pith. sign in

arxiv: 2606.19875 · v1 · pith:ZR72GOL5new · submitted 2026-06-18 · 🌌 astro-ph.GA · astro-ph.IM

The SPHEREx View of Galaxy Clusters: A Simulation-based Validation of the Forced Photometry Pipeline for Extended Sources

Hyeonguk Bahk , Ho Seong Hwang , Lindsey Bleem , Yujin Yang , Yoonsoo P. Bach , Yun-Ting Cheng , Brendan P. Crill , Olivier Dor\'e
show 7 more authors
Andreas L. Faisst Zhaoyu Huai Woong-Seob Jeong Bomee Lee Jeong Hwan Lee Jeonghyun Pyo Michael Zemcov
This is my paper

Pith reviewed 2026-06-26 17:02 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.IM
keywords galaxy clustersphotometric redshiftsforced photometrySPHERExsimulationssource blendingcluster cosmology
0
0 comments X

The pith

SPHEREx can measure cluster galaxy photometric redshifts to 0.003-0.01 precision and recover cluster redshifts to 0.002 scatter at low redshift.

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

This paper tests whether the SPHEREx instrument's photometry pipeline can deliver the accuracy needed for galaxy cluster studies. It creates realistic mock images of eight clusters across a range of redshifts using existing survey data and a sky simulator, then runs forced photometry on them. The results show that while blending of nearby sources causes some bad measurements, selecting brighter or higher signal-to-noise galaxies yields photometric redshifts precise enough to combine into cluster redshifts with very low bias and scatter below redshift 0.5. This matters because accurate cluster redshifts are essential for using clusters to probe the universe's expansion history.

Core claim

Through simulation of SPHEREx observations and application of forced photometry, the pipeline produces unbiased photometry for cluster members, with source blending as the main source of outliers. The survey reaches a depth of Ks ≈ 20 AB, sufficient to detect many members in low-redshift clusters but marginal at z ~ 1. With selection on brightness or signal-to-noise, cluster galaxy photometric redshifts achieve σ_NMAD ≈ 0.003-0.01, and stacking quality members recovers the cluster redshift with |Δz|/(1+z) < 0.002 bias and σ ≈ 0.002 scatter at z ≲ 0.5.

What carries the argument

The end-to-end simulation pipeline that generates mock SPHEREx images with the SPHEREx Sky Simulator from DESI Legacy Survey and COSMOS ancillary data, followed by forced photometry using The Tractor.

If this is right

  • Photometry remains generally unbiased despite blending challenges.
  • Effective depth allows detection of members 7-9 magnitudes fainter than the BCG at low z.
  • Appropriate sample selection based on brightness or S/N yields the quoted photo-z precision.
  • Recovered cluster redshifts meet the precision needed for cluster cosmology at z ≲ 0.5.

Where Pith is reading between the lines

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

  • Real SPHEREx data may require additional blending corrections if the simulation underestimates neighbor effects.
  • The method could be applied to other large-scale structure probes beyond clusters.
  • At higher redshifts, deeper ancillary data might be needed to maintain the same performance.
  • This validation sets a benchmark for future space-based photometry missions targeting extended sources.

Load-bearing premise

The simulated observations accurately capture the real SPHEREx instrument's noise properties, point-spread function, and source blending statistics.

What would settle it

Comparison of actual SPHEREx flight photometry and derived redshifts against spectroscopic measurements for the same galaxy clusters.

Figures

Figures reproduced from arXiv: 2606.19875 by Andreas L. Faisst, Bomee Lee, Brendan P. Crill, Ho Seong Hwang, Hyeonguk Bahk, Jeong Hwan Lee, Jeonghyun Pyo, Lindsey Bleem, Michael Zemcov, Olivier Dor\'e, Woong-Seob Jeong, Yoonsoo P. Bach, Yujin Yang, Yun-Ting Cheng, Zhaoyu Huai.

