circular exponential Sérsic template (n = 1, axial ratio 1, position angle 0), leaving only magnitude and effective radius as free parameters, optimised with the Highlander genetic/MCMC algorithm. The same template is used to inject mock IHL components, whose effective radii are tied to halo mass via the r_IHL–M200 relation from Proctor et al. 2024. The comparison between injected and fitted magnitude and effective radius across 5440 mocks, with and without stage one, is what carries the argument.

What would settle it

Take a set of galaxy-group mocks like those used here but inject an elliptical or substructured IHL (e.g., axial ratio 0.5 with a tidal stream), run the full PSF-correction-plus-exponential-fit pipeline, and check whether the recovered flux and radius deviate from the injected values by more than the paper's quoted ~10%/12% biases at f_IHL = 0.01; any substantially larger deviation would falsify the claim that the method gives unbiased IHL measurements for realistic morphologies.

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Extended reading notes

Core claim

The paper's central claim is that removing the PSF-scattered flux before fitting an exponential model is not optional for IHL measurements — it is the step that turns order-of-magnitude errors into near-percent-level ones. In the authors' mock suite, without PSF correction the fitted IHL flux at an injected fraction f_IHL = 0.01 is on average ~100 times larger than the injected value and the effective radius ~10 times larger; after applying their PSF-correction pipeline, the same mock fits come in 9.6% low in flux and 12.2% low in radius at f_IHL = 0.01, with the bias shrinking to 3.5% and 0.28% at f_IHL = 0.5. The authors interpret the remaining small deficits as a known oversubtraction caused by the correction itself. For the real group G400138, using the same PSF-corrected pipeline, they report median intra-halo light fractions of f_g,IHL ~ 0.19, f_r,IHL ~ 0.08, and f_i,IHL ~ 0.06, which lie in the range of previous measurements and reproduce the trend of lower IHL fractions at redder wavelengths.

Load-bearing premise

The mock validation assumes real intra-halo light is a smooth circular exponential disc whose size is set by the simulation-based r_IHL–M200 relation, and that the same functional form is the one used for fitting; if real IHL has different shapes or substructure, the quoted recovery biases and the G400138 fractions would not transfer directly.

Editorial extensions

If this is right

  • IHL fractions measured from survey images without PSF subtraction are systematically overestimated, with the largest errors at the faintest IHL fractions, so previously published IHL fractions based on PSF-uncorrected images may need revision.
  • With the correction in place, automated exponential fits recover the injected IHL flux and size to within about 10% at f_IHL = 0.01 and to within a few percent at higher fractions, making the pipeline suitable for large survey samples and stacked IHL analysis.
  • For the real group G400138, the reported fractions (fg ~ 0.19, fr ~ 0.08, fi ~ 0.06) imply that roughly one-fifth of the g-band light but only about 6% of the i-band light is diffuse intra-halo light, a colour trend consistent with the idea that IHL is built from tidally stripped stellar populations.
  • The PSF-removal technique is not specific to IHL; it can be applied to any low-surface-brightness measurement in astronomical images, and the mock suite doubles as a calibration of the residual biases.

Reading between the lines

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

  • Inference: because the mocks inject exactly the functional form the fitter assumes, the quoted 9.6% and 12.2% biases are best-case; real IHL with ellipticity or tidal substructure will recover less accurately, as the paper's own elliptical-mock test shows roughly half the flux missed at f_IHL = 0.5.
  • Inference: the pipeline's remaining bias is a systematic flux deficit, so a simple empirical correction factor derived from the mock suite could produce unbiased f_IHL values in real applications, at the cost of added scatter.
  • Inference: the r_IHL–M200 relation used to set mock sizes is simulation-based; the same pipeline applied to a large real sample with independent mass estimates could test that relation observationally.
  • Inference: applied to upcoming deep surveys, the method could measure IHL fractions for thousands of groups, but the single-exponential assumption should be diagnosed with residual maps to avoid mistaking unmodelled substructure for background noise.
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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

