Pith. sign in

REVIEW 3 major objections 6 minor 1 cited by

Chromatic Effects on the PSF and Shear Measurement for the Roman Space Telescope High-Latitude Wide Area Survey

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Chromatic PSF errors from star-galaxy color differences bias Roman shear measurements by 0.2% in the four weak-lensing bands and 2% in the wide filter, above mission limits; a first-order PSF-level correction restores the WL bands.

desk verdict Roman-specific chromatic PSF bias is real and large, the B_n correction formalism is useful, but the headline mitigation claim is idealized and needs its caveats in the abstract. read the letter →

arxiv 2505.00093 v1 pith:GRGOEAY5 submitted 2025-04-30 astro-ph.CO astro-ph.IM

Federico Berlfein , Rachel Mandelbaum , Xiangchong Li , Tianqing Zhang , Scott Dodelson , Katarina Markovic This is my paper · ORCID
classification astro-ph.COastro-ph.IM
keywords weakgravitationallensingchromaticPSFpointspreadfunctionRomanSpaceTelescopeshearcalibrationSEDdifferencesshapemeasurementself-organizingmaps
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 the wavelength dependence of Roman's point-spread function (PSF) makes the spectral energy distribution (SED) difference between stars and galaxies a serious systematic for weak-lensing shear. Using Roman-like image simulations, it measures multiplicative shear biases of about $0.2\%$ in the four weak-lensing bands Y106, J129, H158, and F184, and about $2\%$ in the wide filter W146, both above the mission requirement $|m| < 3.2\times 10^{-4}$; individual redshift bins reach $0.4$-$0.9\%$ and $3$-$6\%$. The paper then develops a PSF-level correction that expands the star-galaxy SED difference in a Taylor series about each filter's effective wavelength, and shows that with perfect SED knowledge the first-order term brings the four WL bands within the strict requirement, while W146 requires higher-order terms. It also tests two practical estimators of the needed SED slope, an analytical color-based estimator and self-organizing maps, finding both reduce biases but with sensitivity to the SED library used for training. A careful reader would care because the result makes chromatic correction a required component of Roman's shear calibration and offers a concrete framework for supplying it.

What carries the argument

The load-bearing object is a Taylor-expansion basis for the chromatic PSF error. The effective PSF difference between a star and a galaxy is written as $$\$\Delta$\mathrm{PSF}_{\rm eff}(x,y)=\sum_n \$\Delta$ S_n\, B_n(x,y),$$ where $\Delta S_n$ is the difference between flux-normalized SED Taylor coefficients about the filter's effective wavelength $\lambda_0$, and $B_n(x,y)=\int \mathrm{PSF}(x,y,\lambda)\,F(\lambda)\,(\lambda-\lambda_0)^n\,d\lambda$ is an SED-independent image depending only on the PSF model and filter throughput. Flux normalization makes $\Delta S_0$ vanish for linear SEDs, so the first-order term $\Delta S_1 B_1$ carries nearly all of the bias in the narrow WL bands. The factorization is what carries the argument: $B_n$ can be precomputed from a trusted PSF model, leaving only the scalar coefficient $\Delta S_1$ to be estimated from galaxy SEDs, and the correction is applied at the PSF level so it is independent of the shape-measurement method.

What would settle it

An on-orbit measurement of Roman's PSF size versus stellar color in each band would settle the matter: if the observed slope of PSF FWHM with color differs from the model's prediction by more than the sub-percent level required to keep $|m|$ below $3.2\times 10^{-4}$, the simulated bias amplitudes and the first-order correction basis are both in doubt. Repeating the end-to-end simulation with an independent PSF model, generated without using the model that also defines the correction basis, would test whether the $0.2\%$/$2\%$ biases and the correction success are an artifact of model reuse.

Watch

Extended reading notes

Core claim

The central claim is that chromatic PSF mismatch, caused by systematically different SEDs for the stars used to model the PSF and the galaxies whose shapes are measured, is a dominant systematic for Roman weak-lensing shear. Averaged over all simulated galaxies, the multiplicative bias is roughly $0.2\%$ in every WL band and $2\%$ in W146, an order of magnitude larger in the wide filter; additive bias is acceptable in the WL bands but exceeds the systematic budget in W146. Because a $1\%$ multiplicative bias maps to roughly $1.5\%$ bias in the cosmological parameter $S_8$, these amplitudes are cosmologically significant. The paper's constructive result is that when the PSF model and filter transmission are known, the chromatic PSF error factorizes into SED-dependent coefficients and SED-independent basis images, and the first-order term corrects the WL bands to within the strictest requirement when each galaxy's SED is known exactly; the wide filter resists first-order correction, and a second-order polynomial version performs unstably.

Load-bearing premise

The load-bearing premise is that the simulated wavelength-dependent Roman PSF used to create the images is a faithful model of the real Roman PSF, because the same model provides both the biased images and the correction basis; if actual filter coatings, charge diffusion, or coaddition of undersampled exposures change the PSF chromaticity, both the measured biases and the correction performance would shift.

Editorial extensions

If this is right

  • If the central claim is correct, Roman's weak-lensing analysis must apply a chromatic PSF correction before shape measurement; leaving the effect uncorrected exceeds the SRD multiplicative-bias requirement by roughly a factor of six in the WL bands.
  • With perfect per-galaxy SED information, the first-order correction satisfies the strictest multiplicative requirement in Y106, J129, H158, and F184, meaning the dominant remaining uncertainty shifts to how well SED slopes can be estimated from photometry.
  • In W146, even a perfect first-order correction leaves residual multiplicative bias, and a straightforward second-order polynomial correction is unstable; this argues that using the wide filter for shear requires either a higher-order correction or acceptance of larger systematics.
  • Ensemble-averaged corrections can fail in individual redshift bins, while redshift-bin-averaged corrections satisfy a relaxed requirement; therefore tomographic analyses need per-redshift-bin calibration.
  • Galaxy color gradients contribute at most about $10^{-4}$ to multiplicative bias in the WL bands, so the dominant chromatic effect is the star-galaxy SED difference rather than internal color gradients.