Figure 1
Figure 1. Figure 1: ). To construct our working sample, we randomly se￾lected seven clusters from the HeCS-omnibus, SPT￾SZ, and GOGREEN catalogs: Abell 2055, Abell 1361, Abell 2187, Abell 2537 (HeCS-omnibus), SPT￾CL J2145−5644, SPT-CL J0546−5345 (SPT-SZ), and SpARCS J1613+5649 (GOGREEN). The randomiza￾tion ensures that the resulting set spans the mass dis￾tribution of the parent catalogs and the redshift range from the local … view at source ↗
Figure 2
Figure 2. Figure 2: Sky distribution of the cluster samples used in this study. Small points represent clusters from HeC￾S-omnibus, SPT, and GOGREEN. Large red circles mark the cluster fields selected for our analysis. The COSMOS field is shown with a yellow star, and the approximate loca￾tions of the SPHEREx NEP and SEP deep fields are outlined in green. The blue shaded regions indicate the footprint of the DESI Legacy Surve… view at source ↗
Figure 3
Figure 3. Figure 3: Schematic overview of the end-to-end pipeline used in this work. Cluster and field samples (HeCS-omnibus, SPT, GOGREEN, and COSMOS) provide the input target regions. Legacy Survey photometry and COSMOS data are used to estimate stellar and galaxy SEDs with eazy-py, forming the spectral and morphological inputs for the SPHEREx image simulations. Using the SPHEREx Sky Simulator, high-resolution GalSim cutout… view at source ↗
Figure 4
Figure 4. Figure 4: Color–color diagram (g − z vs. z − W1) used for star–galaxy separation. Sources classified as galaxies are shown with color-coding according to their photometric red￾shifts, while sources classified as stars are marked as black points. Only the top 1% of objects with the highest signal– to-noise ratio in the Legacy Survey r-band flux are displayed. The effectiveness of this classification scheme is shown i… view at source ↗
Figure 5
Figure 5. Figure 5: Phase-space diagrams (∆v vs. projected radius) for galaxies with spectroscopic redshifts in the selected cluster fields. In each panel the bottom axis shows the projected radius normalized to the cluster’s virial radius, r/R200, and the corresponding physical scale in Mpc is shown on the top axis. Galaxies within ±2000 km s−1 of the cluster redshift, indicated by the horizontal dotted lines, are identified… view at source ↗
Figure 6
Figure 6. Figure 6: Illustration of the photometry pipeline for a single target galaxy. In the upper panels, the cross marks the target galaxy, while the dashed circle shows five times its half-light radius; only sources within this region are included in the image simulation. Starting from the left, the Legacy Survey image and the corresponding high-resolution input (five times finer than SPHEREx) are shown. This high-resolu… view at source ↗
Figure 7
Figure 7. Figure 7: Fractional flux residual, (f − finput)/finput, as a function of input magnitude, shown separately for the cluster and COSMOS field measurements. The input magnitude (minput) corresponds to the source brightness at the observed wavelength (per-pixel LVF bandpass for the Primary Catalog; per-channel bandpass for the Secondary Catalog). Top row: Primary Catalog (single-image photometry), for the cluster sampl… view at source ↗
Figure 10
Figure 10. Figure 10: Comparison between flux residuals and reported measurement errors for the Primary (top) and Secondary (bottom) catalogs. The horizontal axis shows the logarithm of the absolute flux residuals (in mJy), log |f − finput|, and the vertical axis shows the logarithm of the measurement er￾rors, log σf . The black dashed line indicates the one-to-one relation, along which the residuals would align with the er￾ro… view at source ↗
Figure 9
Figure 9. Figure 9: Bias comparison between extended and point sources in terms of the normalized flux residuals, (f − finput)/σf , where σf is the photometric uncertainty es￾timated from The Tractor. The top panels show extended sources, while the bottom panels correspond to point sources. The left column presents results from the Primary Catalog, and the right column shows those from the Secondary Cata￾log. The black points… view at source ↗
Figure 11
Figure 11. Figure 11: Distributions of the three crowding metrics for the cluster and COSMOS samples (Primary Catalog, sources with Nneigh ≥ 1). Columns, from left to right: flux-weighted normalized neighbor distance, log⟨dneigh/(re + re,neigh)⟩f ; total neighbor-to-target flux ratio, log Σfneigh/f; number of neighbors, Nneigh. Top row: raw histograms on a logarithmic counts scale. Bottom row: cumulative distribution functions… view at source ↗
Figure 12
Figure 12. Figure 12: The fractional flux residual as a function of three blending-related parameters for the Primary Catalog (top row) and Secondary Catalog (bottom row). The panels show the bias and scatter, with respect to the normalized neighbor distance (left), the total neighbor-to-target flux ratio (middle), and the number of neighbors (right). For the Secondary Catalog, the blending parameters on the horizontal axes ar… view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of photometric bias of the frac￾tional flux residual, for blended and isolated (single) sources. The blended case includes all neighboring galaxies within 5re in the image simulation, while the single case uses only the target galaxy, removing blending effects. Each point shows the mean flux bias, with error bars indicating the uncer￾tainty on the mean. Shaded regions represent the 1σ scatter o… view at source ↗
Figure 15
Figure 15. Figure 15: Blending fraction as a function of normal￾ized cluster-centric radius (rcl/R200), measured from the Sec￾ondary Catalog. The blending fraction is defined as the proportion of sources for which the mean neighbor-to-tar￾get flux ratio across the 102 channels, ⟨ Pfneigh/f⟩, exceeds unity. The top panel shows this fraction for all sources within the cluster fields, while the bottom panel shows the same relatio… view at source ↗
Figure 16
Figure 16. Figure 16: 5σ depth as a function of source size, normalized by the PSF FWHM, and S´ersic index. Each panel shows results for different detector or channel subsets: (left to right) Detector 1 (Primary Catalog; 0.75–1.09 µm), Detector 6 (Primary Catalog; 4.42–5.00 µm), Secondary Catalog channels 1–17 (0.75–1.12 µm), and channels 85–102 (4.37–5.01 µm). Depth is largely insensitive to S´ersic index, but becomes shallow… view at source ↗
Figure 17
Figure 17. Figure 17: Detection limit of member galaxies in the Sec￾ondary Catalog as a function of wavelength, across the 102 channels. Upper panel: the magnitude difference ∆mBCG between the BCG and the faintest member galaxy detected with S/N > 5 in each channel; solid lines with square mark￾ers show the main blended-field simulation and dotted lines the single-source counterpart (Section 5.4). Lower panel: the observed BCG… view at source ↗
Figure 18
Figure 18. Figure 18: Photometric redshift performance for individual cluster fields and the COSMOS field. Each block displays five fields, with the top left row showing results for all fields combined. Subsequent rows corresponds to individual cluster fields and the COSMOS field (right bottom). Within each block, the three panels compare zinput and zphot for different galaxy selections: all galaxies (left), galaxies with synt… view at source ↗
Figure 19
Figure 19. Figure 19: Scatter in member-galaxy photometric redshift with respect to Legacy Survey z-band magnitude, with cluster redshift color-coded (left) and σNMAD shown for binned members (right) [PITH_FULL_IMAGE:figures/full_fig_p024_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Apparent-magnitude coverage of the spectroscopic member sample. For each cluster field, and stacked over all eight clusters (bottom right), the gray filled histogram shows the LS DR10 z-band magnitude distribution of all galaxies in the field. The blue and red histograms show the subsets with an available spectroscopic redshift and with confirmed cluster membership (|c ∆z| < 2000 km s−1 ), respectively. C… view at source ↗
Figure 21
Figure 21. Figure 21: Cluster redshift estimation from the combined biweight location of the member photometric redshift estimates. Upper panels: recovered cluster redshift zcl,phot versus true redshift zcl,true. Lower panels: normalized residual ∆z/(1+z), where the green shaded band indicates the |∆z|/(1 + z) < 0.003 bias floor required for cluster cosmology (M. Lima & W. Hu 2007); the requirement spans 0.001–0.005 depending … view at source ↗
Figure 22
Figure 22. Figure 22: Comparison of photometric redshift perfor￾mance for member galaxies as a function of Legacy Survey z-band magnitude. The top panel shows the photometric redshift scatter, quantified by the normalized median abso￾lute deviation (σNMAD), while the bottom panel shows the 3ˆσ outlier fraction (η3ˆσ). Results are shown for the Blended (black) and Single (blue) simulations. The photometric red￾shift scatter and… view at source ↗
Figure 24
Figure 24. Figure 24: Flux and photometric redshift performance as a function of cluster redshift for the Baseline (gray), Con￾fusion-only (green), and Full (blue) simulations. All met￾rics are computed for galaxies with Ks < 19. From top to bottom: the flux-residual scatter σNMAD(∆f /finput), the photometric redshift scatter σNMAD(zphot), and the outlier fractions η0.15 and η3ˆσ. The Confusion-only results closely track the B… view at source ↗
Figure 25
Figure 25. Figure 25: Photometric redshift performance as a function of Legacy Survey z-band magnitude for galaxies in the cluster fields, separated by input SED type: all galaxies (black), pas￾sive (red; E, S0, and Sa templates), and non-passive (blue). Upper panel: photometric redshift scatter σNMAD. Lower panel: 3ˆσ catastrophic outlier fraction η3ˆσ. Passive galaxies show substantially lower outlier rates at bright magnitu… view at source ↗
Figure 26
Figure 26. Figure 26: Input and best-fit SEDs for a passive galaxy and two star-forming galaxies of comparable brightness and spec￾tral coverage (mz, LS ≈ 19.8–20.0, Nch = 56–58 channels at S/N > 5). Each panel gives the input SED (blue), the Sec￾ondary Catalog spectrophotometry (black points), and the best-fit template at the photometric redshift (red dashed). The passive galaxy (top) is recovered in both template and redshif… view at source ↗
read the original abstract

