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KiDS-1000: Improved constraints on cosmology, intrinsic alignments and baryonic feedback from clipped cosmic shear

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Pith's one-line read Combining clipped and unclipped shear correlations extracts extra cosmological information from KiDS-1000 data, tightening S8 by 16% and w0 by 24%.

desk verdict A careful, genuinely new application of clipping to KiDS-1000 with credible precision gains, but the IA bias modelling is the soft spot to probe in review. read the letter →

arxiv 2608.07377 v1 pith:GAKZGJC2 submitted 2026-08-07 astro-ph.CO

classification astro-ph.CO
keywords clippedcosmicshearKiDS-1000weaklensingintrinsicalignmentsbaryonicfeedbackGaussianprocessemulatorswCDMS8tension
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 argues that a simple preprocessing step — clipping, or cutting out the densest patches of the projected matter field before measuring two-point shear correlations — recovers cosmological information that ordinary two-point statistics throw away, because the cosmic lensing field is strongly non-Gaussian. Applied to the fourth data release of the Kilo-Degree Survey (KiDS-1000) with all major systematics forward-modelled by simulation-based emulators, the clipped and unclipped statistics jointly tighten the dark-energy equation-of-state parameter $w_0$ by 24% and the clustering amplitude $S_8$ by 16% relative to the conventional analysis alone, yielding $\Omega_{\rm m} = 0.263^{+0.035}_{-0.038}$, $S_8 = 0.724 \pm 0.027$, and $w_0 = -1.24^{+0.26}_{-0.28}$. The constraints stay consistent with $\Lambda$CDM and with the earlier KiDS-1000 analysis of Asgari et al. (2021), which used a completely independent modelling pipeline. A sympathetic reader should care because the gain comes from larger, linear scales that are least contaminated by baryonic feedback: if the claim holds, existing lensing surveys already contain extra cosmological and astrophysical information that clipping can extract at no observational cost.

What carries the argument

The load-bearing object is the clipped shear correlation function $\xi^c_\pm$. Convergence maps are reconstructed from smoothed galaxy ellipticity maps via Kaiser-Squires inversion (Gaussian smoothing $\sigma_s = 6.6$ arcmin); every pixel with $\kappa \geq \kappa_c = 0.010$ is set to the threshold value $\kappa_c$, clipping roughly 20% of the observed area; the residual map $\Delta\kappa$ is inverted back to residual ellipticities and subtracted from the observed galaxy ellipticities, so only galaxies sitting on convergence peaks are modified. The correlation function of these clipped ellipticities, measured in nine angular bins across five tomographic redshift bins, forms the $\xi^c_\pm$ data vector. Its cosmological and systematic dependence is predicted by Gaussian-process emulators trained on the 26-cosmology cosmoSLICS suite (sampling $\Omega_{\rm m}$, $S_8$, $h$, $w_0$), with the covariance estimated from 1,240 independent SLICS realisations and the intrinsic-alignment, photo-$z$, and baryonic-feedback biases modelled as differences between dedicated contaminated and uncontaminated mock sets. The emulator accuracy is validated by leave-one-out cross-validation and folded into the covariance as an error term.

What would settle it

Build intrinsic-alignment mocks that carry the same 18-tile masked KiDS-1000 footprint as the cosmology set and recompute the intrinsic-alignment bias in the clipped correlation functions; if the masked and unmasked biases differ by more than the emulator-plus-statistical error budget, the separability assumption fails and the reported $A_{\rm IA}$, which already sits $3.2\sigma$ from the Asgari et al. (2021) result, would flag a biased model. A complementary check is to run the identical combined pipeline on KiDS-Legacy or DES Year 6 data and ask whether the same $S_8$ and $A_{\rm IA}$ are recovered.