Reading between the lines

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

  • Beyond the paper: the same Taylor-basis formalism transfers to any diffraction-limited NIR survey with a well-characterized PSF model; the key input is the accuracy of $\mathrm{PSF}(x,y,\lambda)$, so the structure of the method is portable even though the paper only demonstrates it for Roman.
  • Beyond the paper: the wide-filter failure implies that a decision to use W146 for Roman weak lensing should be gated on on-orbit measurements of the PSF size-color relation; the paper's simulations are self-consistent, so real-data chromaticity remains untested.
  • Beyond the paper: the SOM experiments suggest that spectroscopic training samples for Roman should be built with explicit coverage of high-redshift SED space; the demonstrated degradation under cross-library training makes SED-space completeness a testable design requirement.
  • Beyond the paper: a direct follow-up is to test the correction on coadded Roman images rather than oversampled individual exposures; the paper leaves coaddition to future work, and coaddition can alter the effective PSF chromaticity.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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. This paper quantifies, with Roman-like image simulations, the shear calibration biases induced by the wavelength dependence of the PSF when the PSF is modeled with stars but applied to galaxies. The authors use two extragalactic catalogs (Diffsky and cosmoDC2) with distinct SED libraries, a Kurucz-based stellar catalog, and the galsim.roman PSF model to generate noiseless oversampled postage stamps, measuring shapes with the FPFS/AnaCal estimator. They find uncorrected multiplicative biases of roughly 0.2% in the four Roman WL bands and roughly 2% in the wide filter W146, exceeding both the SRD requirement |m| < 3.2e-4 and the relaxed 1e-3 requirement; additive biases are acceptable in the WL bands but not in W146. The paper then develops a PSF-level correction based on a Taylor expansion of the flux-normalized SED difference into coefficients ΔS_n and precomputed basis images B_n, and shows that with perfect per-galaxy SED knowledge the first-order correction brings all WL bands within the stringent requirement, while W146 is not corrected to requirement. It also tests an analytical color-based estimator and a self-organizing-map estimator for realistic implementation, finding that both reduce biases but with catalog-dependent performance.

Significance. If the results hold, this is a timely and useful contribution for Roman weak lensing: it is the first systematic quantification of chromatic PSF biases from star-galaxy SED differences for the four Roman WL bands and W146, and it proposes a clean, SED-only correction framework that is independent of the shape-measurement method. The numerical work is careful: 10,000 galaxies per catalog, 45-degree rotations to suppress shape noise, three input shears, bootstrap error bars, a convergence check at 1,500 galaxies, and two independent SED libraries that give consistent uncorrected biases. The correction coefficients ΔS_n are derived from the SEDs, not fitted to the measured bias, so there is no parameter-fitted-to-target circularity. The public code and catalogs also make the analysis reproducible. The principal caveat is that the validation is performed entirely within the galsim.roman PSF model: the same model produces the simulated images and the B_n correction basis, so the Roman-specific amplitude of the biases and the demonstrated post-correction accuracy are conditional on that model's fidelity.

major comments (3)
  1. [Sec. 5.2.1 and Abstract] The central mitigation claim is validated in a closed loop: both the simulated galaxy images and the B1 basis functions used in Eq. (14) are generated from the same galsim.roman PSF model. A wavelength-dependent error in that model, for example from filter coating uncertainties, charge diffusion, or reshaping by coaddition (effects explicitly deferred in Sec. 3.5 and Sec. 7), would change both the uncorrected bias amplitude and the correction basis. The manuscript therefore does not yet demonstrate that the corrected bias remains below |m| < 3.2e-4 for the actual Roman PSF. Please add a sensitivity test that perturbs the PSF wavelength dependence (e.g., scaling B1 or using an independent PSF model such as WebbPSF) and shows the corrected m stays within budget, and state this closed-loop caveat explicitly in the abstract.
  2. [Sec. 5.2.1, Eq. (15)] The text states that the SCA-constant approximation for B_n is 'tested later on for B1 and confirmed to hold,' but I could not find this test anywhere in the manuscript: Secs. 5.3 through 6 contain no comparison of the center-of-SCA basis against the basis at the actual galaxy positions. Because Eq. (15) enters every corrected measurement in Fig. 5, please either present the missing test with quantitative residuals, or remove the claim and propagate the approximation uncertainty into the corrected m values.
  3. [Sec. 5.3 and Abstract] The abstract's statement that 'higher-order terms are necessary for the wide filter' is not backed by a working higher-order implementation in the paper: Sec. 5.3 reports that a second-order polynomial fit produced a higher multiplicative bias than the first-order correction and failed to meet the relaxed requirement in all redshift bins for both catalogs. Please either demonstrate a higher-order correction that actually reduces W146 biases to requirement, or rephrase the conclusion to say that the first-order correction is insufficient for W146 and a successful higher-order correction is not yet demonstrated.
minor comments (6)
  1. [Sec. 3.5] The choice of 0.0275 arcsec/pixel oversampled scale is described as 'somewhat arbitrary'; since the correction results and their comparison with coadded Roman images depend on pixel scale, please add a brief justification or a test of sensitivity to this choice.
  2. [Sec. 5.4, Table 2] What is called an 'upper limit' is actually the largest absolute bias across redshift bins, not a statistical upper limit; please rename it to something like 'largest |m| across redshift bins' to avoid over-interpretation.
  3. [Sec. 6.1, Eq. (23)] The statement that N_t/N_f 'resembles something like the color' is imprecise; the color is proportional to log10(N_t/N_f), not to the flux ratio itself, and this distinction affects how one expects the estimator to behave with noise.
  4. [Sec. 6.3, Table 3] The phrase 'fail to exceed the allowed relative error' should read 'exceed the allowed relative error' or 'fail to stay within the allowed relative error'; the current wording states the opposite of what Table 3 shows.
  5. [Sec. 2.2, Eq. (3)] Please define explicitly whether the SEDs in Eq. (3) are in photon units or energy units; the text mentions the necessary λ/hc conversion factor, but the equation as written is ambiguous, and the slope coefficients ΔS1 depend on this convention.
  6. [Fig. 4] The green curve representing the second-order approximation is not labeled in the legend of the right panel; please add a legend entry or a clear caption description so the reader can identify it.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the chromatic shear biases are simulation outputs, and the correction coefficients come from SEDs, not from the measured biases.

