Pith. sign in

REVIEW 3 major objections 6 minor 38 references

Integrated Galaxy Light from Stacking $10^5$ Random Pointings in the Dark Energy Survey Data

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Galaxy background light measured by stacking 100,000 sky patches

desk verdict The stacking idea is interesting, but the R-dependent curve they fit is largely a normalization artefact, so the reported IGL values are probably biased high. read the letter →

arxiv 2506.08162 v1 pith:CSV2ZSTK submitted 2025-06-09 astro-ph.CO

Jenna E. Moore , Seth H. Cohen , Philip Mauskopf , Evan Scannapieco This is my paper
classification astro-ph.CO
keywords integratedgalaxylightextragalacticbackgroundimagestackingsourcemaskingDarkEnergySurveycosmicopticalcounts
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims a new way to measure the integrated galaxy light (IGL), the sum of all light emitted by resolved galaxies, which dominates the cosmic optical background. By stacking roughly 100,000 random one-arcminute cutouts from the Dark Energy Survey and subtracting images in which detected galaxies have been masked from images in which they have not, the method isolates galaxy light while automatically canceling zodiacal light, diffuse galactic light, and atmospheric foregrounds shared by both stacks. The resulting IGL values in five bands agree with previous estimates from galaxy number counts and gamma-ray observations. If this holds, the diffuse component of the optical background is small and the IGL can be measured from the ground over large sky areas without foreground modeling.

What carries the argument

The central mechanism is the stacked-image difference: the weighted average of images with stars masked minus the weighted average of images with both stars and galaxies masked, computed over roughly 100,000 cutouts per band. Because every stack is the same set of images with different pixels masked, foregrounds, sky background, and instrumental noise cancel in the difference. Source extraction and classification into stars, galaxies, and unclassified objects are done with SourceExtractor on co-added detection images, and circular masks with six dilation radii (5 to 15 pixels) generate the family of masked stacks. Equation (8), a Sersic-inspired curve that asymptotes to $I_e$, is the object that converts finite-mask measurements into a total IGL estimate.

What would settle it

Run the same stacking and masking pipeline on a deep reference field such as the COSMOS or CANDELS area, where the true IGL is known from galaxy counts to fainter limits; if the fitted $I_e$ does not reproduce the reference value, the functional-form extrapolation is incorrect. For the $Y$-band specifically, extending the mask radius to 20-30 pixels with deeper co-adds would show whether the measured brightness continues climbing toward the fitted asymptote or has already converged.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the difference in brightness between a stack of random sky positions with stars masked and a stack with both stars and galaxies masked yields a clean measurement of galaxy light, $I_G(R)$, and that this quantity rises with mask radius toward an asymptotic value that represents the total integrated galaxy light. The authors fit the measured $I_G(R)$ at mask radii of 5 to 15 pixels with an empirically adopted Sersic-inspired function, $I_G(R) = I_e\left[1-\exp\left(-7.669\left[(R/R_e)^{1/n}-1\right]\right)\right]$, and take the fitted $I_e$ as the IGL. They report $g=4.27\pm0.28$, $r=6.97\pm0.42$, $i=8.66\pm0.53$, $z=10.16\pm0.7$, and $Y=13.78\pm2.35$ nW/m$^2$/sr. These measurements require no foreground estimation or removal, and they agree with IGL values derived from galaxy counts and gamma-ray absorption, with the $Y$-band value lying higher than previous estimates.

Load-bearing premise

The assumption that the adopted Sersic-inspired function correctly describes how the measured galaxy brightness grows with mask radius beyond the probed range of 5 to 15 pixels is what carries the extrapolation from finite masks to a total IGL value.

Editorial extensions

If this is right

  • The IGL can be measured from the ground without modeling zodiacal light, diffuse galactic light, or atmospheric emission, because these are common to all stacks and cancel in the difference.
  • The technique yields values consistent with galaxy-count and gamma-ray IGL estimates, supporting the conclusion that resolved galaxies make up essentially the entire cosmic optical background.
  • Because the method is insensitive to diffuse light by construction, it cleanly separates the source-attributable component of the background from any truly diffuse signal.
  • The same stacking pipeline can be applied to other wide-field surveys, including Sloan Digital Sky Survey, WISE, Pan-STARRS, and future Rubin, Euclid, and Roman data, to extend IGL measurements to other wavelengths and sky regions.
  • The derived star light and unclassified-source light fractions (roughly two-thirds and about one percent of total brightness) provide a consistency check on source classification.

