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Intrinsic galaxy alignments in the KiDS-1000 bright sample: dependence on colour, luminosity, morphology, and galaxy scale

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Red galaxies with low Sérsic index align about 1.5 sigma more weakly than their luminosity predicts, while colour- and morphology-selected samples otherwise share the same alignment–luminosity power law.

desk verdict A careful, well-executed IA measurement whose solid core is the luminosity-scaling confirmation; the headline morphology claim is an honest but unestablished hint, not the paper's main value. read the letter →

arxiv 2502.09452 v2 pith:UVXPUT55 submitted 2025-02-13 astro-ph.CO astro-ph.GA

Christos Georgiou , Nora Elisa Chisari , Maciej Bilicki , Francesco La Barbera , Nicola R. Napolitano , Nivya Roy , Crescenzo Tortora This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords intrinsicalignmentsweakgravitationallensinggalaxymorphologySérsicindexluminositydependenceKiDSbrightsamplephotometricredshiftsshapes
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 asks whether the well-established scaling of galaxy intrinsic alignments — the statistical tendency of galaxies to align their shapes with the surrounding tidal field, a major contaminant of weak-lensing measurements — with luminosity is universal, or whether a galaxy's internal structure matters. Using the bright (r < 20) sample of the Kilo-Degree Survey with machine-learning photometric redshifts, supplemented by a catalogue of two-dimensional Sérsic-profile fits, the authors split galaxies by intrinsic colour, luminosity, and Sérsic index and fit the non-linear linear alignment (NLA) model to the projected position–shape correlation on scales above 6 Mpc/h. They find a power-law luminosity dependence of the large-scale alignment amplitude $A_{IA}$ that is statistically the same for red galaxies and for high-Sérsic-index ($n_s>2.5$) galaxies, but red galaxies with $n_s<4$ sit about $1.5\sigma$ below the amplitude their luminosity predicts — a hint that the luminosity relation is not universal across morphology. Varying the radial weight of the shape measurement reveals that alignments increase with galaxy scale: outer galaxy regions align more strongly than inner regions on scales below about 1 Mpc/h, while blue and low-$n_s$ galaxies show no significant alignment. The stakes: weak-lensing surveys need accurate intrinsic-alignment priors, and this is the first measurement of how those alignments depend on galaxy structure rather than colour alone.

What carries the argument

The load-bearing observable is the projected galaxy position–shape correlation function $w_{g+}(r_p)$, estimated with $\hat\xi_{g+} = (S_+D - S_+R)/RR$ and modelled on large scales by the non-linear linear alignment (NLA) power spectrum $P_{gI}(k,z) = -b_g A_{IA}(\rho_{\rm crit}\Omega_m \bar C_1/D(z))P_\delta(k,z)$, where the fitted amplitude $A_{IA}$ is the quantity of interest and the linear galaxy bias $b_g = 1.23\pm0.09$ is fixed from the clustering of GAMA III galaxies. Two measurement choices carry the new results. First, galaxy morphology is encoded by the Sérsic index $n_s$ from two-dimensional PSF-convolved profile fits, with $n_s>2.5$ selecting spheroids and the red sample further split at $n_s=4$; this converts the colour–luminosity split of previous work into a structure–luminosity split. Second, DEIMOS moment-based shape measurement applies a Gaussian weight of size $r_{wf}$ tied to each galaxy's isophotal radius $r_{iso}$, so repeating the measurement at $r_{wf}/r_{iso}=0.5$, $1.0$, and $1.5$ re-weights inner versus outer galaxy light and turns the shape pipeline into a scale probe. Photometric-redshift uncertainty enters through a generalized Lorentzian error distribution calibrated on GAMA matches, and jackknife resampling supplies the covariance.