We present a simulation-driven assessment of the performance of the SPHEREx pipeline for galaxy cluster science, focusing on photometry, source blending, survey depth, and photometric redshift accuracy. To do that, we compile a sample of eight galaxy clusters spanning a wide redshift range ($z \approx 0.02$-$1.1$) and develop an end-to-end pipeline. We use the ancillary data from the DESI Legacy Survey and COSMOS survey, and generate realistic mock SPHEREx observations with the SPHEREx Sky Simulator. By performing forced photometry on these images with The Tractor, we quantify the characteristic biases and uncertainties relevant to cluster science. We find that the photometry is generally unbiased, but source blending is the primary driver of catastrophic outliers, particularly when the combined flux of neighbors is comparable to the flux of targets. Measuring the effective survey depth, we find that SPHEREx detects members down to $K_{s}\approx 20$ AB ($5\sigma$), 7-9 mag fainter than the brightest cluster galaxy (BCG) in nearby clusters but only 1-2 mag for clusters at $z \sim 1$, where the BCG itself has faded close to this depth. Despite these challenges, we demonstrate that SPHEREx can achieve a photometric redshift precision of $\sigma_{\mathrm{NMAD}}\approx 0.003$-$0.01$ for cluster galaxies with an appropriate sample selection based on brightness or signal-to-noise. Combining the redshifts of quality-selected members, we recover cluster redshifts with a bias of $|\Delta z|/(1+z) < 0.002$ and a scatter of $\sigma \approx 0.002$ at $z \lesssim 0.5$, meeting the precision required for cluster cosmology.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper presents a simulation-driven assessment of the SPHEREx forced photometry pipeline for galaxy cluster science. Using mocks generated from DESI Legacy Survey and COSMOS data with the SPHEREx Sky Simulator for eight clusters at z ≈ 0.02-1.1, it performs forced photometry with The Tractor and evaluates biases, blending, depth, and photo-z accuracy. The key findings are that photometry is generally unbiased, blending drives catastrophic outliers when neighbor flux is comparable, SPHEREx reaches Ks≈20 AB depth, and with selection, achieves σ_NMAD≈0.003-0.01 for galaxy photo-z and cluster redshift recovery with |Δz|/(1+z) < 0.002 bias and σ ≈ 0.002 scatter at z ≲ 0.5.