Reading between the lines

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

  • Applied to wide-field archival data from other surveys, the same difference-of-stacks technique could produce IGL measurements over much of the sky with small cosmic variance, turning many ground-based datasets into IGL probes.
  • If the $Y$-band excess is real, it may indicate light in the outer envelopes of galaxies that shallower number-count surveys miss, which would deepen the tension with galaxy-count IGL at near-infrared wavelengths; if it is a fit artifact, it will vanish when deeper $Y$-band stacks or larger mask radii are used.
  • The negative background measured in the all-sources-masked stacks implies that the DES background is overestimated, and the method's ability to cancel this in the difference is a direct test of the claim that no foreground estimation is needed: any foreground that differs between the two stacks would leak into the IGL.
Share X Bluesky LinkedIn Reddit HN

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. Moore et al. present a stacking-based measurement of the integrated galaxy light (IGL). They extract roughly 98,713 random 1x1 arcminute cutouts from DES DR2 in g, r, i, z, and Y, identify sources with SExtractor, classify them as stars, galaxies, or 'other', and build circular masks of radius 5, 7, 9, 11, 13, and 15 pixels. They co-add four mask variants and define the galaxy brightness I_G(R) as the difference between the stars-masked and the stars-plus-galaxies-masked stacked images. They then fit an empirically adopted Sersic-inspired function, Eq. (8), to I_G(R) as a function of mask radius and extrapolate to infinite radius to obtain IGL values of g=4.27±0.28, r=6.97±0.42, i=8.66±0.53, z=10.16±0.70, and Y=13.78±2.35 nW/m2/sr. The paper's central claim is that these values require no foreground estimation or removal and agree with previous IGL estimates from galaxy number counts and gamma-ray observations.

Significance. If the method is valid, it is a valuable complement to number-count IGL measurements: it uses a very large number of random pointings, covers a wide area with low cosmic variance, and avoids explicit modeling of zodiacal light, diffuse Galactic light, and atmospheric emission by taking differences of stacks built from the same images. The paper also includes jackknife tests and compares with several independent data sets. However, the central numerical results rest entirely on interpreting I_G(R) as the cumulative galaxy light within a mask radius and on extrapolating an unvalidated functional form far beyond the measured range, particularly in the Y band. The paper does not provide image simulations or a forward model connecting the measured aperture differences to the galaxy light profile, and the adopted fitting function is internally inconsistent with the Sersic form it claims to be inspired by. The idea has merit, but the reported IGL values are not established by the analysis as presented.