Watch

Extended reading notes

Core claim

The central claim is that two-point shear correlation functions measured on a clipped convergence field carry cosmological information that is sufficiently independent of the unclipped measurement to sharpen parameter inference when the two are combined. The paper reports the first systematics-controlled application of clipping to real lensing data, with tomographic binning, masked footprint infusion, per-object shear calibration, and emulators trained on the cosmoSLICS, SLICS, intrinsic-alignment, photo-$z$, and magneticum simulation suites. On KiDS-1000 data the combined probe gives $\Omega_{\rm m} = 0.263^{+0.035}_{-0.038}$, $S_8 = 0.724 \pm 0.027$, $w_0 = -1.24^{+0.26}_{-0.28}$, tightening $S_8$ by 16% and $w_0$ by 24% over the unclipped analysis, improving the figure of merit in the $\Omega_{\rm m}$–$S_8$ plane by a factor of 1.2, and reproducing the Asgari et al. (2021) cosmology through an independent forward-modelling pipeline. It further finds that the clipped statistic breaks degeneracies the unclipped probe cannot: the intrinsic-alignment amplitude is constrained 27% more tightly ($A_{\rm IA} = -0.50^{+0.19}_{-0.20}$), and a first upper limit on baryonic feedback, $b_{\rm bary} < 0.97$, is placed from lensing alone. The paper attributes these gains to clipping's decoupling of scale-dependent information: removing high-density peaks suppresses the small scales where baryonic feedback dominates while preserving the larger, cleaner scales.

Load-bearing premise

The analysis assumes that the contamination from galaxies' intrinsic shape alignments and the cosmological lensing signal are separable in the clipped statistics, and that the survey's masked sky pattern does not change how that contamination enters the measurement — an assumption made because the intrinsic-alignment simulations are run on full, unmasked lightcones.

Editorial extensions

If this is right

  • The same KiDS-1000 data, analysed with the combined clipped and unclipped probes, yields $S_8$ and $w_0$ constraints 16% and 24% tighter than the unclipped analysis alone, with the $\Omega_{\rm m}$–$S_8$ figure of merit improved by a factor of 1.2.
  • The emulator-based unclipped analysis independently reproduces the Asgari et al. (2021) cosmology — $\Omega_{\rm m}$ within 0.8$\sigma$ and $S_8$ within 1.8$\sigma$ — validating the standard hmcode-based KiDS-1000 pipeline from a completely different modelling direction.
  • Clipping breaks the degeneracy between intrinsic alignments and cosmic shear: $A_{\rm IA}$ is measured at 39% precision (a 27% improvement over the unclipped probe), and a first upper limit $b_{\rm bary} < 0.97$ is placed on baryonic feedback from lensing alone, where the unclipped probe leaves it unconstrained.
  • Because the precision gains come from larger, less baryon-contaminated scales rather than from the small scales targeted by other higher-order statistics, the method ports directly to future data sets (KiDS-Legacy, Euclid, LSST) once tailored simulations exist.

Reading between the lines

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

  • Editorial inference: the mechanism behind the gain — clipping decouples scale-separated information — suggests the same transform could sharpen other probes, such as clipped galaxy clustering or a clipped 3x2-point analysis, and the cross-statistic between clipped and unclipped fields (excluded here to keep the covariance invertible) is a natural addition once more realisations are available.
  • Editorial inference: the 3.2$\sigma$ offset in $A_{\rm IA}$ relative to Asgari et al. (2021) could be a first hint that clipping is sensitive to scale-dependent intrinsic-alignment physics (luminosity- or redshift-dependent alignment, or tidal torquing) that the simple NLA model does not capture; a testable extension would be to fit a TATT-like model to the combined data vector.
  • Editorial inference: the baryon-feedback limit is set more by emulator noise on small scales than by the information content of the data — the paper shows the $b_{\rm bary}$ constraint tightens sharply when emulator error is handled by propagating the Gaussian-process covariance — so targeted improvements to small-scale emulation accuracy (denser simulation grids) would turn clipping into a compet
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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 / 6 minor

Summary. The paper presents the first systematics-controlled application of density-field clipping to cosmological inference from real weak lensing data. Clipping replaces convergence-map pixels above a fixed threshold with the threshold value and recovers a clipped shear catalogue; the authors model the clipped and unclipped shear correlation functions jointly. The model is built from the cosmoSLICS and SLICS N-body suites, with the KiDS-1000 footprint, shape noise, multiplicative and additive shear calibrations, and photometric redshift distributions injected into mock catalogues. Gaussian-process emulators trained on 26 cosmologies predict the cosmological dependence; bias emulators and linear models describe intrinsic alignments, photo-z shifts, baryonic feedback, and source-lens clustering. The combined clipped and unclipped data vector yields Omega_m=0.263+0.035-0.038, S8=0.724+/-0.027, w0=-1.24+0.26-0.28, consistent with A21, with reported precision gains of 16% for S8 and 24% for w0 over the unclipped statistic alone, a 27% improvement on the IA amplitude, and an upper limit on baryonic feedback. Validation includes 1240 covariance realizations, leave-one-out emulator cross-validation with covariance inflation, a systematics-free SLICS mock, and a systematics-contaminated mock.