What would settle it

Measure $w_{g+}$ on the same red galaxies at $r_{wf}/r_{iso}=0.5$ and $1.5$ with an independent shape pipeline, for example a forward-modelling fit rather than moment deconvolution, and compare the small-scale difference: if the monotonic rise in alignment with weight-function size vanishes, the galaxy-scale trend is a shape-measurement artefact. In parallel, re-split red galaxies by Sérsic index inside a single luminosity bin with a larger sample; the $n_s<4$ deficit disappearing at fixed luminosity would mean the luminosity power law is universal after all.

Watch

Extended reading notes

Core claim

Fitting the non-linear linear alignment model to $w_{g+}(r_p)$ for $r_p > 6$ Mpc/h with a single power law $A_{IA}(L) = A_0(\langle L\rangle/L_0)^\beta$, the paper finds $A_0 = 5.95\pm0.49$, $\beta = 0.68\pm0.18$ for intrinsically red galaxies and $A_0 = 5.11\pm0.43$, $\beta = 0.79\pm0.16$ for galaxies with $n_s>2.5$, with the two power laws consistent within about $2\sigma$ and consistent with earlier KiDS, SDSS, and DES measurements. Splitting the red sample at $n_s=4$ yields $A_{IA}=5.24\pm0.85$ for the high-Sérsic-index half, matching the luminosity expectation, but $A_{IA}=1.12\pm1.18$ for the low-Sérsic-index half, about $1.5\sigma$ below it; the authors interpret this as red, disk-like (and likely rotationally supported) galaxies aligning more weakly than colour alone would suggest. When the same red galaxies have their shapes remeasured with radial weights $r_{wf}/r_{iso} = 0.5$, $1.0$, and $1.5$, the large-scale amplitudes agree, but below about 1 Mpc/h the alignment signal rises monotonically with weight-function size: outer galaxy regions align more strongly than inner regions, attributed to isophotal twisting in satellite galaxies. Intrinsically blue galaxies give $A_{IA} = -0.67\pm1.00$ and low-$n_s$ galaxies $A_{IA} = 0.64\pm0.88$, both consistent with zero, providing upper bounds on the contamination of these populations to cosmic shear.

Load-bearing premise

The small-scale conclusion that outer galaxy regions align more strongly than inner ones (Sect. 4.3, Fig. 6) assumes that residual shape-measurement effects — imperfect point-spread-function modelling, galaxy blending, stray light, and isophote-orientation errors — do not strengthen as the radial weight function is enlarged; the paper argues these are expected to be small near 1 Mpc/h, but the trend sits below the 6 Mpc/h fitting range and no dedicated systematics budget is presented.

Editorial extensions

If this is right

  • Weak-lensing analyses that adopt a luminosity-only intrinsic-alignment prior will misestimate the contamination from red, disk-like galaxies, because the same luminosity does not buy the same alignment strength for $n_s<4$ and $n_s>4$ galaxies; the paper notes this risk grows across redshift bins where the Sérsic-index distribution evolves.
  • Because the power-law luminosity scaling is consistent between colour-selected red and morphology-selected $n_s>2.5$ galaxies, pressure-supported galaxies can be selected on structure alone, giving an independent route to intrinsic-alignment priors that does not rely on the colour–magnitude diagram.
  • The galaxy-scale dependence measured in Sect. 4.3 implies the effective intrinsic-alignment signal entering cosmic-shear analyses is tied to the shape-measurement pipeline: choices that weight inner versus outer galaxy light change the measured alignment amplitude.
  • Consistent-zero amplitudes for the blue and low-$n_s$ samples ($A_{IA}=-0.67\pm1.00$ and $0.64\pm0.88$) supply upper bounds on the contamination from rotationally supported galaxies, which dominate the source samples of weak-lensing surveys.
  • If the $n_s<4$ deficit is confirmed, the Sérsic-index distribution becomes part of the alignment model: samples with different fractions of red spirals will sit at different $A_{IA}$ for the same mean luminosity.