full rationale

The paper's central results are not circular. The multiplicative and additive biases in Fig. 3 are outputs of forward image simulations using GalSim and the AnaCal estimator (Eqs. 2 and 7), with galaxy and stellar SEDs from independent catalogs; no parameter is fitted to the measured m or c. The correction basis B_n is defined by Eq. (11) from the PSF model and filter throughput, and the coefficients ΔS_n are estimated from the SEDs in Sec. 5.2.2, not from the shear biases, so the corrected results in Fig. 5 are not a fit renamed as a prediction. The main caveat is model dependence: Sec. 5.2.1 states 'In this work we use the model provided by GalSim, as this is also the software used for image simulations,' so the demonstration that first-order correction meets requirements is closed-loop within galsim.roman. That is an external-validity limitation, not a circular reduction, and the paper explicitly flags related limits: Sec. 3.5 says 'No detector effects are included in these simulations,' and Sec. 7 says 'We emphasize that this work has produced results for oversampled images. Therefore, it will be extremely important to understand how the image coaddition of individual undersampled exposures will affect the chromaticity of the coadded PSF.' Self-citations to AnaCal (Li & Mandelbaum 2023) and to galsim.roman (Kannawadi et al. 2016) are code and estimator support with independent implementation, not load-bearing uniqueness claims or ansatz smuggling. Consequently no circular step is identified.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central bias numbers are measured, not fitted, so there are no free parameters in the headline result; the listed parameters are shape-measurement and simulation choices that influence the amplitudes. The axioms are the standard mathematical expansion plus the domain assumptions about PSF model fidelity, SED library representativeness, and idealized image conditions.

free parameters (4)
  • sigma_h (FPFS shapelet scale) = 1.15 x PSF scale radius
    Chosen by hand in Sec. 3.6 to be 15% larger than the PSF scale radius for each filter. It sets the shapelet basis size and can affect measured ellipticities and shear responses, so it contributes systematic uncertainty to m and c.
  • C (FPFS weight) = 10
    Set to 10 in Sec. 3.6, close to the LSST-optimal value, but not optimized for Roman's zero point and noise. It stabilizes the ellipticity estimator and can influence the bias amplitudes.
  • Oversampled pixel scale = 0.0275 arcsec/pixel
    Images are drawn at one-fourth the native Roman pixel scale in Sec. 3.5, a choice the authors call somewhat arbitrary and guided by Hirata et al. (2024). The measured PSF FWHM values depend on this sampling choice.
  • SOM grid and hyperparameters = 32x32 grid, std_coeff=12.0, learning rate=0.75
    Hand-set following the RAIL documentation in Sec. 6.2. These affect how finely the color space is binned and the accuracy of the slope estimates.
assumptions (5)
  • domain assumption The SED of each object can be flux-normalized and Taylor-expanded around the filter effective wavelength, and the linear term dominates the chromatic PSF difference (Sec. 5.1, Eq. 9-13).
    This is the core assumption of the correction method. It fails for the wide filter, where second-order terms are needed, and for high-redshift Diffsky galaxies with nonlinear SEDs (Sec. 6.3, Fig. 8).
  • domain assumption The galsim.roman and WebbPSF model reproduces the real Roman PSF wavelength dependence (Sec. 5.2.1).
    The bias measurements and B1 basis functions are computed with this model; the authors argue accurate modeling is expected for a space telescope, but this is not empirically demonstrated in the paper.
  • domain assumption Diffsky and cosmoDC2 SED libraries bracket the real galaxy SED population (Sec. 3.1).
    The results are almost identical between the two catalogs for the bias quantification, but the correction performance differs strongly, so the SED library is a load-bearing input.
  • domain assumption The images are noiseless, oversampled, single-exposure, and free of detector effects and blending (Sec. 3.5).
    This isolates chromatic effects but means that real survey conditions such as noise, coaddition, charge diffusion, and blending could change both the bias amplitudes and the correction accuracy. The paper lists these as future work.
  • standard math Flux normalization implies that the zero-order SED difference Delta-S0 vanishes for linear SEDs (Appendix B).
    Valid mathematical proof using the definition of effective wavelength; this justifies dropping the zero-order term and is correct within the linear model.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Chromatic Effects on the PSF and Shear Measurement for the Roman Space Telescope High-Latitude Wide Area Survey." pith.science (2026). https://pith.science/paper/GRGOEAY5

@misc{pith2026250500093,
  author       = {Pith},
  title        = {Pith review of: Chromatic Effects on the PSF and Shear Measurement for the Roman Space Telescope High-Latitude Wide Area Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GRGOEAY5}},
  note         = {Machine review of arXiv:2505.00093}
}
abstract