Significance. If the headline claims hold, this is a substantive methodological advance: it is the first demonstration that a non-Gaussian (HOWLS-type) statistic can be modelled end-to-end with systematics and applied to real lensing data, and that clipping extracts additional information while preferentially using scales least affected by baryons. The paper's strengths include the large and carefully constructed mock suites, the explicit inflation of the covariance with measured emulator error (Eq. 10), the check of Gaussianity of the data vector, the use of both Gaussian-with-Hartlap and Sellentin-Heavens likelihoods, the open-source release of the clipping and likelihood codes, and an independent simulation-based consistency check of the KiDS-1000 A21 result. The recovered consistency with LCDM and with A21 for the cosmological parameters, together with the agreement of the Delta-z posteriors with A21, gives reasonable confidence that the cosmology result is not dominated by a gross pipeline error.

major comments (3)
  1. [Sec. 2.2(iii) and Eq. (11)] The intrinsic-alignment bias model is the main load-bearing assumption of the combined-probe claim, and it is currently untested in precisely the two directions that matter. Equation (11) defines B_IA as the difference between AIA != 0 and AIA = 0 measurements from the IA Set, which spans full 10x10 deg^2 lightcones without the KiDS-1000 footprint and is run only at the fiducial cosmology. This single bias shape is scaled by AIA and added to the cosmology emulator at every point of the parameter space (Sec. 3.3). Clipping is a nonlinear, mask-dependent operation: the mask is reapplied to the reconstructed kappa map before thresholding, and the residual map Delta-kappa is masked before interpolation to galaxy positions (Sec. 3.1, Eqs. 1-3). The assumption that the IA bias of the clipped statistic is unaffected by the ~23% removed area is asserted in Sec. 2.2(iii) but not demonstrated, and the GI term in B_IA depends on cosmology through the lensing efficiency and the matter power spectrum. The Appendix C1 validation cannot catch a bias in B_IA because the systematics-contaminated mocks are generated with the same emulators and linear models, so that validation tests internal consistency only. The 3.2-sigma offset of the inferred AIA from the A21 value is exactly the signature a mask- or cosmology-dependent B_IA would produce, and since the combined probe is claimed to improve the AIA constraint by 27%, this matters for both the headline precision gains and the IA secondary result. I recommend that the authors (i) compute B_IA with a KiDS-like mask applied to the IA Set realizations and quantify the change in both the clipped and unclipped bias; (ii) propagate a conservative systematic error on B_IA and demonstrate that the 16% S8 and 24% w0 gains survive; and (iii) state explicitly that the Appendix C1 contaminated-mock test does not validate the bias model.
  2. [Sec. 2.2(v) and Sec. 3.3] The baryonic feedback model linearly rescales the magneticum dark-matter-only versus hydro difference with a single parameter bbary in [0,2], with the upper half of the prior representing an extrapolation to twice the magneticum feedback level. The comparison in Fig. 8 with hmcode for log10(T_AGN/K) = [7.6, 7.8, 8.0] shows that the fractional suppression of the unclipped correlation functions is not linear in feedback strength, and the clipped auto-correlations behave non-monotonically: stronger feedback reduces small-scale power, which decreases the amount of clipping and can increase power at 5-10 arcmin. A one-parameter linear rescaling therefore imposes a specific shape for the feedback effect, and the headline secondary result bbary < 0.97 depends on that shape. Please validate the linear-scaling ansatz against intermediate feedback strengths (for example, an additional magneticum or BAHAMAS run, or the multiple AGN-temperature nodes already available through hmcode for the unclipped part), or quantify the sensitivity of the upper limit to relaxing the linearity assumption.
  3. [Appendix C1] The two mock validations cover complementary but incomplete ground. The systematics-contaminated mock data are drawn from the trained emulators and linear models themselves, so they verify that the sampler and likelihood recover the input values given the adopted bias model, but they cannot falsify that model; the SLICS-based systematics-free test verifies emulator generalisation to a sister simulation suite but contains no IA, photo-z, or baryon contamination. The paper would be strengthened by a third test in which the contamination is generated from a model outside the inference set, for example a TATT-motivated IA signal or hydrodynamics from an independent simulation code, to provide at least one end-to-end check that the decompositions of Eqs. (11)-(13) and the linear BB rescaling recover the truth.
minor comments (6)
  1. [Sec. 3.4 and Table 2] The text states that the unclipped analysis is prior-limited for w0, yet Table 2 reports a 24% precision gain for w0 without qualification. Please report the unclipped marginalised width for w0 and state clearly how much of the quoted gain reflects the prior rather than information in the data; the same caveat applies to the FoM factors of 1.6 and 4.0 in Sec. 4.
  2. [Sec. 3.1 and Eq. (4)] In Eq. (4) the argument of the tangential and cross components is written with theta_g,b for both galaxies in the numerator and denominator; the second position should refer to galaxy a (theta_g,a) so that the pair weighting is unambiguous.
  3. [Sec. 4] The claimed 3.2-sigma disagreement of AIA with A21 should be justified: with the quoted values AIA = 0.39+0.32-0.37 (A21) and AIA = -0.50+0.19-0.20 (this work), a naive quadrature combination gives roughly 2.1-2.3 sigma depending on how the asymmetric errors are combined.
  4. [Abstract] There is a typographical error in the abstract: 'complimentary information' should be 'complementary information'.
  5. [Sec. 4] The comparison with A21 for Omega_m is made for different models (A21 fixes w0 = -1, this work varies w0); one sentence noting the implication of that difference for the 0.8-sigma consistency statement would help the reader interpret the comparison.
  6. [Sec. 3.1] The clipping threshold (kappa_c = 0.010) and smoothing scale (sigma_s = 6.6 arcmin) are inherited from G18 and revalidated with a single test; since these settings control the ~20% clipped area and the depth of the clipping trough, a brief exploration of the sensitivity of the final constraints to (kappa_c, sigma_s) would strengthen the robustness picture.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: simulation-based emulators and external mocks support the central derivation; flagged assumptions are limitations, not circular reductions.