Significance. If the simulation results hold, this provides a useful benchmark for SPHEREx cluster studies by identifying blending as the dominant error source and showing that the photometric precision needed for cluster cosmology can be reached at low redshift in the mocks. The end-to-end pipeline on realistic mocks is a strength.

major comments (1)
  1. [Abstract] Abstract: the headline performance metrics (σ_NMAD≈0.003-0.01 for galaxies; cluster redshift bias |Δz|/(1+z)<0.002 and scatter σ≈0.002 at z≲0.5) are obtained exclusively from forced photometry on SPHEREx Sky Simulator mocks; the manuscript provides no quantitative validation of the simulator's noise, PSF wings, or close-pair blending statistics against real data in dense fields, which is load-bearing for translating the results to flight data.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review of our manuscript. We address the single major comment below and agree that additional caveats are warranted to properly contextualize the simulation results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline performance metrics (σ_NMAD≈0.003-0.01 for galaxies; cluster redshift bias |Δz|/(1+z)<0.002 and scatter σ≈0.002 at z≲0.5) are obtained exclusively from forced photometry on SPHEREx Sky Simulator mocks; the manuscript provides no quantitative validation of the simulator's noise, PSF wings, or close-pair blending statistics against real data in dense fields, which is load-bearing for translating the results to flight data.

    Authors: We agree that the manuscript does not include a quantitative validation of the SPHEREx Sky Simulator's noise properties, PSF wings, or close-pair blending statistics against real data in dense fields. The simulator is constructed from empirical inputs drawn from the DESI Legacy Survey and COSMOS catalogs, but this work does not perform a direct statistical comparison to observed dense-field data. Because the headline metrics are derived exclusively from these mocks, we will revise the abstract to include an explicit statement that the reported performance is obtained from simulated observations and will add a paragraph in the discussion section describing the simulator's assumptions and the need for future validation once flight data become available. These changes will better frame the applicability of the results to actual SPHEREx cluster studies. revision: yes

Circularity Check

0 steps flagged

No circularity: direct empirical comparison of pipeline outputs to known mock inputs

full rationale

The paper is a simulation-based validation study. It generates mock SPHEREx images from known ancillary catalogs (DESI Legacy Survey, COSMOS), runs forced photometry with The Tractor, and reports performance metrics by direct comparison to the input truth values. No derivations, parameter fits presented as predictions, or self-referential steps exist. The central claims (photo-z precision, cluster redshift recovery) are measured quantities from the mocks, not quantities that reduce to the inputs by construction. The simulator fidelity is an external modeling assumption, not a circularity issue.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the work rests on standard assumptions about the fidelity of astronomical image simulators and the correctness of forced-photometry algorithms.

pith-pipeline@v0.9.1-grok · 5936 in / 1226 out tokens · 41863 ms · 2026-06-26T17:02:06.532204+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

123 extracted references · 120 canonical work pages · 9 internal anchors

  1. [1]

    Blended”), while the lower row shows the control run where the source was simulated in isolation (“Single

    Abbott, T. M. C., Aguena, M., Alarcon, A., et al. 2020, PhRvD, 102, 023509, doi: 10.1103/PhysRevD.102.023509 Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35, doi: 10.3847/1538-4365/ac4414 42Bahk et al. LS DR10 Image High-Res Input SPHEREx-Res Input SPHEREx Mock Tractor Model Residual = 194.78 = 28.53 = 2.52 m 0.7 1 2 3 4 5 Wavelength [ m] 0...

    work page doi:10.1103/physrevd.102.023509 2020

  2. [2]

    2018, PhRvD, 98, 123529, doi: 10.1103/PhysRevD.98.123529

    Aguena, M., & Lima, M. 2018, PhRvD, 98, 123529, doi: 10.1103/PhysRevD.98.123529

    work page doi:10.1103/physrevd.98.123529 2018

  3. [3]

    2026, The Open Journal of Astrophysics, 9, 55863, doi: 10.33232/001c.155863

    Aguena, M., Aiola, S., Allam, S., et al. 2026, The Open Journal of Astrophysics, 9, 55863, doi: 10.33232/001c.155863

    work page doi:10.33232/001c.155863 2026

  4. [4]

    P., Crill, B

    Akeson, R., Dubois-Felsmann, G. P., Crill, B. P., et al. 2025, arXiv e-prints, arXiv:2511.15823, doi: 10.48550/arXiv.2511.15823

    work page doi:10.48550/arxiv.2511.15823 2025

  5. [5]

    Philosophical Transactions of the Royal Society of London Series A , keywords =

    Allard, F., Homeier, D., & Freytag, B. 2012, Philosophical Transactions of the Royal Society of London Series A, 370, 2765, doi: 10.1098/rsta.2011.0269

    work page doi:10.1098/rsta.2011.0269 2012

  6. [6]