major comments (3)
  1. [Sec. 4.3, Eq. (3), Table 1] The quantity I_G(R) defined by Eq. (3) is not the cumulative galaxy light within mask radius R, so fitting Eq. (8) to it does not directly measure an IGL. The stacked images are weighted averages over different sets of unmasked pixels: the stars-masked stack averages over N_1 pixels, while the stars-plus-galaxies-masked stack averages over N_1 - m(R) pixels, where m(R) is the area of the galaxy masks. For a single galaxy of total flux F and enclosed flux F(<R), the difference of the two aperture-averaged brightnesses is approximately [F(<R) - (m/N) F]/(N - m), not F(<R). The R-dependence of I_G therefore contains a mask-area normalization term in addition to the galaxy light profile. Table 1 shows that the 'Total Brightness' column, which is derived from the same unmasked stack, increases from 17.92 to 18.18 nW/m2/sr as R goes from 5 to 15 pixels, demonstrating that the mean-brightness normalization in the pipeline is R-dependent. The authors do not derive the relation between Eq. (3) and the true cumulative light profile. Until this is done, or the pipeline is validated with image simulations, the fitted I_e values in Table 2 cannot be identified with the integrated galaxy light.
  2. [Sec. 4.3, Eq. (8), Fig. 7, Table 2] Equation (8) is introduced as 'inspired by the Sersic profile' but it is not a Sersic cumulative profile: the constant 7.669 is the Sersic b_n appropriate for n=4, while n is a free parameter fitted to values between 14.8 and 77.6. More importantly, no derivation, no comparison to alternative profile families, and no systematic error for the adopted functional form are provided. The extrapolation is particularly severe in the Y band, where the measured points end at 9.35 nW/m2/sr at R=15 and the fitted I_e is 13.78 nW/m2/sr, implying that about 32% of the signal lies beyond the largest measured mask radius, and the fitted n=77.6±31.7 is essentially unconstrained. The E_fit error in Table 3 only reflects the statistical uncertainty of the fit to the chosen function. A mock-injection test, or at least a battery of alternative profile families, is needed to establish that the extrapolated I_e values are robust and unbiased.
  3. [Secs. 3.2 and 4.1, Eq. (3)] Equation (3) assumes that the only difference between the stars-masked and stars-plus-galaxies-masked stacks is galaxy light. In practice, galaxy masks are circular apertures that can overlap stars and 'other' sources; any star or other-source light inside a galaxy mask is removed in the second stack but not in the first, so it is attributed to I_G. Given that the stars column in Table 1 is roughly two-thirds of the total brightness, even a small overlap fraction could bias the galaxy signal. The authors should quantify the overlap of the three mask sets and demonstrate that its contribution to I_G is negligible, or construct disjoint masks before taking the difference.
minor comments (6)
  1. [Abstract and Sec. 5] The abstract states that all measurements are in agreement with previous IGL values, while Section 5 says the Y-band value is significantly higher than previously reported values; please reconcile this inconsistency.
  2. [Fig. 2] The axis tick labels appear to be missing minus signs on the negative values; the current labels are visually confusing.
  3. [Sec. 3.3] The text says 'all of the stacked images in which all detected sources are masked have all negative pixel values,' but Figure 5 shows distributions with negative means rather than all pixels negative; please rephrase.
  4. [Eq. (5)] The surface-brightness conversion is written with 'counts/sec' rather than 'counts/sec/pixel' before dividing by the pixel area; please clarify the units in the equation.
  5. [Table 1 caption] Please specify whether 'normalized by the average brightness of the image with all sources masked' means subtraction or division, and explain why this normalization is applied only to the total-brightness column.
  6. [Table 2] The R_e values are reported in pixels; giving the values in arcseconds as well would make the physical scale of the fitted profiles easier to assess.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the IGL values are fitted to direct stacked-image differences, not derived from prior IGL inputs or self-citations.

full rationale

The derivation chain is self-contained. The measured quantities I_G(R) are computed from direct aperture photometry of stacked images with and without galaxy masks (Eq. 3), using the same set of 98,713 cutouts. The central IGL values are obtained by fitting the empirical function in Eq. 8 to the six I_G(R) points per band; the fitted parameters I_e, R_e, and n are all free and are not constrained by any prior IGL measurement. No literature value, external model, or self-citation is used as an input to the fit. The agreement with previously reported IGL values is presented only as a post-hoc comparison, which is an external benchmark rather than a circular validation. The paper does not invoke a uniqueness theorem or import an ansatz from prior work by the same authors; Eq. 8 is introduced in this paper as an 'empirically adopted function.' The main caveat is that the fitted I_e values depend on the assumed functional form and on extrapolation beyond the largest mask radius, but this is a model-dependence/correctness concern, not circular reasoning. In particular, the skeptic's claim that I_G(R) is merely a masked-pixel normalization artifact is not supported by the paper's equations: I_G(R) is the difference between two weighted means with different unmasked-pixel sets, so it depends on how much galaxy light is removed by the larger masks. The paper therefore measures its target quantity from independent image data rather than reducing to its own inputs by construction.

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

The central measurement rests on domain assumptions about background cancellation and on an empirical extrapolation function whose parameters are fitted to six data points per band. Several hand-set classification cuts enter the pipeline, but the authors report they do not strongly affect the final values. No new physical entities are introduced.