full rationale

The paper's central derivation chain is self-contained rather than circular. The clipped and unclipped shear correlation functions are predicted by Gaussian-process emulators trained on independent N-body simulation suites (cosmoSLICS, SLICS, magneticum), not fitted to the KiDS-1000 data. The cosmological emulators are validated by leave-one-out cross-validation and by recovery of the true cosmology from external SLICS mocks that are not part of the training set. Systematic contributions are constructed as differences between contaminated and baseline simulations (Eqs. 11-13 for IA, photo-z, and baryon biases); the free amplitudes (A_IA, Delta-z_i, b_bary) are nuisance parameters with priors, not quantities derived from the data vector that is then 'predicted'. The headline precision gains are outputs of the joint likelihood of clipped and unclipped probes and are additionally consistent with the fully independent A21 pipeline, although the S8 precision does not beat A21's quoted 2.3%. Two points deserve mention but do not amount to circularity. First, the systematics-contaminated mock validation in Appendix C1 is generated by the same emulators and linear models being tested, so it is an internal consistency check rather than external validation; the paper discloses this construction and also provides the external SLICS test. Second, the IA bias model in Sec. 2.2(iii) assumes additive separability of IA and cosmological contributions and negligible mask-IA coupling, using full-lightcone mocks without the KiDS footprint. This is a genuine modelling assumption and a plausible source of the reported 3.2-sigma A_IA tension with A21, but it is not a circular step: the IA bias shape comes from NLA mocks, and the assumption is stated explicitly rather than imported by definition. Self-citations to G18 for the clipping threshold and to Harnois-Deraaps et al. for emulator-error treatment are methodological and not load-bearing; the clipping threshold is re-tested in the tomographic bins. Overall, the derivation does not reduce to its inputs by construction, so the circularity score is low.

Assumptions & free parameters 10 free parameters · 7 assumptions · 0 invented entities

The analysis rests on simulation-based emulation. The main external inputs are the N-body codes, the KiDS calibration, and the adopted systematics models. No new physical entities are introduced. The free parameters are the standard cosmological and nuisance parameters plus the inherited clipping settings and the chosen emulator error definition.