3 major / 5 minor

Summary. The paper presents two techniques for measuring intra-halo light in galaxy groups and clusters: a ProFound-based routine that rescales source fluxes and subtracts PSF-scattered flux outside source segments (Sec. 2), and a ProFit-based MCMC fit of a circular exponential (Sérsic n=1) IHL model to the masked, PSF-corrected image (Sec. 3). The methods are tested on 5440 HSC-SSP PDR3 mock images of GAMA groups with injected circular exponential IHL components at fractions f_IHL = 0.01-0.5. The mocks show that without PSF correction the fitted IHL flux can be ~100 times too high and the effective radius ~10 times too large at f_IHL=0.01, while after correction the bias is reduced to ~9.6% in flux and ~12.2% in Reff at f_IHL=0.01 (Sec. 5.2). The pipeline is then applied to the real GAMA group G400138 in HSC-UD g,r,i, yielding median PSF-corrected fractions f_g,IHL~0.19, f_r,IHL~0.08, f_i,IHL~0.06 (Sec. 6).

Significance. If the central claim holds, the proposed combination of PSF-scattered-light removal and single-component exponential fitting offers a computationally efficient, automatable route to IHL measurements in large deep surveys, and the 5440-mock controlled test is a useful resource. The paper is transparent about several caveats, including the up to 4% oversubtraction of intrinsic source flux in Sec. 2.3, unreliable recovery at f_IHL=0.01-0.05 in Sec. 5.2, and the limitations of a circular exponential model in Sec. 5.3. The use of publicly available software (ProFound, ProFit) and a detailed mock-generation recipe supports reproducibility. However, the external validity of the recovery biases and of the real G400138 measurement is limited by two modelling gaps identified in the major comments.

major comments (3)
  1. [Sec. 4 / Sec. 3 Step v] The mock validation does not include the PSF convolution of the target IHL. In Sec. 4, the construction sequence is: convolve the HSC cutouts (with galaxy segments and injected point sources) with the extended PSF model, add Gaussian noise, and only then 'inject the corresponding IHL component into each mock group'. In Sec. 3 Step v, ProFit fits an unconvolved circular Sérsic n=1 template and is not given a PSF image. The mocks therefore test recovery of a sharp, unconvolved IHL that is contaminated by PSF-scattered source flux, not the PSF-convolved IHL that is present in real HSC data. The quoted recovery biases in Sec. 5.2 (9.6% low flux and 12.2% low Reff at f_IHL=0.01) and the G400138 fractions in Sec. 6 are thus not directly transferable to real data, because the target component is misspecified there. I recommend adding a mock variant in which the IHL is injected before the PSF convolution (and/or fitting with a PSF-convolved ProFit model) and rerunning the recovery and G400138 analyses.
  2. [Sec. 3 / Sec. 5.3 / Appendix B] The recovery test is internally consistent but does not validate the exponential model: the injected IHL is a circular exponential (Sec. 4, 'we select a circular exponential model') and the fitting template is the same functional form (Sec. 3 Step v). The one morphological robustness test in Sec. 5.3 varies only ellipticity (Arat=0.5, theta=45 deg) while keeping an exponential radial profile. Appendix B claims 'supporting evidence' for an exponential model by fitting an exponential to the SB-limit IHL of G400138 and extrapolating that same fit into the core; this is not an independent test. The real-data f_IHL values in Sec. 6 should therefore be presented as conditional on the exponential-profile assumption, or the authors should add tests with Sérsic n != 1, substructure, or a realistic simulated IHL morphology to quantify the resulting bias.
  3. [Sec. 6 / Table 2] The uncertainties quoted for the real G400138 fractions are fitting uncertainties only; the three methods (ProFit fixed, ProFit free, SB-extrapolated) share the same PSF-correction pipeline and the same exponential-model assumption, so their spread does not encompass the dominant systematics discussed in Secs. 2.3 and 5.3. The reported medians f_g,IHL~0.19, f_r,IHL~0.08, f_i,IHL~0.06 and their asymmetric ranges would be more robust with an explicit systematic error term from the PSF oversubtraction (up to 4% source flux) and from the model-choice sensitivity.
minor comments (5)
  1. [Sec. 2.3] There is a typo: 'profile profile' should be 'profile'.
  2. [Sec. 4] The chronological order of noise addition and IHL injection is described ambiguously: 'Finally, we add image noise...' appears before the paragraph describing the IHL injection. Please clarify whether the noise is added before or after the IHL injection, since this matters for interpreting the mock construction and the PSF-convolution issue raised above.
  3. [Sec. 5.2] The conversions from log-ratios to percentages (9.6% low flux and 12.2% low Reff at f_IHL=0.01) are not exactly consistent with the displayed log values; please verify the arithmetic or the quoted log values.
  4. [Sec. 5.2 / Fig. 10] There are typos: 'uncertanties' should be 'uncertainties' and 'accross' should be 'across'.
  5. [Software / References] In the software section, 'Robotham 023a' should be 'Robotham 2023a'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the mock recovery is a forward-model self-consistency test, the exponential-profile choice is a stated ansatz supported by external simulations, and no derivation reduces to its own input by construction.