    W., Evrard, A

    Allen, S. W., Evrard, A. E., & Mantz, A. B. 2011, ARA&A, 49, 409, doi: 10.1146/annurev-astro-081710-102514

    work page doi:10.1146/annurev-astro-081710-102514 2011

  7. [7]

    2025, https://arxiv.org/abs/2512.24537

    Bae, J., Lee, B., Im, M., et al. 2025, https://arxiv.org/abs/2512.24537

    arXiv 2025

  8. [8]

    Bahk, H., & Hwang, H. S. 2024, ApJS, 272, 7, doi: 10.3847/1538-4365/ad323f

    work page doi:10.3847/1538-4365/ad323f 2024

  9. [9]

    L., van der Burg, R

    Balogh, M. L., van der Burg, R. F. J., Muzzin, A., et al. 2021, MNRAS, 500, 358, doi: 10.1093/mnras/staa3008

    work page doi:10.1093/mnras/staa3008 2021

  10. [10]

    B., Zengo, K., Ruel, J., et al

    Bayliss, M. B., Zengo, K., Ruel, J., et al. 2017, ApJ, 837, 88, doi: 10.3847/1538-4357/aa607c

    work page doi:10.3847/1538-4357/aa607c 2017

  11. [11]

    C., Flynn, K., & Gebhardt, K

    Beers, T. C., Flynn, K., & Gebhardt, K. 1990, AJ, 100, 32, doi: 10.1086/115487

    work page doi:10.1086/115487 1990

  12. [12]

    A., Frieman, J., Weinberg, D

    Berlind, A. A., Frieman, J., Weinberg, D. H., et al. 2006, ApJS, 167, 1, doi: 10.1086/508170

    work page doi:10.1086/508170 2006

  13. [13]

    2014, MNRAS, 443, 874, doi: 10.1093/mnras/stu1106

    Bernardi, M., Meert, A., Vikram, V., et al. 2014, MNRAS, 443, 874, doi: 10.1093/mnras/stu1106

    work page doi:10.1093/mnras/stu1106 2014

  14. [14]

    E., Stalder, B., de Haan, T., et al

    Bleem, L. E., Stalder, B., de Haan, T., et al. 2015, ApJS, 216, 27, doi: 10.1088/0067-0049/216/2/27

    work page doi:10.1088/0067-0049/216/2/27 2015

  15. [15]

    E., Klein, M., Abbot, T

    Bleem, L. E., Klein, M., Abbot, T. M. C., et al. 2024, The Open Journal of Astrophysics, 7, 13, doi: 10.21105/astro.2311.07512

    work page doi:10.21105/astro.2311.07512 2024

  16. [16]

    J., Aboobaker, A

    Bock, J. J., Aboobaker, A. M., Adamo, J., et al. 2025, arXiv e-prints, arXiv:2511.02985, doi: 10.48550/arXiv.2511.02985

    work page doi:10.48550/arxiv.2511.02985 2025

  17. [17]

    E., et al

    Bocquet, S., Grandis, S., Bleem, L. E., et al. 2024, PhRvD, 110, 083510, doi: 10.1103/PhysRevD.110.083510

    work page doi:10.1103/physrevd.110.083510 2024

  18. [18]

    2021, eazy-py, 0.5.2 doi: 10.5281/zenodo.5012704

    Brammer, G. 2021, eazy-py, 0.5.2 doi: 10.5281/zenodo.5012704

    work page doi:10.5281/zenodo.5012704 2021

  19. [19]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503, doi: 10.1086/591786

    work page internal anchor Pith review doi:10.1086/591786 2008

  20. [20]

    Brown, M. J. I., Moustakas, J., Smith, J. D. T., et al. 2014, ApJS, 212, 18, doi: 10.1088/0067-0049/212/2/18

    work page doi:10.1088/0067-0049/212/2/18 2014

  21. [21]

    2025, arXiv e-prints, arXiv:2508.20332, doi: 10.48550/arXiv.2508.20332

    Bryan, S., Bock, J., Burk, T., et al. 2025, arXiv e-prints, arXiv:2508.20332, doi: 10.48550/arXiv.2508.20332

    work page doi:10.48550/arxiv.2508.20332 2025

  22. [22]

    2024, A&A, 685, A106, doi: 10.1051/0004-6361/202348264

    Bulbul, E., Liu, A., Kluge, M., et al. 2024, A&A, 685, A106, doi: 10.1051/0004-6361/202348264

    work page doi:10.1051/0004-6361/202348264 2024

  23. [23]

    , keywords =

    Casey, C. M., Kartaltepe, J. S., Drakos, N. E., et al. 2023, ApJ, 954, 31, doi: 10.3847/1538-4357/acc2bc

    work page doi:10.3847/1538-4357/acc2bc 2023

  24. [24]

    The Pan-STARRS1 Surveys

    Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints, arXiv:1612.05560, doi: 10.48550/arXiv.1612.05560

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1612.05560 2016

  25. [25]

    2023, ApJ, 958, 118, doi: 10.3847/1538-4357/acf4a1

    Chiang, Y.-K. 2023, ApJ, 958, 118, doi: 10.3847/1538-4357/acf4a1

    work page doi:10.3847/1538-4357/acf4a1 2023

  26. [26]