Reading between the lines

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

  • A decisive check would be to repeat the $n_s<4$ versus $n_s>4$ split inside a single luminosity bin with a larger sample: if the deficit persists at fixed luminosity, morphology genuinely modulates alignment, whereas if it shrinks, the present result is luminosity mixing within the roughly equal-mass red sample.
  • If the small-scale trend is isophotal twisting, then radially binned shape measurements (multiple annuli per galaxy) should reveal a continuous alignment profile that rises outward; such a profile could be compared with simulated galaxies to test tidal-torque models directly.
  • The same radial-weight machinery could be applied to blue galaxies: an outer-versus-inner alignment difference there would complicate the picture that rotationally supported galaxies do not align, since the outer disc regions should be the most tidally responsive.
  • For Stage-IV weak-lensing surveys, a practical consequence is that intrinsic-alignment nuisance parameters calibrated on bright, low-redshift samples may need recalibration on fainter, higher-redshift source samples, whose Sérsic-index and luminosity distributions differ from the one measured here.
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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

2 major / 5 minor

Summary. The paper measures intrinsic galaxy alignments (IA) in the KiDS-1000 bright sample (r<20) using DEIMOS moment-based shapes and a Sérsic-based morphology catalogue. The authors split the sample by intrinsic colour, luminosity, Sérsic index n_s, and radial weight function size, and fit the non-linear linear alignment (NLA) model to the projected position–shape correlation function on scales r_p > 6 Mpc/h. They report: (i) a power-law luminosity dependence of A_IA for both intrinsically red and high-Sérsic (n_s>2.5) samples, with no significant difference between the two selections; (ii) a ~1.5σ deficit in A_IA for red, low-Sérsic (n_s<4) galaxies relative to the amplitude expected from the sample's mean luminosity; (iii) a scale-dependent IA signal when varying the radial weight function, with larger weight functions yielding stronger alignment on small scales; and (iv) A_IA consistent with zero for blue and low-Sérsic galaxies, with A_IA = -0.67 ± 1.00 for blue galaxies.

Significance. If the results hold, this is a useful step toward using galaxy structure as a physical prior for IA modelling in weak lensing, and it provides a new test of the universality of the luminosity scaling. The measurement pipeline is solid: it uses standard estimators (Landy–Szalay and shape–position correlations), jackknife covariances with Hartlap correction, explicit modelling of photo-z errors and lensing contamination, and a cross-component null test. The luminosity-scaling parameters (A0 ≈ 5.95 ± 0.49, β ≈ 0.68 ± 0.18 for red galaxies) are consistent with previous work. The paper is appropriately cautious in several places, but the headline morphology and small-scale claims need stronger quantitative support, as detailed below.