full rationale

The central validation in Sec. 5.2 is a standard injected-recovery experiment: mocks are built with a known circular exponential IHL (Sec. 4) and the same functional form is fitted in Sec. 3 Steps v–vi. This does not make the recovery circular, because the fitted parameters are not fixed to the injected values; they are optimized by Highlander and compared with the truth only to quantify PSF-contamination bias. The equality of template shape is an explicit assumption, acknowledged in Sec. 3 ('the main disadvantage of our technique is the lack of flexibility to model non-circular and non-exponential IHL distributions'), and the paper tests an elliptical alternative in Sec. 5.3. Equations (1) and (2) are inverse algebraic definitions of fIHL; using the same definition for injection and estimation is a convention, not a derivation. Appendix B justifies the exponential choice by an empirical fit to the G400138 SB profile; this is weak evidence but not a reduction to the fitted model, because the fit is to measured unmasked SB points, not to the desired fIHL value. The PSF model taken from Garate-Nuñez et al. 2024 is a self-citation, but it is an external calibration product used as an input; the mock test that uses it is an internal-consistency check of the correction algorithm, not a proof of the PSF model, and the cited prior work does not contain the target IHL result. The omission of PSF convolution of the injected IHL component (Sec. 4) is a modeling limitation and a correctness risk for the real-data numbers, but it is not circularity: the paper's claim that source-scattered flux biases IHL fits is tested by adding source PSF wings before injection. Overall, no load-bearing step reduces by construction to its own input.

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

No new particles, forces, dimensions, or conserved quantities are introduced. The injected IHL components are simulation inputs, not invented entities. The main load-bearing inputs are the assumed exponential IHL model, the extended PSF model from the authors' prior work, and the external r_IHL(M200) relation.