Weak gravitational lensing (WL) is a key cosmological probe that requires precise measurement of galaxy images to infer shape distortions, or shear, and constrain cosmology. Accurate estimation of the Point Spread Function (PSF) is crucial for shear measurement, but the wavelength dependence of the PSF introduces chromatic biases that can systematically impact shear inference. We focus on biases arising from spectral energy distribution (SED) differences between stars, used for PSF modeling, and galaxies, used for shear measurement. We investigate these effects in $\textit{Roman's}$ four design reference mission WL bands (Y106, J129, H158, F184) and wide filter (W146). Using $\textit{Roman}$-like image simulations, we quantify the induced shear biases and compare them to requirements on those biases. Multiplicative biases over all galaxies hover around $\sim$0.2% in the WL bands and 2% in the wide filter, exceeding the mission requirement of $|m| < 0.032\%$ and relaxed requirement of $|m| < 0.1\%$. In individual redshift bins, biases can reach 0.4$\unicode{x2013}$0.9% for the WL bands and 3$\unicode{x2013}$6% for the wide filter. Additive biases remain acceptable in the WL bands but exceed systematic limits in the wide filter. We develop and test PSF-level corrections, showing that a first-order correction reduces biases within survey requirements for the WL bands; however, higher-order terms are necessary for the wide filter. Our results highlight the necessity of chromatic corrections for precision WL with $\textit{Roman}$ and provide a framework for mitigating these biases. Finally, we compare analytical color-based corrections to self-organizing maps (SOMs) and find that both methods effectively reduce biases.

Figures

Figures reproduced from arXiv: 2505.00093 by the authors.

Figure 1
Figure 1. Left: Contour plot of the redshift vs. 𝐻-band magnitude for both Diffsky (filled blue) and cosmoDC2 (dashed red) galaxies. The adjacent 1D histograms show the respective distributions. A WL selection cut of 𝐻 < 24.96 is applied to exclude galaxies with SNR < 18. All magnitudes shown are the observed quantities after applying the catalog-level noise described in Sec. 3.3. We see that cosmoDC2 contains a higher number… view at source ↗
Figure 2
Figure 2. Examples of the normalized and oversampled Roman PSF produced using the galsim.roman module for a random star in the catalog. From left to right we show the bluest (Y106) and reddest (F184) filters used for this analysis, and the wide filter (W146) PSF, simulated at a pixel scale of 0.0275 arcsec/pixel, one-fourth of the native Roman pixel scale. We can visually see an increase in PSF size as we go from bluer to red… view at source ↗
Figure 3
Figure 3. Shear measurement bias due to chromatic PSF effects averaged over all 10,000 simulated galaxies when no attempt is made to mitigate the effect. Error bars, calculated using a bootstrap method, are very small and may be challenging to see: for context, the typical uncertainty on 𝑚 is 4 × 10−5 for the WL bands and 3 × 10−4 for the wide filter. Uncertainties on 𝑐 are approximately an order of magnitude smaller than tho… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Comparison of the average spin-0 component radial profile, 𝑎0 (𝑟 ), of the chromatic PSF differences from image simulations using Diffsky (blue solid line) and the first-order term in the Taylor expansion (red dotted line). The x-axis is normalized by the ratio of a ch…
Figure 5
Figure 5. Figure 5: Shear measurement multiplicative and additive biases after applying image-level correction to the effective stellar PSF using the first-order linear correction for both Diffsky (solid lines) and cosmoDC2 (dashed lines). We test the per-galaxy correction using the true …
Figure 6
Figure 6. Figure 6: Trained SOM for the Diffsky galaxies using 9 colors constructed from the LSST (ugrizy) + Roman (YJHF) bands. The top and bottom panels show the ⟨𝐽 − 𝐻⟩ color and ⟨𝑆 1 𝑔 ⟩ values for the training set galaxies, re￾spectively. The color distribution is somewhat smooth acr…
Figure 7
Figure 7. Figure 7: The multiplicative shear bias for filter H158 after the PSF-level correction using the estimated values of Δ𝑆ˆ 1 for both Diffsky (solid) and cosmoDC2 (dashed). Δ𝑆ˆ 1 is estimated in two ways: analytically using imaging data from adjacent filters J129 (blue) and F184 (…
Figure 8
Figure 8. Figure 8: The average flux-normalized galaxy SED for Diffsky (solid line) and cosmoDC2 (dashed line) for the highest redshift bin. The filled areas show the filter transmission curves of the 4 WL filters for visualization. We see a large difference between the average SEDs from …
Figure 9
Figure 9. Figure 9: The multiplicative shear bias for two tests of the impact of the SED library on the chromatic correction using SOMs to learn the SED-dependent correction coefficients. The two scenarios are: train the SOM on cosmoDC2 and test on Diffsky (blue solid line), and two diffe…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. D$_4$CNN$\times$AnaCal: Physics-Informed Machine Learning for Accurate and Precise Weak Lensing Shear Estimation

    astro-ph.IM 2026-03 conditional novelty 6.0 of 10

    A D4-equivariant CNN calibrated with analytic gradients measures weak-lensing shear from isolated-galaxy simulations with sub-0.1% multiplicative bias and ~10% lower shape noise than FPFS.