    2024, A&A, 687, A238, doi: 10.1051/0004-6361/202449447

    Clerc, N., Comparat, J., Seppi, R., et al. 2024, A&A, 687, A238, doi: 10.1051/0004-6361/202449447

    work page doi:10.1051/0004-6361/202449447 2024

  27. [27]

    Condon, J. J. 1974, ApJ, 188, 279, doi: 10.1086/152714

    work page doi:10.1086/152714 1974

  28. [28]

    J., Csabai, I., Szalay, A

    Connolly, A. J., Csabai, I., Szalay, A. S., et al. 1995, AJ, 110, 2655, doi: 10.1086/117720

    work page doi:10.1086/117720 1995

  29. [29]

    P., Werner, M., Akeson, R., et al

    Crill, B. P., Werner, M., Akeson, R., et al. 2020, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 11443, Space Telescopes and Instrumentation 2020: Optical, Infrared, and Millimeter Wave, ed. M. Lystrup & M. D. Perrin, 114430I, doi: 10.1117/12.2567224

    work page doi:10.1117/12.2567224 2020

  30. [30]

    P., Bach, Y

    Crill, B. P., Bach, Y. P., Bryan, S. A., et al. 2025, arXiv e-prints, arXiv:2505.24856, doi: 10.48550/arXiv.2505.24856

    work page doi:10.48550/arxiv.2505.24856 2025

  31. [31]

    The DESI Experiment Part I: Science,Targeting, and Survey Design

    Dachan, K., Song, H., Kim, Y., et al. 2024, Journal of Korean Astronomical Society, 57, 45, doi: 10.5303/JKAS.2024.57.1.45 DESI Collaboration, Aghamousa, A., Aguilar, J., et al. 2016, arXiv e-prints, arXiv:1611.00036, doi: 10.48550/arXiv.1611.00036

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.5303/jkas.2024.57.1.45 2024

  32. [32]

    Cosmology with the SPHEREX All-Sky Spectral Survey

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d Dor´ e, O., Bock, J., Ashby, M., et al. 2014, arXiv e-prints, arXiv:1412.4872, doi: 10.48550/arXiv.1412.4872 Dor´ e, O., Werner, M. W., Ashby, M., et al. 2016, arXiv e-prints, arXiv:1606.07039, doi: 10.48550/arXiv.1606.07039 Dor´ e, O., Werner, M. W., Ashby, M. L....

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.3847/1538-3881/ab089d 2019

  33. [33]

    , keywords =

    Dressler, A. 1980, ApJ, 236, 351, doi: 10.1086/157753

    work page doi:10.1086/157753 1980

  34. [34]

    R., Schaye, J., Kay, S

    Duffy, A. R., Schaye, J., Kay, S. T., & Dalla Vecchia, C. 2008, MNRAS, 390, L64, doi: 10.1111/j.1745-3933.2008.00537.x

    work page doi:10.1111/j.1745-3933.2008.00537.x 2008

  35. [35]

    , keywords =

    Ebeling, H., Edge, A. C., Bohringer, H., et al. 1998, MNRAS, 301, 881, doi: 10.1046/j.1365-8711.1998.01949.x

    work page doi:10.1046/j.1365-8711.1998.01949.x 1998

  36. [36]

    H., Johnston, S., & Das, P

    Eke, V. R., Baugh, C. M., Cole, S., et al. 2004, MNRAS, 348, 866, doi: 10.1111/j.1365-2966.2004.07408.x

    work page doi:10.1111/j.1365-2966.2004.07408.x 2004

  37. [37]

    M., Parker, L., & Seljak, U

    Feder, R. M., Parker, L., & Seljak, U. 2026, arXiv e-prints, arXiv:2603.24668, doi: 10.48550/arXiv.2603.24668

    work page doi:10.48550/arxiv.2603.24668 2026

  38. [38]

    M., Masters, D

    Feder, R. M., Masters, D. C., Lee, B., et al. 2024, ApJ, 972, 68, doi: 10.3847/1538-4357/ad596d

    work page doi:10.3847/1538-4357/ad596d 2024

  39. [39]

    2026, A&A, 709, A102, doi: 10.1051/0004-6361/202557708 44Bahk et al

    Fumagalli, A., Costanzi, M., Castro, T., et al. 2026, A&A, 709, A102, doi: 10.1051/0004-6361/202557708 44Bahk et al. Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1, doi: 10.1051/0004-6361/201629272

    work page doi:10.1051/0004-6361/202557708 2026

  40. [40]

    2009, A&A, 498, L33, doi: 10.1051/0004-6361/200911841

    Gavazzi, R., Adami, C., Durret, F., et al. 2009, A&A, 498, L33, doi: 10.1051/0004-6361/200911841

    work page doi:10.1051/0004-6361/200911841 2009

  41. [41]

    2024, A&A, 689, A298, doi: 10.1051/0004-6361/202348852

    Ghirardini, V., Bulbul, E., Artis, E., et al. 2024, A&A, 689, A298, doi: 10.1051/0004-6361/202348852

    work page doi:10.1051/0004-6361/202348852 2024

  42. [42]

    D., & Yee, H

    Gladders, M. D., & Yee, H. K. C. 2000, AJ, 120, 2148, doi: 10.1086/301557

    work page doi:10.1086/301557 2000

  43. [43]