free parameters (5)
  • Injected IHL effective radius (Reff) = derived from r_IHL(M200) relation of Proctor et al. 2024 for each group
    Sets the size of every injected IHL component in the mocks; if this relation is wrong for real groups, the recovery statistics in Sec. 5.2 do not transfer to observations.
  • Injected IHL fraction grid = 0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5
    The ten discrete f_IHL values used to build 5440 mocks. The recovery accuracy is binned by these values, so conclusions are only demonstrated at these fractions.
  • Surface brightness threshold for SB method = mu_V = 26.5 mag arcsec^-2
    Adopted from Martinez-Lombilla et al. 2023, used to define IHL in the real-group measurement; changes the resulting f_IHL.
  • Integration radius for SB method = R = 275 kpc
    Matches Martinez-Lombilla et al. semi-major axis; the f_IHL,SB value depends on this radial cut.
  • Extrapolated group light factor = Fgroup,extrap = 1.48 * Fgroup
    From G3Cv10 luminosity function correction; directly lowers all final f_IHL values when included.
assumptions (5)
  • domain assumption The IHL component of groups and clusters can be described by a circular or elliptical exponential (Sérsic n=1) profile.
    Used both to inject IHL in mocks (Sec. 4) and to fit it (Sec. 3, Step v); justified by simulations and by an exponential fit to G400138 in Appendix B, but not by an independent decomposition of real data.
  • domain assumption The extended HSC-SSP PDR3 PSF model from Garate-Nuñez et al. 2024 accurately represents scattered light in these images.
    The PSF correction convolves and subtracts using this model; errors in the PSF wings directly bias the corrected IHL. The model is from the authors' own prior work and is not revalidated here.
  • domain assumption ProFound segmentation maps enclose essentially all intrinsic light of each source, and WAVES segments used to build mocks are faithful proxies.
    The correction factor C in Sec. 2.1 assumes flux outside segments is scattered light; Sec. 2.3 acknowledges this is approximate and can overestimate intrinsic flux by up to 4%.
  • domain assumption The r_IHL(M200) relation of Proctor et al. 2024, used to set injected effective radii, applies to the GAMA groups at z~0.2.
    Introduced in Sec. 4 to set Reff; if the relation is biased, the mock IHL sizes are unrepresentative.
  • domain assumption Background noise in mocks is Gaussian with the HSC Wide median sky RMS.
    Used in Sec. 4 to add noise; real HSC images have correlated noise and residual sky structure.

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Cite this review

Pith. "Pith review of Correcting for the effects of the point spread function in intra-halo light measurements and application to deep Hyper Suprime-Cam data." pith.science (2026). https://pith.science/paper/3T7GO4S6

@misc{pith2026250524395,
  author       = {Pith},
  title        = {Pith review of: Correcting for the effects of the point spread function in intra-halo light measurements and application to deep Hyper Suprime-Cam data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3T7GO4S6}},
  note         = {Machine review of arXiv:2505.24395}
}
abstract

The intra-halo light (IHL) is the diffuse stellar component that surrounds galaxies, groups, and clusters. Its formation is intimately linked to the hierarchical assembly of the system, making it a key tracer of galaxy evolution. However, the low surface brightness (LSB) of the IHL makes it challenging to detect and also to distinguish from the point spread function (PSF) effect of the telescope. In this paper, we present two independent techniques that, when combined, provide a statistically robust estimation of the IHL component in galaxy groups and clusters. The first technique corrects for the PSF-scattering effect to obtain unbiased LSB measurements, while the second fits an exponential model to the IHL component using a Markov Chain Monte Carlo (MCMC) optimiser algorithm. To test our methodology, we build a set of 5440 Hyper Suprime-Cam Subaru Strategic Program Public Data Release 3 (HSC-SSP PDR3) mock observations of Galaxy And Mass Assembly (GAMA) groups, each containing an IHL component with a flux fraction ($\mathrm{f_{IHL}}$) ranging from 0.01 to 0.5. Our results demonstrate the importance of properly removing the PSF-scattered flux, especially at lower $\mathrm{f_{IHL}}$. Without the PSF correction, our IHL model overestimates the true flux by up to a factor of 100, and the effective radius by up to a factor of 10. Finally, we apply our methodology to real observations and estimate the $\mathrm{f_{IHL}}$ of the GAMA group G400138 using HSC-PDR3 UD data in the $\textit{g,r}$ and $\textit{i}$-bands, finding median IHL fractions of: $\mathrm{f_{g,IHL}}$ $\sim$ 0.19$^{+0.09}_{-0.01}$, $\mathrm{f_{r,IHL}}$ $\sim$ 0.08$^{+0.06}_{-0.02}$ and $\mathrm{f_{i,IHL}}$ $\sim$ 0.06$^{+0.04}_{-0.02}$.

Figures

Figures reproduced from arXiv: 2505.24395 by the authors.