Reference graph

Works this paper leans on

87 extracted references · 1 canonical work pages · cited by 1 Pith paper

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    Aihara H., et al., 2018, @doi [ ] 10.1093/pasj/psx066 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...4A 70, S4

    doi:10.1093/pasj/psx066 2018

  3. [3]

    Albrecht A., et al., 2006, @doi [arXiv e-prints] 10.48550/arXiv.astro-ph/0609591 , https://ui.adsabs.harvard.edu/abs/2006astro.ph..9591A pp astro--ph/0609591

    arXiv doi:10.48550/arxiv.astro-ph/0609591 2006

  4. [4]

    Amon A., et al., 2022, @doi [ ] 10.1103/PhysRevD.105.023514 , https://ui.adsabs.harvard.edu/abs/2022PhRvD.105b3514A 105, 023514

    doi:10.1103/physrevd.105.023514 2022

  5. [5]

    R., 2000, @doi [ ] 10.1086/316632 , https://ui.adsabs.harvard.edu/abs/2000PASP..112.1360A 112, 1360

    Anderson J., King I. R., 2000, @doi [ ] 10.1086/316632 , https://ui.adsabs.harvard.edu/abs/2000PASP..112.1360A 112, 1360

    doi:10.1086/316632 2000

  6. [6]

    Antilogus P., Astier P., Doherty P., Guyonnet A., Regnault N., 2014, @doi [Journal of Instrumentation] 10.1088/1748-0221/9/03/C03048 , https://ui.adsabs.harvard.edu/abs/2014JInst...9C3048A 9, C03048

    doi:10.1088/1748-0221/9/03/c03048 2014

  7. [7]

    Asgari M., et al., 2021, @doi [ ] 10.1051/0004-6361/202039070 , https://ui.adsabs.harvard.edu/abs/2021A&A...645A.104A 645, A104

    doi:10.1051/0004-6361/202039070 2021

  8. [8]

    Bartelmann M., Maturi M., 2017, @doi [Scholarpedia] 10.4249/scholarpedia.32440 , https://ui.adsabs.harvard.edu/abs/2017SchpJ..1232440B 12, 32440

    doi:10.4249/scholarpedia.32440 2017

Show all 87 references
  1. [9]

    J., 2012, @doi [New Astronomy] https://doi.org/10.1016/j.newast.2011.07.004 , 17, 175

    Benson A. J., 2012, @doi [New Astronomy] https://doi.org/10.1016/j.newast.2011.07.004 , 17, 175

    2012 doi

  2. [10]

    A., Liebert J., 1992, @doi [ ] 10.1086/171575 , https://ui.adsabs.harvard.edu/abs/1992ApJ...394..228B 394, 228

    Bergeron P., Saffer R. A., Liebert J., 1992, @doi [ ] 10.1086/171575 , https://ui.adsabs.harvard.edu/abs/1992ApJ...394..228B 394, 228

    1992 doi

  3. [11]

    arXiv:2408.00922

    Campos A., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2408.00922 , https://ui.adsabs.harvard.edu/abs/2024arXiv240800922C p. arXiv:2408.00922

    2024 doi arXiv

  4. [12]

    M., Laliotis K., Yamamoto M., Macbeth E., Troxel M

    Cao K., Hirata C. M., Laliotis K., Yamamoto M., Macbeth E., Troxel M. A., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2410.05442 , https://ui.adsabs.harvard.edu/abs/2024arXiv241005442C p. arXiv:2410.05442

    2024 doi

  5. [13]

    G., Strauss M

    Carlsten S. G., Strauss M. A., Lupton R. H., Meyers J. E., Miyazaki S., 2018, @doi [ ] 10.1093/mnras/sty1636 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.1491C 479, 1491

    2018 doi

  6. [14]

    Chang C., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.22134.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.2572C 427, 2572

    2012

  7. [15]

    F., Kalmbach J

    Crenshaw J. F., Kalmbach J. B., Gagliano A., Yan Z., Connolly A. J., Malz A. I., Schmidt S. J., Collaboration T. L. D. E. S., 2024, Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows ( @eprint arXiv 2405.04740 )

    2024 arXiv

  8. [16]

    S., Amara A., Voigt L

    Cypriano E. S., Amara A., Voigt L. M., Bridle S. L., Abdalla F. B., R \'e fr \'e gier A., Seiffert M., Rhodes J., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16461.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.405..494C 405, 494

    2010

  9. [17]

    Dalal R., et al., 2023, @doi [ ] 10.1103/PhysRevD.108.123519 , https://ui.adsabs.harvard.edu/abs/2023PhRvD.108l3519D 108, 123519

    2023 doi

  10. [18]

    Eifler T., et al., 2021, @doi [ ] 10.1093/mnras/stab533 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.1514E 507, 1514

    2021 doi

  11. [19]

    Eriksen M., Hoekstra H., 2018, @doi [ ] 10.1093/mnras/sty830 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.3433E 477, 3433

    2018 doi

  12. [20]

    Euclid Collaboration et al., 2019, @doi [ ] 10.1051/0004-6361/201935187 , https://ui.adsabs.harvard.edu/abs/2019A&A...627A..59E 627, A59

    2019 doi

  13. [21]

    Euclid Collaboration et al., 2022, @doi [ ] 10.1051/0004-6361/202141938 , https://ui.adsabs.harvard.edu/abs/2022A&A...662A.112E 662, A112

    2022 doi

  14. [22]

    J., Gorgas J., Peletier R

    Falc \'o n-Barroso J., S \'a nchez-Bl \'a zquez P., Vazdekis A., Ricciardelli E., Cardiel N., Cenarro A. J., Gorgas J., Peletier R. F., 2011, @doi [ ] 10.1051/0004-6361/201116842 , https://ui.adsabs.harvard.edu/abs/2011A&A...532A..95F 532, A95

    2011 doi

  15. [23]

    L., et al., 2020, @doi [ ] 10.3847/1538-3881/ab8a43 , https://ui.adsabs.harvard.edu/abs/2020AJ....159..258G 159, 258

    Graham M. L., et al., 2020, @doi [ ] 10.3847/1538-3881/ab8a43 , https://ui.adsabs.harvard.edu/abs/2020AJ....159..258G 159, 258

    2020 doi

  16. [24]