    , year = 1972, month = aug, volume =

    Gunn, J. E., & Gott, III, J. R. 1972, ApJ, 176, 1, doi: 10.1086/151605

    work page doi:10.1086/151605 1972

  44. [44]

    P., Pereira, M

    Haines, C. P., Pereira, M. J., Smith, G. P., et al. 2013, ApJ, 775, 126, doi: 10.1088/0004-637X/775/2/126

    work page doi:10.1088/0004-637x/775/2/126 2013

  45. [45]

    2021, ApJS, 253, 3, doi: 10.3847/1538-4365/abd023

    Hilton, M., Sif´ on, C., Naess, S., et al. 2021, ApJS, 253, 3, doi: 10.3847/1538-4365/abd023

    work page doi:10.3847/1538-4365/abd023 2021

  46. [46]

    M., et al

    Ho, M., Ntampaka, M., Rau, M. M., et al. 2022, Nature Astronomy, 6, 936, doi: 10.1038/s41550-022-01711-1

    work page doi:10.1038/s41550-022-01711-1 2022

  47. [47]

    W., & Lang, D

    Hogg, D. W., & Lang, D. 2013, PASP, 125, 719, doi: 10.1086/671228

    work page doi:10.1086/671228 2013

  48. [48]

    J., Cheng, Y.-T., et al

    Huai, Z., Bock, J. J., Cheng, Y.-T., et al. 2026, ApJ, 1000, 56, doi: 10.3847/1538-4357/ae472b

    work page doi:10.3847/1538-4357/ae472b 2026

  49. [49]

    P., & Geller, M

    Huchra, J. P., & Geller, M. J. 1982, ApJ, 257, 423, doi: 10.1086/160000

    work page doi:10.1086/160000 1982

  50. [50]

    M., & Broderick, T

    Huterer, D., Kim, A., Krauss, L. M., & Broderick, T. 2004, ApJ, 615, 595, doi: 10.1086/424726

    work page doi:10.1086/424726 2004

  51. [51]

    Huterer, D., & Shafer, D. L. 2018, Reports on Progress in Physics, 81, 016901, doi: 10.1088/1361-6633/aa997e

    work page doi:10.1088/1361-6633/aa997e 2018

  52. [52]

    S., Elbaz, D., Lee, J

    Hwang, H. S., Elbaz, D., Lee, J. C., et al. 2010, A&A, 522, A33, doi: 10.1051/0004-6361/201014807

    work page doi:10.1051/0004-6361/201014807 2010

  53. [53]

    Zahid, H. J. 2014, ApJ, 797, 106, doi: 10.1088/0004-637X/797/2/106

    work page doi:10.1088/0004-637x/797/2/106 2014

  54. [54]

    J., Cha, S., & Cho, H

    HyeongHan, K., Jee, M. J., Cha, S., & Cho, H. 2024, Nature Astronomy, 8, 377, doi: 10.1038/s41550-023-02164-w

    work page doi:10.1038/s41550-023-02164-w 2024

  55. [55]

    J., et al

    Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841, doi: 10.1051/0004-6361:20065138

    work page internal anchor Pith review doi:10.1051/0004-6361:20065138 2006

  56. [56]

    2009, ApJ, 690, 1236, doi: 10.1088/0004-637X/690/2/1236 Ivezi´ c,ˇZ., Kahn, S

    Ilbert, O., Capak, P., Salvato, M., et al. 2009, ApJ, 690, 1236, doi: 10.1088/0004-637X/690/2/1236 Ivezi´ c,ˇZ., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c

    work page doi:10.1088/0004-637x/690/2/1236 2009

  57. [57]

    S., Okabe, N., & Park, C

    Kang, W., Hwang, H. S., Okabe, N., & Park, C. 2025, ApJS, 278, 51, doi: 10.3847/1538-4365/adcac8

    work page doi:10.3847/1538-4365/adcac8 2025

  58. [58]

    2012, MNRAS, 427, 1344, doi: 10.1111/j.1365-2966.2012.22008.x

    Kelvin, L. S., Driver, S. P., Robotham, A. S. G., et al. 2012, MNRAS, 421, 1007, doi: 10.1111/j.1365-2966.2012.20355.x

    work page doi:10.1111/j.1365-2966.2012.20355.x 2012

  59. [59]

    A., Kartaltepe, J

    Khostovan, A. A., Kartaltepe, J. S., Salvato, M., et al. 2025, arXiv e-prints, arXiv:2503.00120, doi: 10.48550/arXiv.2503.00120

    work page doi:10.48550/arxiv.2503.00120 2025

  60. [60]

    H., Im, M., Lee, H., et al

    Kim, J. H., Im, M., Lee, H., et al. 2024, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 13094, Ground-based and Airborne Telescopes X, ed. H. K. Marshall, J. Spyromilio, & T. Usuda, 130940X, doi: 10.1117/12.3019546

    work page doi:10.1117/12.3019546 2024

  61. [61]

    S., et al

    Kim, T., Sohn, J., Hwang, H. S., et al. 2025, ApJS, 277, 41, doi: 10.3847/1538-4365/adb42a

    work page doi:10.3847/1538-4365/adb42a 2025

  62. [62]