major comments (2)
  1. [Sect. 4.2, Eq. (21) and Fig. 5] The 'expected' A_IA for the red n_s<4 sample is computed by evaluating the power-law A_IA(L) = A0 (⟨L⟩/L0)^β at the sample's mean luminosity. Because the fitted slope is β ≈ 0.68 < 1 and the luminosity distribution of this sub-sample is broad (as the authors themselves note, the sample is 'not very localised in luminosity'), the correct expectation is A0 times the luminosity-weighted average of (L/L0)^β over the sample distribution, not A0 times (⟨L⟩/L0)^β. For a concave power law, the latter is biased high relative to the former, which could contribute to (or even produce) the reported ~1.5σ deficit. In addition, the uncertainty on the expectation from the A0 and β fits is not propagated, and the expectation is not statistically independent of the measured A_IA, since the same galaxies enter both. I recommend recomputing the expectation using the observed luminosity distribution of the red n_s<4 sample, propagating the full covariance of A0 and β, and reporting a formal Δχ² or equivalent with the jackknife covariance.
  2. [Sect. 4.3, Fig. 6 and Abstract] The claim that 'IA increase with galaxy scale' on small scales (r_p ≲ 1 Mpc/h) is presented as a visually clear trend, but no quantitative significance is given for the small-scale differences between weight-function sizes. The paper computes a large-scale ΔA_IA = 0.38 ± 0.71 between rwf/riso = 1.5 and 1.0, but does not compute a similar difference statistic for the small-scale points that drive the headline conclusion. Furthermore, the wg× null test does not exclude blending, stray light, or PSF-model residuals, which could affect the + component in a weight-function-dependent way. Because this is a novel claim featured in the abstract and title, the authors should either provide small-scale difference measurements with jackknife covariances (and ideally a systematic-error budget including realistic image simulations) or soften the claim to a tentative trend pending such tests.
minor comments (5)
  1. [Eq. (2)] The expression for the ellipticity is typeset incorrectly: the factor 'q' in the denominator should be a square root symbol, i.e., the denominator should read Q20 + Q02 + 2 sqrt(Q20 Q02 - Q11^2). Please fix the equation in the published version.
  2. [Abstract and Sect. 1] The statement that this work is 'the first time' that the dependence of intrinsic alignments on galaxy structural parameters is studied is too broad given prior literature on alignment versus concentration and shape-measurement method (e.g., Singh & Mandelbaum 2016; Georgiou et al. 2019a). Please rephrase to specify the novelty, e.g., 'the first measurement of IA dependence on Sérsic index in a wide-field imaging survey'.
  3. [Sect. 4.1 et seq.] The sentence 'the parameters are approximately within 2-σ of each other’s interval (however, they are not independent)' is unclear. Since the red and n_s>2.5 samples overlap, the two power-law fits are correlated; please report the covariance between the fits or explicitly state the degree of overlap and its effect on the comparison.
  4. [Table 3 and fits] No goodness-of-fit values (e.g., chi-square/dof) are reported for the NLA fits. Adding these to Table 3 would help readers judge whether the NLA model is an adequate description on the fitted scales for each sub-sample.
  5. [Sect. 2.5, Table 2] The interval notation for luminosity bins, e.g., '( −∞,−0.55]', is ambiguous for the lower end; consider using standard half-open interval notation, e.g., 'L/L0 ≤ 10^-0.55' or similar, to avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: all fitted quantities are measured from data and benchmarked externally; the Sect. 4.2 expectation is a residual check, not a forced prediction.

full rationale

The paper's derivation chain is measurement-driven: wg+ and wgg are measured from KiDS/GAMA data, the NLA model (Eqs. 7-10) is fitted to obtain AIA per subsample, and the luminosity scaling (Eq. 21) is a fit to those AIA values. The Sect. 4.2 comparison for red, ns<4 galaxies uses an expectation from that power-law fit, which is in-sample because the red luminosity bins include some of the same galaxies; however, the measured AIA of the ns<4 subsample is an independent fit to that subsample's wg+ and is not algebraically forced by the power-law parameters, so this is a statistical non-independence caveat rather than a definitional circularity. Self-citations (DEIMOS shape pipeline, Georgiou et al. 2019b; small-scale systematics, Georgiou et al. 2019a) refer to externally published, falsifiable prior work and are not used to forbid alternatives or import uniqueness. The luminosity-scaling results are explicitly benchmarked against independent literature (Joachimi 2011, Singh 2015, Fortuna 2021b, Samuroff 2023). No equation reduces the target claim to its own input.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central results rest on the NLA model, the transfer of GAMA III galaxy bias to KiDS bright, photo-z error modelling, jackknife covariance, and the Sersic-index morphological classification. The fitted amplitudes A_IA, A0, beta, b_g and photo-z shape parameters are all empirical inputs, and no new physical entities are invented.