    R., Lin H., Gawiser E., Jha S

    Gris P., Awan H., Becker M. R., Lin H., Gawiser E., Jha S. W., The LSST Dark Energy Science Collaboration 2024, @doi [ ] 10.3847/1538-4365/ad79f5 , https://ui.adsabs.harvard.edu/abs/2024ApJS..275...21G 275, 21

    2024 doi

  17. [25]

    Hearin A., Korytov D., Kovacs E., Benson A., Aung H., Bradshaw C., Campbell D., LSST Dark Energy Science Collaboration 2020, @doi [ ] 10.1093/mnras/staa1495 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.5040H 495, 5040

    2020 doi

  18. [26]

    P., Chaves-Montero J., Alarcon A., Becker M

    Hearin A. P., Chaves-Montero J., Alarcon A., Becker M. R., Benson A., 2023, @doi [ ] 10.1093/mnras/stad456 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.1741H 521, 1741

    2023 doi

  19. [27]

    Heitmann K., et al., 2019, @doi [ ] 10.3847/1538-4365/ab4da1 , https://ui.adsabs.harvard.edu/abs/2019ApJS..245...16H 245, 16

    2019 doi

  20. [28]

    Heymans C., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10198.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.368.1323H 368, 1323

    2006

  21. [29]

    M., et al., 2024, @doi [ ] 10.1093/mnras/stae182 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.2533H 528, 2533

    Hirata C. M., et al., 2024, @doi [ ] 10.1093/mnras/stae182 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.2533H 528, 2533

    2024 doi

  22. [30]

    Hoekstra H., Jain B., 2008, @doi [Annual Review of Nuclear and Particle Science] 10.1146/annurev.nucl.58.110707.171151 , https://ui.adsabs.harvard.edu/abs/2008ARNPS..58...99H 58, 99

    2008

  23. [31]

    arXiv:1702.02600

    Huff E., Mandelbaum R., 2017, @doi [arXiv e-prints] 10.48550/arXiv.1702.02600 , https://ui.adsabs.harvard.edu/abs/2017arXiv170202600H p. arXiv:1702.02600

    2017 doi arXiv

  24. [32]

    Huterer D., Takada M., Bernstein G., Jain B., 2006, @doi [Monthly Notices of the Royal Astronomical Society] 10.1111/j.1365-2966.2005.09782.x , 366, 101

    2006

  25. [34]

    Ivezi \'c Z ., et al., 2019b, @doi [ ] 10.3847/1538-4357/ab042c , https://ui.adsabs.harvard.edu/abs/2019ApJ...873..111I 873, 111

    doi

  26. [35]

    Jarvis M., et al., 2021, @doi [ ] 10.1093/mnras/staa3679 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.1282J 501, 1282

    2021 doi

  27. [36]

    Joachimi B., et al., 2015, @doi [ ] 10.1007/s11214-015-0177-4 , https://ui.adsabs.harvard.edu/abs/2015SSRv..193....1J 193, 1

    2015 doi

  28. [37]

    Juri \'c M., et al., 2008, @doi [ ] 10.1086/523619 , https://ui.adsabs.harvard.edu/abs/2008ApJ...673..864J 673, 864

    2008 doi

  29. [38]

    E., Burchat P

    Kamath S., Meyers J. E., Burchat P. R., 2019, @doi [arXiv e-prints] 10.48550/arXiv.1907.04459 , https://ui.adsabs.harvard.edu/abs/2019arXiv190704459K p. arXiv:1907.04459

    2019 doi arXiv

  30. [39]

    A., Mandelbaum R., Hirata C

    Kannawadi A., Shapiro C. A., Mandelbaum R., Hirata C. M., Kruk J. W., Rhodes J. D., 2016, @doi [ ] 10.1088/1538-3873/128/967/095001 , https://ui.adsabs.harvard.edu/abs/2016PASP..128i5001K 128, 095001

    2016 doi

  31. [40]

    Kilbinger M., 2015, @doi [Rept. Prog. Phys.] 10.1088/0034-4885/78/8/086901 , 78, 086901

    2015 doi

  32. [41]

    Kohonen T., 1982, @doi [Biological Cybernetics] 10.1007/BF00337288 , 43, 59

    1982 doi

  33. [42]

    Korytov D., et al., 2019, @doi [ ] 10.3847/1538-4365/ab510c , https://ui.adsabs.harvard.edu/abs/2019ApJS..245...26K 245, 26

    2019 doi

  34. [43]

    Kovacs E., et al., 2022, @doi [The Open Journal of Astrophysics] 10.21105/astro.2110.03769 , https://ui.adsabs.harvard.edu/abs/2022OJAp....5E...1K 5, 1

    2022 arXiv

  35. [44]

    Kurucz R., 1993, Robert Kurucz CD-ROM, https://ui.adsabs.harvard.edu/abs/1993KurCD..13.....K 13

    1993

  36. [45]

    M., Cao K., Yamamoto M., Troxel M., 2024, @doi [ ] 10.1088/1538-3873/ad9bec , https://ui.adsabs.harvard.edu/abs/2024PASP..136l4506L 136, 124506

    Laliotis K., Macbeth E., Hirata C. M., Cao K., Yamamoto M., Troxel M., 2024, @doi [ ] 10.1088/1538-3873/ad9bec , https://ui.adsabs.harvard.edu/abs/2024PASP..136l4506L 136, 124506

    2024 doi

  37. [46]

    Li X., Mandelbaum R., 2023, @doi [ ] 10.1093/mnras/stad890 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.4904L 521, 4904

    2023 doi

  38. [47]

    Li X., Katayama N., Oguri M., More S., 2018, @doi [ ] 10.1093/mnras/sty2548 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.4445L 481, 4445

    2018 doi

  39. [48]

    Li X., Li Y., Massey R., 2022, @doi [ ] 10.1093/mnras/stac342 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.4850L 511, 4850