free parameters (10)
  • Ωm = 0.263+0.035-0.038
    Marginalized matter density parameter from the combined analysis.
  • S8 = 0.724±0.027
    Marginalized clustering amplitude parameter from the combined analysis.
  • w0 = -1.24+0.26-0.28
    Marginalized dark energy equation-of-state parameter from the combined analysis.
  • h = >0.76 (prior-limited)
    Hubble parameter, only a lower limit is obtained from weak lensing.
  • AIA = -0.50+0.19-0.20
    Intrinsic alignment amplitude, constrained with 27% improved precision over the unclipped analysis.
  • Δz1-5 = (-0.002, 0.000, -0.012, -0.008, 0.008)
    Mean shifts of the five tomographic redshift distributions, sampled from a correlated multivariate Gaussian prior.
  • bbary = <0.97
    Baryonic feedback amplitude, linearly scaling the magneticum feedback bias; upper limit from the combined analysis.
  • Clipping threshold κc = 0.010
    Adopted from G18 based on KiDS-450, verified against KiDS-1000-like simulations; not optimized on the current data.
  • Gaussian smoothing scale σs = 6.6 arcmin
    Adopted from G18, verified for the tomographic bins; enters the convergence map construction.
  • Emulator error node count n = 12
    Chosen by hand among n=6, 12, 20, 0; the paper finds constraints insensitive to this choice for n>0.
assumptions (7)
  • domain assumption The non-linear linear alignment (NLA/δ-NLA) model captures the intrinsic alignment signal for KiDS-1000.
    The IA mock implements δ-NLA with no luminosity or redshift dependence; the paper argues TATT extra parameters are unconstrained by Stage-III data (Sec. 2.2(iii)).
  • ad hoc to paper Baryonic feedback from the magneticum simulation can be rescaled linearly via bbary in [0,2] to cover the full prior range.
    The BF bias is multiplied by bbary, extrapolating to twice the simulated feedback strength (Sec. 3.3, Fig. 8).
  • ad hoc to paper The intrinsic alignment bias is independent of the survey footprint and masking.
    The IA Set spans full 10x10 degree lightcones without the KiDS footprint; the paper assumes masking does not affect the IA bias (Sec. 2.2(iv)).
  • domain assumption The covariance matrix estimated at the fiducial cosmology is independent of cosmology.
    Covariance is computed from SLICS at the fiducial cosmology; the paper cites Eifler et al. (2009) that biases are small for Stage-III surveys (Sec. 2.2(ii)).
  • domain assumption The shear correlation function likelihood is Gaussian.
    Verified by Kolmogorov-Smirnov tests, with 97% of the 540 data vector elements passing (Sec. 3.4).
  • domain assumption The clipping threshold κc=0.010 and smoothing scale σs=6.6 arcmin from G18 remain appropriate for the KiDS-1000 tomographic bins.
    Adopted from G18 and validated on KiDS-1000-like simulations; not re-optimized for this dataset (Sec. 3.1).
  • ad hoc to paper Emulator errors can be approximated by diagonal covariance inflation using leave-one-out CV with n=12 nodes.
    The paper tests n=6, 12, 20, 0 and finds similar constraints for n>0; the n=12 definition is adopted as fiducial (Appendix A).

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Cite this review

Pith. "Pith review of KiDS-1000: Improved constraints on cosmology, intrinsic alignments and baryonic feedback from clipped cosmic shear." pith.science (2026). https://pith.science/paper/GAKZGJC2

@misc{pith2026260807377,
  author       = {Pith},
  title        = {Pith review of: KiDS-1000: Improved constraints on cosmology, intrinsic alignments and baryonic feedback from clipped cosmic shear},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GAKZGJC2}},
  note         = {Machine review of arXiv:2608.07377}
}
abstract

We present improved cosmological constraints from the fourth data release of the Kilo-Degree Survey ("KiDS-1000") using "clipped" shear correlation functions. Clipping filters the projected density field inferred from weak lensing data for the highest-density regions, allowing for two-point functions to extract additional cosmological information. We model, for the first time, the impact of systematics on clipped lensing statistics, including intrinsic alignments, baryonic feedback, photometric redshift uncertainties, and source-lens clustering. We train Gaussian process emulators on dark-matter-only and hydrodynamical $N$-body simulations to predict the cosmological and systematic dependence of both the clipped and conventional, "unclipped" shear correlation functions. We find that the combination of the clipped and unclipped probes improves the constraints on the free parameters of the $w$CDM model relative to the conventional approach, with a 16% tightening of the $S_8$ uncertainty and 24% for $w_0$. Our constraints, $\Omega_{\rm m} = 0.263^{+0.035}_{-0.038}$, $S_8=0.724^{+0.027}_{-0.027}$, and $w_0 = -1.24^{+0.26}_{-0.28}$, are consistent with the $\Lambda$CDM model and with the cosmic shear analysis of Asgari et al. (2021) via a completely independent simulation- and emulator-based forward-modelling approach. We also find the complimentary information in the clipped statistic provides an upper limit on the baryon feedback strength, and improves the constraints on intrinsic alignments by 27%.