free parameters (5)
  • A_IA (per sub-sample) = e.g., 8.07±1.04 (red L5); 1.12±1.18 (red, ns<4); -0.67±1.00 (blue); 0.64±0.88 (ns<2.5)
    Intrinsic alignment amplitude fitted to projected position-shape correlation wg+ through Eq. (10) for each galaxy sub-sample.
  • A0 (luminosity scaling normalization) = 5.95±0.49 (red); 5.11±0.43 (ns>2.5)
    Normalization fitted in the relation AIA(L)=A0 (L/L0)^beta, Eq. (21).
  • beta (luminosity scaling slope) = 0.68±0.18 (red); 0.79±0.16 (ns>2.5)
    Slope fitted in Eq. (21); used for the expected-amplitude comparison in Sect. 4.2.
  • linear galaxy bias b_g = 1.23±0.09
    Nuisance parameter fitted to GAMA III projected clustering, Eq. (14), and assumed to apply to the KiDS bright density sample; enters the IA model through PgI in Eq. (10).
  • photo-z error distribution parameters (alpha, s) = fit per sub-sample, not tabulated
    Parameters of the generalized Lorentzian in Eq. (11), fitted to GAMA spectroscopic matches to describe p(z|z_phot); used in modelling wg+.
assumptions (6)
  • domain assumption The non-linear linear alignment (NLA) model with linear galaxy bias describes the position-shape correlation for rp>6 Mpc/h (Eq. 10, with non-linear P_delta).
    This is the physical model from which A_IA is extracted; if the NLA form is wrong on these scales, the fitted amplitudes are biased.
  • domain assumption The linear galaxy bias of the KiDS bright density sample equals that measured from GAMA III spectroscopic clustering.
    Stated in Sect. 2.4 and used in Eq. (10); the same b_g=1.23±0.09 scales all A_IA values.
  • domain assumption The generalized Lorentzian photo-z error distribution fitted to GAMA matches represents the true redshift PDFs of all sub-samples.
    Introduced in Eq. (11) and Sect. 3; inaccurate redshift PDFs would redistribute wg+ and bias A_IA.
  • domain assumption Weak lensing contamination is modelled and lensing magnification is negligible.
    Sect. 3 and Fig. 3; magnification is neglected based on Samuroff et al. 2023, so a significant magnification contribution would contaminate wg+.
  • domain assumption Jackknife covariance with 40 patches and no super-sample covariance is adequate for the measured scales.
    Sect. 3.2; inaccurate covariance affects all quoted significances, including the 1.5 sigma morphology deviation.
  • domain assumption The Sersic index from 2DPHOT, with the ns=2.5 cut, separates pressure-supported from rotationally supported galaxies.
    Sect. 2.3; the interpretation of the ns>2.5 sample as elliptical depends on this morphological classification.

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Cite this review

Pith. "Pith review of Intrinsic galaxy alignments in the KiDS-1000 bright sample: dependence on colour, luminosity, morphology, and galaxy scale." pith.science (2026). https://pith.science/paper/UVXPUT55

@misc{pith2026250209452,
  author       = {Pith},
  title        = {Pith review of: Intrinsic galaxy alignments in the KiDS-1000 bright sample: dependence on colour, luminosity, morphology, and galaxy scale},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UVXPUT55}},
  note         = {Machine review of arXiv:2502.09452}
}
abstract

The intrinsic alignment (IA) of galaxies is a major astrophysical contaminant to weak gravitational lensing measurements, and the study of its dependence on galaxy properties helps provide meaningful physical priors that aid cosmological analyses. This work studied for the first time the dependence of IA on galaxy structural parameters. We measured the IA of bright galaxies, selected on apparent r-band magnitude r<20, in the Kilo-Degree Survey (KiDS). Machine-learning-based photometric redshift estimates are available for this galaxy sample that helped us obtain a clean measurement of its IA signal. We supplemented this sample with a catalogue of structural parameters from Sersic profile fits to the surface-brightness profiles of the galaxies. We split the sample on galaxy intrinsic colour, luminosity, and Sersic index, and we fitted the non-linear linear alignment model to galaxy position-shape projected correlation function measurements on large scales. We observe a power-law luminosity dependence of the large-scale IA amplitude, $A_{IA}$, for both the red and high-Sersic-index ($n_s>2.5$) samples, and find no significant difference between the two. We measure an $\sim1.5\sigma$ lower $A_{IA}$ for red galaxies that also have a Sersic index of $n_s<4$ compared to the expected amplitude predicted using the sample's luminosity. We also probe the IA of red galaxies as a function of galaxy scale by varying the radial weight employed in the shape measurement. On large scales (above 6 Mpc/$h$), we do not detect a significant difference in the alignment. On smaller scales, we observe that IA increase with galaxy scale, with outer galaxy regions showing stronger alignments than inner regions. Finally, for intrinsically blue galaxies, we find $A_{IA}=-0.67\pm1.00$, which is consistent with previous works, and we find IA to be consistent with zero for the low-Sersic-index ($n_s<2.5$) sample.