    2022 doi

  40. [49]

    Li X., et al., 2023, @doi [ ] 10.1103/PhysRevD.108.123518 , https://ui.adsabs.harvard.edu/abs/2023PhRvD.108l3518L 108, 123518

    2023 doi

  41. [50]

    Li X., Mandelbaum R., Jarvis M., Li Y., Park A., Zhang T., 2024, @doi [ ] 10.1093/mnras/stad3895 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.52710388L 527, 10388

    2024 doi

  42. [51]

    Li X., Mandelbaum R., The LSST Dark Energy Science Collaboration 2025, @doi [ ] 10.1093/mnras/stae2764 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536.3663L 536, 3663

    2025 doi

  43. [52]

    Liaudat T., Starck J.-L., Kilbinger M., 2023, @doi [Front. Astron. Space Sci.] 10.3389/fspas.2023.1158213 , 10, 1158213

    2023

  44. [53]

    Mandelbaum R., 2018, @doi [ ] 10.1146/annurev-astro-081817-051928 , https://ui.adsabs.harvard.edu/abs/2018ARA&A..56..393M 56, 393

    2018 doi

  45. [54]

    Massey R., et al., 2012, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/sts371 , 429, 661

    2012 doi

  46. [55]

    E., Burchat P

    Meyers J. E., Burchat P. R., 2015a, @doi [JINST] 10.1088/1748-0221/10/06/C06004 , 10, C06004

    doi

  47. [56]

    E., Burchat P

    Meyers J. E., Burchat P. R., 2015b, @doi [Astrophys. J.] 10.1088/0004-637X/807/2/182 , 807, 182

    doi

  48. [57]

    Mosby G., et al., 2020, @doi [Journal of Astronomical Telescopes, Instruments, and Systems] 10.1117/1.JATIS.6.4.046001 , https://ui.adsabs.harvard.edu/abs/2020JATIS...6d6001M 6, 046001

    2020 doi

  49. [58]

    Munshi D., Valageas P., van Waerbeke L., Heavens A., 2008, @doi [ ] 10.1016/j.physrep.2008.02.003 , https://ui.adsabs.harvard.edu/abs/2008PhR...462...67M 462, 67

    2008 doi

  50. [59]

    Myles J., et al., 2021, @doi [ ] 10.1093/mnras/stab1515 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.4249M 505, 4249

    2021 doi

  51. [60]

    Niemi S.-M., Cropper M., Szafraniec M., Kitching T., 2015, @doi [Experimental Astronomy] 10.1007/s10686-015-9440-7 , https://ui.adsabs.harvard.edu/abs/2015ExA....39..207N 39, 207

    2015 doi

  52. [61]

    arXiv:2501.05632

    OpenUniverse et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.05632 , https://ui.adsabs.harvard.edu/abs/2025arXiv250105632O p. arXiv:2501.05632

    2025 doi

  53. [62]

    L., 2008, @doi [ ] 10.1051/0004-6361:20079150 , https://ui.adsabs.harvard.edu/abs/2008A&A...484...67P 484, 67

    Paulin-Henriksson S., Amara A., Voigt L., Refregier A., Bridle S. L., 2008, @doi [ ] 10.1051/0004-6361:20079150 , https://ui.adsabs.harvard.edu/abs/2008A&A...484...67P 484, 67

    2008 doi

  54. [63]

    D., Sivaramakrishnan A., Lajoie C.-P., Elliott E., Pueyo L., Ravindranath S., Albert L., 2014, in Oschmann Jr

    Perrin M. D., Sivaramakrishnan A., Lajoie C.-P., Elliott E., Pueyo L., Ravindranath S., Albert L., 2014, in Oschmann Jr. J. M., Clampin M., Fazio G. G., MacEwen H. A., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 9143, Space Telescopes ...

    2014 doi

  55. [64]

    W., et al., 2013, @doi [ ] 10.1051/0004-6361/201219230 , https://ui.adsabs.harvard.edu/abs/2013A&A...551A.119P 551, A119

    Piotrowski L. W., et al., 2013, @doi [ ] 10.1051/0004-6361/201219230 , https://ui.adsabs.harvard.edu/abs/2013A&A...551A.119P 551, A119

    2013 doi

  56. [65]

    A., Bernstein G., 2012, @doi [ ] 10.1086/668294 , https://ui.adsabs.harvard.edu/abs/2012PASP..124.1113P 124, 1113

    Plazas A. A., Bernstein G., 2012, @doi [ ] 10.1086/668294 , https://ui.adsabs.harvard.edu/abs/2012PASP..124.1113P 124, 1113

    2012 doi

  57. [66]

    Plazas A. A., Shapiro C., Kannawadi A., Mandelbaum R., Rhodes J., Smith R., 2016, @doi [ ] 10.1088/1538-3873/128/968/104001 , https://ui.adsabs.harvard.edu/abs/2016PASP..128j4001P 128, 104001

    2016 doi

  58. [67]

    19, Progress in Optics

    Roddier F., 1981, in Wolf E., ed., Progress in Optics, Vol. 19, Progress in Optics. Elsevier, pp 281--376, @doi https://doi.org/10.1016/S0079-6638(08)70204-X , https://www.sciencedirect.com/science/article/pii/S007966380870204X

    1981 doi

  59. [68]

    Rowe B. T. P., et al., 2015, @doi [Astronomy and Computing] 10.1016/j.ascom.2015.02.002 , https://ui.adsabs.harvard.edu/abs/2015A&C....10..121R 10, 121

    2015 doi

  60. [69]

    N., 2017, @doi [ ] 10.1103/PhysRevD.95.123512 , https://ui.adsabs.harvard.edu/abs/2017PhRvD..95l3512S 95, 123512