Figures

Figures reproduced from arXiv: 2608.07377 by the authors.

Figure 1
Figure 1. Convergence, 𝜅, maps for the northern (upper) and southern (lower) patches of KiDS-1000, measured from galaxies in the highest tomographic bin (𝑧B ∈ [0.9, 1.2]) with regions exceeding the clipping threshold (𝜅 𝑐 = 0.010) highlighted by the red contours (approximately 20% of the observed area). with other mocks used to model HOWLS for Stage-III lensing sur￾veys: CosmoGrid (Kacprzak et al. 2023), the Marques et al. (2… view at source ↗
Figure 2
Figure 2. The distribution of input parameters in the Cosmology Set (cos￾moSLICS) colour-coded by 𝑆8 = 𝜎8 √︁ Ωm/0.3. The black star designates the fiducial cosmology at which the covariance matrix is estimated. to create 50 mock KiDS-1000 surveys with no realisation appear￾ing more than once across the 18 tiles. The absence of correlations across tiles in the simulations necessitates that we compute per-tile clipped and uncli… view at source ↗
Figure 3
Figure 3. The 𝜉 𝑖 𝑗 + ( 𝜃 ) (upper block) and 𝜉 𝑖 𝑗 − ( 𝜃 ) (lower block) scaled by 𝜃 × 104 , with the clipped and unclipped measurements occupying the lower-left and upper-right corners of each block respectively. Each panel is annotated with the 𝑖-𝑗 redshift bin combination. The cosmoSLICS are shown colour-coded by their input 𝑆8, the KiDS-1000 measurement by the magenta data data points, and the best-fit model by the dashe… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The correlation coefficient matrix for the clipped, 𝜉 c ± , and unclipped, 𝜉 uc ± , statistics. Each block marked by the white lines consists of 135 elements (15 redshift bin combinations × 9 𝜃 bins). smoothing scale implemented were identified in G18 as appropriate fo…
Figure 5
Figure 5. Figure 5: The accuracy of the cosmological emulators of 𝜉 c + ( 𝜃 ), assessed via cross-validation (CV) represented by the magenta band which is twice the range spanned by 68% of the measurements across the training nodes. The grey band shows twice the statistical uncertainty fo…
Figure 6
Figure 6. Figure 6: The fiducial cosmology 𝜉+ (scaled by 𝜃 × 104 [arcmin]) contaminated with intrinsic alignments of various strengths (colour-coded by 𝐴IA), with the clipped and unclipped measurements occupying the lower-left and upper-right corners respectively, relative to the (systema…
Figure 7
Figure 7. Figure 7: Biases, 𝐵 𝑐 +;Δ𝑧𝑖 (Eqn. 13) in four 𝜃 bins (as shown by the colour bar) to the 𝜉 c + auto-correlations in the five tomographic bins (𝑖 ∈ [1, 5]; upper to lower panels respectively) as a function of the shift to the mean of the redshift distribution, Δ𝑧/𝜎𝑖 , where 𝜎𝑖 is…
Figure 8
Figure 8. Figure 8: The fractional impact of baryonic feedback (BF) on the clipped (lower left) and unclipped (upper right) 𝜉+ measured relative to the dark-matter-only (DMO) magneticum simulation (𝑏bary = 0.0; cyan line). The BF-contaminated magneticum simulation (𝑏bary = 1.0) is shown b…
Figure 9
Figure 9. Figure 9: Constraints for KiDS-1000 from the clipped (magenta), unclipped (black), and the combined (orange) 𝜉±, compared with the unclipped 𝜉± constraints of Asgari et al. (2021, A21, cyan). A21 do not constrain 𝑤0 (their analysis assumed the ΛCDM model, 𝑤0 = −1) or 𝑏bary; henc…
Figure 10
Figure 10. Figure 10: 𝑆8 constraints for KiDS-1000 under the fiducial inference settings (grey bar) compared with the results from omitting tomographic bins (‘t1’– ‘t5’ and ‘Auto only’), using an alternative definition of the emulator error (‘GP emu err’; see Appendix A), or neglecting the…

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Reviewed August 15, 2026 · model on record in the stance chip above.