    , keywords =

    Kitching, T. D., Miller, L., Heymans, C. E., van Waerbeke, L., & Heavens, A. F. 2008, MNRAS, 390, 149, doi: 10.1111/j.1365-2966.2008.13628.x

    work page doi:10.1111/j.1365-2966.2008.13628.x 2008

  63. [63]

    J., Desai, S., et al

    Klein, M., Mohr, J. J., Desai, S., et al. 2018, MNRAS, 474, 3324, doi: 10.1093/mnras/stx2929

    work page doi:10.1093/mnras/stx2929 2018

  64. [64]

    2020, ApJS, 247, 43, doi: 10.3847/1538-4365/ab733b

    Kluge, M., Neureiter, B., Riffeser, A., et al. 2020, ApJS, 247, 43, doi: 10.3847/1538-4365/ab733b

    work page doi:10.3847/1538-4365/ab733b 2020

  65. [65]

    2024, A&A, 688, A210, doi: 10.1051/0004-6361/202349031

    Kluge, M., Comparat, J., Liu, A., et al. 2024, A&A, 688, A210, doi: 10.1051/0004-6361/202349031

    work page doi:10.1051/0004-6361/202349031 2024

  66. [66]

    P., McKay, T

    Koester, B. P., McKay, T. A., Annis, J., et al. 2007, ApJ, 660, 221, doi: 10.1086/512092

    work page doi:10.1086/512092 2007

  67. [67]

    M., Bock, J

    Korngut, P. M., Bock, J. J., Akeson, R., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10698, Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave, ed. M. Lystrup, H. A. MacEwen, G. G

    2018

  68. [68]

    Batalha, N

    Fazio, N. Batalha, N. Siegler, & E. C. Tong, 106981U, doi: 10.1117/12.2312860

    work page doi:10.1117/12.2312860

  69. [69]

    E., Rykoff, E

    Kornoelje, K., Bleem, L. E., Rykoff, E. S., et al. 2025, arXiv e-prints, arXiv:2503.17271, doi: 10.48550/arXiv.2503.17271

    work page doi:10.48550/arxiv.2503.17271 2025

  70. [70]

    V., & Borgani, S

    Kravtsov, A. V., & Borgani, S. 2012, ARA&A, 50, 353, doi: 10.1146/annurev-astro-081811-125502

    work page doi:10.1146/annurev-astro-081811-125502 2012

  71. [71]

    M., Stebbins, A., Annis, J., et al

    Kubo, J. M., Stebbins, A., Annis, J., et al. 2007, ApJ, 671, 1466, doi: 10.1086/523101 La Barbera, F., de Carvalho, R. R., de La Rosa, I. G., et al. 2010, MNRAS, 408, 1313, doi: 10.1111/j.1365-2966.2010.16850.x

    work page doi:10.1086/523101 2007

  72. [72]

    2020, arXiv e-prints, arXiv:2012.15797, doi: 10.48550/arXiv.2012.15797

    Lang, D. 2020, arXiv e-prints, arXiv:2012.15797, doi: 10.48550/arXiv.2012.15797

    work page doi:10.48550/arxiv.2012.15797 2020

  73. [73]

    W., & Schlegel, D

    Lang, D., Hogg, D. W., & Schlegel, D. J. 2016b, AJ, 151, 36, doi: 10.3847/0004-6256/151/2/36

    work page doi:10.3847/0004-6256/151/2/36

  74. [74]

    Lauer, T. R. 1999, PASP, 111, 1434, doi: 10.1086/316460

    work page doi:10.1086/316460 1999

  75. [75]

    Euclid Definition Study Report

    Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints, arXiv:1110.3193, doi: 10.48550/arXiv.1110.3193

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1110.3193 2011

  76. [76]

    H., Lee, M

    Lee, J. H., Lee, M. G., Mun, J. Y., Cho, B. S., & Kang, J. 2022, ApJL, 931, L22, doi: 10.3847/2041-8213/ac6e39

    work page doi:10.3847/2041-8213/ac6e39 2022

  77. [77]

    H., Kim, M., Kim, T., et al

    Lee, J. H., Kim, M., Kim, T., et al. 2025, AJ, 169, 185, doi: 10.3847/1538-3881/adb285 SPHEREx View of Galaxy Clusters45

    work page doi:10.3847/1538-3881/adb285 2025

  78. [78]

    LSST Science Book, Version 2.0

    Lima, M., & Hu, W. 2007, PhRvD, 76, 123013, doi: 10.1103/PhysRevD.76.123013 LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, arXiv e-prints, arXiv:0912.0201, doi: 10.48550/arXiv.0912.0201

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.76.123013 2007

  79. [79]

    2011, ApJ, 731, 53, doi: 10.1088/0004-637X/731/1/53

    Mainzer, A., Bauer, J., Grav, T., et al. 2011, ApJ, 731, 53, doi: 10.1088/0004-637X/731/1/53

    work page doi:10.1088/0004-637x/731/1/53 2011

  80. [80]

    Marocco, F., Eisenhardt, P. R. M., Fowler, J. W., et al. 2021, ApJS, 253, 8, doi: 10.3847/1538-4365/abd805

    work page doi:10.3847/1538-4365/abd805 2021

Showing first 80 references.