    Schaan E., Krause E., Eifler T., Dor \'e O., Miyatake H., Rhodes J., Spergel D. N., 2017, @doi [ ] 10.1103/PhysRevD.95.123512 , https://ui.adsabs.harvard.edu/abs/2017PhRvD..95l3512S 95, 123512

    2017 doi

  61. [70]

    arXiv:2501.05781

    Schutt T., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.05781 , https://ui.adsabs.harvard.edu/abs/2025arXiv250105781S p. arXiv:2501.05781

    2025 doi arXiv

  62. [71]

    F., et al., 2022, @doi [ ] 10.1103/PhysRevD.105.023515 , https://ui.adsabs.harvard.edu/abs/2022PhRvD.105b3515S 105, 023515

    Secco L. F., et al., 2022, @doi [ ] 10.1103/PhysRevD.105.023515 , https://ui.adsabs.harvard.edu/abs/2022PhRvD.105b3515S 105, 023515

    2022 doi

  63. [72]

    Semboloni E., et al., 2013, @doi [Mon. Not. Roy. Astron. Soc.] 10.1093/mnras/stt602 , 432, 2385

    2013 doi

  64. [73]

    S., Huff E

    Sheldon E. S., Huff E. M., 2017, @doi [ ] 10.3847/1538-4357/aa704b , https://ui.adsabs.harvard.edu/abs/2017ApJ...841...24S 841, 24

    2017 doi

  65. [74]

    S., Becker M

    Sheldon E. S., Becker M. R., MacCrann N., Jarvis M., 2020, @doi [ ] 10.3847/1538-4357/abb595 , https://ui.adsabs.harvard.edu/abs/2020ApJ...902..138S 902, 138

    2020 doi

  66. [75]

    arXiv:1503.03757

    Spergel D., et al., 2015, @doi [arXiv e-prints] 10.48550/arXiv.1503.03757 , https://ui.adsabs.harvard.edu/abs/2015arXiv150303757S p. arXiv:1503.03757

    2015 doi arXiv

  67. [76]

    The Dark Energy Survey Collaboration 2005, @doi [arXiv e-prints] 10.48550/arXiv.astro-ph/0510346 , https://ui.adsabs.harvard.edu/abs/2005astro.ph.10346T pp astro--ph/0510346

    2005 doi arXiv

  68. [77]

    arXiv:1809.01669

    The LSST Dark Energy Science Collaboration et al., 2018, @doi [arXiv e-prints] 10.48550/arXiv.1809.01669 , https://ui.adsabs.harvard.edu/abs/2018arXiv180901669T p. arXiv:1809.01669

    2018 doi arXiv

  69. [78]

    A., Ishak M., 2015, @doi [ ] 10.1016/j.physrep.2014.11.001 , https://ui.adsabs.harvard.edu/abs/2015PhR...558....1T 558, 1

    Troxel M. A., Ishak M., 2015, @doi [ ] 10.1016/j.physrep.2014.11.001 , https://ui.adsabs.harvard.edu/abs/2015PhR...558....1T 558, 1

    2015 doi

  70. [79]

    A., et al., 2023, @doi [ ] 10.1093/mnras/stad664 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.2801T 522, 2801

    Troxel M. A., et al., 2023, @doi [ ] 10.1093/mnras/stad664 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.2801T 522, 2801

    2023 doi

  71. [80]

    M., Bridle S

    Voigt L. M., Bridle S. L., Amara A., Cropper M., Kitching T. D., Massey R., Rhodes J., Schrabback T., 2012, @doi [ ] 10.1111/j.1365-2966.2011.20395.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.421.1385V 421, 1385

    2012

  72. [81]

    C., Creath K., 1992, in Shannon R

    Wyant J. C., Creath K., 1992, in Shannon R. R., Wyant J. C., eds, 1 Vol. 11, Applied Optics and Optical Engineering, Volume XI. p. 2

    1992

  73. [82]

    A., Jarvis M., Mandelbaum R., Hirata C., Long H., Choi A., Zhang T., 2023, @doi [ ] 10.1093/mnras/stac2644 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.4241Y 519, 4241

    Yamamoto M., Troxel M. A., Jarvis M., Mandelbaum R., Hirata C., Long H., Choi A., Zhang T., 2023, @doi [ ] 10.1093/mnras/stac2644 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.4241Y 519, 4241

    2023 doi

  74. [83]

    Zhang T., 2018, Measuring the Chromatic Effect of Atmospheric Point-Spread-Function in Optical Wavelength, @doi 10.5281/zenodo.14947939 , https://doi.org/10.5281/zenodo.14947939

    2018 doi

  75. [84]

    Zonca A., Singer L., Lenz D., Reinecke M., Rosset C., Hivon E., Gorski K., 2019, @doi [Journal of Open Source Software] 10.21105/joss.01298 , 4, 1298

    2019 doi

  76. [85]

    de Jong J. T. A., et al., 2013, The Messenger, https://ui.adsabs.harvard.edu/abs/2013Msngr.154...44D 154, 44

    2013

  77. [86]

    H., Olivier S

    de Vries W. H., Olivier S. S., Asztalos S. J., Rosenberg L. J., Baker K. L., 2007, @doi [ ] 10.1086/517873 , https://ui.adsabs.harvard.edu/abs/2007ApJ...662..744D 662, 744

    2007 doi

  78. [87]

    L., et al., 2020, @doi [ ] 10.1051/0004-6361/202038835 , https://ui.adsabs.harvard.edu/abs/2020A&A...642A.200V 642, A200

    van den Busch J. L., et al., 2020, @doi [ ] 10.1051/0004-6361/202038835 , https://ui.adsabs.harvard.edu/abs/2020A&A...642A.200V 642, A200

    2020 doi

  79. [88]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.