Figures

Figures reproduced from arXiv: 2502.09452 by the authors.

Figure 1
Figure 1. Two-dimensional histograms of properties of galaxies in final sample of KiDS bright catalogue. Left: Distribution of rest-frame g−r colour (y-axis) against absolute magnitude in the r band (x-axis). The dashed red line shows our criteria for splitting the sample in intrinsically red and blue galaxies. Right: Same as before, but with the Sérsic index, ns , in the x-axis (see Sect. 2.3 for details). The solid red line… view at source ↗
Figure 2
Figure 2. Redshift (top) and observed colour distribution (bottom) of the GAMA III (orange) and KiDS bright (red) samples. Limiting the KiDS bright sample to r ≤ 19.77 yields a clustering sample (green) that better matches the properties of the GAMA III sample. which is shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Contamination of weak gravitational lensing measurements in the intrinsic alignment measurements, expressed as a ratio of the lensing signal over the total signal. signal, but its effect is expected to be very small by comparison (Samuroff et al. 2023), especially over the relatively low redshift baseline of our galaxy sample. For this reason, we neglect its impact here. The last ingredient in our modelling is the l… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Top: Projected position–shape correlation function measurements as a function of galaxy pair separation for the red (red circles) and high ns (orange triangles) galaxy sub-samples. The different luminosity sub-samples are indicated in the top left of each panel, with l…
Figure 5
Figure 5. Figure 5: Best-fit intrinsic alignment amplitude of NLA fits to wg+ mea￾surements from the different galaxy sub-samples, plotted against their average luminosity logarithm. The red or ns > 2.5 galaxy samples (red circles and orange triangles, respectively) are split into five lu…
Figure 6
Figure 6. Figure 6: Similar to [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Similar to [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. Where Galaxies Point: First Measurement of the Large-Scale Axial Intrinsic Alignment

    astro-ph.CO 2025-11 reject novelty 7.0 of 10

    DES galaxy position angles show a coherent preferred axis (RA≈300°, Dec≈50°) — elliptical major and spiral minor axes aligned along it — reported as the first detection of horizon-scale axial intrinsic alignment.

  2. Intrinsic alignment demographics for next-generation lensing: Revealing galaxy property trends with DESI Y1 direct measurements

    astro-ph.CO 2025-07 conditional novelty 7.0 of 10

    With over two million DESI galaxies, the bluest galaxies show zero intrinsic alignment across z=0.05 to 1.55, and red galaxy alignment is set by luminosity and colour alone, with no extra redshift dependence.

  3. Intrinsic alignment of disks and ellipticals across hydrodynamical simulations

    astro-ph.GA 2025-10 conditional novelty 6.0 of 10

    With consistent shape and morphology definitions, disk-galaxy intrinsic alignments are positive in TNG300 and EAGLE, positive or null in Horizon-AGN, and negative only for reduced shapes at z=1 with |v/σ| disk selecti...

  4. Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising

    astro-ph.IM 2025-06 reject novelty 5.0 of 10

    Applying a U-Net VAE denoising step to galaxy images before classification is reported to improve accuracy, reaching 97.45% with a GCNN on Galaxy10 DECaLS, although no direct noisy baseline is presented.

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