Figure 1
Figure 1. Schematic view of the processing steps outlined in Sec. 2 to remove the PSF scattered light from an astronomical image. The first part of the technique covers Steps i-v, which are explained in Sec. 2.1. The second part covers Steps vi-viii, explained in Sec. 2.2. Throughout both parts of the technique, we make reference to the orange numbered labels to clarify intermediate and final outputs of each step. Solid dark … view at source ↗
Figure 2
Figure 2. Comparison between the true factor to correct for the PSF effect and the one estimated using our PSF-scattered flux removal technique. The upper panel shows the true PSF correction factor as a function of the magnitude of the detected segments in an HSC-PDR3 mock image. The median and 1𝜎 range are shown in solid and dashed pink respectively. The medium left panel shows the ratio between the true and estimated correc… view at source ↗
Figure 3
Figure 3. Schematic view of the processing steps outlined in Sec. 3 to model the IHL component of a system with a ProFit circular exponential 2D template. The steps shown correspond to those outlined in that section. Solid dark gray lines indicate transitions between steps, whereas dashed light gray lines indicate repeated panels. MNRAS 000, 1–21 (2024) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Celestial map highlighting the surveys that were involved in the process to generate our datset of HSC-SSP PDR3 mock observations: GAMA, WAVES, and HSC-SSP PDR3. ID G200195, with a manually injected exponential circular IHL com￾ponent of fIHL = 0.5. G200195 is a group …
Figure 5
Figure 5. Figure 5: Schematic view of the processing steps outlined in Sec. 4 to construct our set of HSC-SSP PDR3 mock observations. PSF flux by slightly removing IHL flux as well, leading to fainter fitted fluxes at all fIHL. We prefer this approach to ensure that the detected light is …
Figure 6
Figure 6. Figure 6: Comparison between the intrinsic (non-PSF-convolved), PSF￾convolved and PSF-corrected versions of the sample of 544 HSC-SSP Wide PDR3 GAMA groups with fIHL = 0.1. All sources in the different versions of the mock images have been masked to emphasise the impact of the P…
Figure 7
Figure 7. Figure 7: Example of galaxy group mock fitting. Top panels show the mock image data (left), the ProFit IHL model (middle) and the result of subtracting the model from the data (right). Bottom panels show the histogram of residuals in units of 𝜎 (left), the 𝜒 2 residuals compared…
Figure 8
Figure 8. Figure 8: Triangle plot of the stationary MCMC chains for the model fit of G200195 mock. Raw and contoured sample distributions are shown in the 1st and 4th panels respectively, while the other diagonal panels present the marginalised one-dimensional density plots for each fitte…
Figure 9
Figure 9. Figure 9: Comparison between the injected vs fitted parameters for both original and PSF-corrected datasets. Upper half of the plot corresponds to the flux, lower half to the effective radius. Upper panel of each half corresponds to the original dataset and lower panel to the PS…
Figure 10
Figure 10. Figure 10: Comparison of the ratio between fitted and injected parameters weighted by the 1𝜎-quantile estimated from each parameter’s posterior, for both original and PSF-corrected datasets. Upper half of the plot corresponds to the flux, lower half to the effective radius. Uppe…
Figure 11
Figure 11. Figure 11: PSF-corrected normalised distributions of the ratio between injected and fitted values, the 1𝜎-quantile posterior, and the weighted ratio between injected and fitted values binned by fIHL for Flux and Reff, where higher fIHL are indicated with darker blues. The median…
Figure 12
Figure 12. Figure 12: Comparison between fitting the inclined, elongated IHL of G200043 mock (left panel) with a circular model versus an elliptical model. Right panels show the ratios between the injected and fitted values for the flux (first), effective radius (second), position angle (t…
Figure 13
Figure 13. Figure 13: Estimations of the IHL fraction of the HSC UD GAMA group G400138. Left panel shows a 1 × 1 Mpc2 cutout of G400138 in the g-band (galaxy members are highlighted with pink circles). Right panel shows the fIHL estimated in this work (green) and in Martínez-Lombilla et al…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.