free parameters (6)
  • Source classification line: mag = -6 log(FLUX_RADIUS) + 24.2 = slope -6, intercept 24.2
    Hand-set boundary separating stars from galaxies in the r-band magnitude-size plane (Eq. 1); affects which sources are masked as galaxies. The authors state small changes do not affect final IGL.
  • Faint magnitude cut for galaxy classification = 25.5 mag
    Sources fainter than 25.5 mag are discarded as not clearly galaxies; may exclude faint galaxies from the IGL estimate.
  • Minimum log(FLUX_RADIUS) cut = -0.325
    Removes compact sources below the adopted star-galaxy boundary.
  • Sersic-inspired fit parameters R_e and n per band = g: 0.0767 px, 14.81; r: 0.00868 px, 24.71; i: 0.00346 px, 28.77; z: 0.00108 px, 38.07; Y: 0.000337 px, 77.58
    Shape parameters of Eq. 8 fitted to galaxy brightness versus mask radius; they control the extrapolation that defines the IGL, especially in the Y band.
  • I_e (extrapolated IGL amplitude) = 4.27, 6.97, 8.66, 10.16, 13.78 nW/m2/sr
    The reported IGL is the asymptotic amplitude of Eq. 8, not a direct aperture measurement; it is a fitted quantity.
  • Mean E(B-V) = 0.024
    Used for the reddening correction through A_B = E(B-V) R_B; measured from Schlegel et al. maps, not hand-set, but applied as a single global value.
assumptions (6)
  • domain assumption DES DR2 coadd images are already background-subtracted with a uniform zero point of 30.0 mag.
    Invoked in Sections 3.3 and 4.1; the method relies on the pipeline background subtraction being identical across masked and unmasked stacks so the difference cancels it.
  • domain assumption Foregrounds (zodiacal light, diffuse Galactic light, airglow) and instrumental noise are common to all stacks and cancel exactly in the differences.
    Central to Sections 1 and 4.1; if masked and unmasked pixels sample different foreground levels, the difference would be biased.
  • ad hoc to paper The empirical function in Eq. 8, inspired by the Sersic profile, describes how measured galaxy light depends on mask radius for radii beyond 15 pixels.
    Used to extrapolate I_G(R) to infinite mask radius in Section 4.3; no derivation or external calibration is given.
  • domain assumption Sources classified as galaxies are the dominant contributors to the IGL, and the unclassified 'other' sources can be neglected in the IGL estimate.
    Used in Sections 3.2 and 5; the paper measures I_other at roughly 1 percent of total brightness but does not include it in the IGL.
  • domain assumption The mean brightness within a 25 arcsecond circular aperture of the stacked random positions estimates the mean IGL per steradian without cosmic variance bias.
    Used in Section 4.1; assumes random pointings are representative and that edge effects are negligible after excluding outer pixels.
  • domain assumption The global mean E(B-V) correction applies to the stack average.
    Used in Section 4.2; variations in dust reddening across fields are not propagated into the reported error budget.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Integrated Galaxy Light from Stacking $10^5$ Random Pointings in the Dark Energy Survey Data." pith.science (2026). https://pith.science/paper/CSV2ZSTK

@misc{pith2026250608162,
  author       = {Pith},
  title        = {Pith review of: Integrated Galaxy Light from Stacking $10^5$ Random Pointings in the Dark Energy Survey Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CSV2ZSTK}},
  note         = {Machine review of arXiv:2506.08162}
}
abstract

We present a new technique for measuring the integrated galaxy light (IGL) with stacked image data from the Dark Energy Survey (DES). We extract $1\times1$ arcminute cutouts from nearly 100,000 randomly selected positions in the g, r, i, z, and Y bands from the DES data release 2 (DR2) maps. We generate source catalogs and masks for each cutout and the images are subsequently stacked to generate deep images of the sky both with and without sources. The IGL is then calculated by taking the difference in average brightness between stacks that contain galaxies and stacks in which galaxies have been masked. We find IGL values of $g = 4.27 \pm 0.28, r = 6.97 \pm 0.42, i = 8.66 \pm 0.53, z = 10.16 \pm 0.7,$ and $Y = 13.78 \pm 2.35$ nW/m$^2$/sr. These measurements, which require no foreground estimation or removal, are in agreement with previously reported IGL values derived from galaxy number counts and other methods. This stacking technique reduces the sensitivity to diffuse local backgrounds but is not sensitive to large-scale diffuse extragalactic background light.

Figures

Figures reproduced from arXiv: 2506.08162 by the authors.

Figure 1
Figure 1. Flow chart of the image processing pipeline. Green rectangles represent data products while pink circles represent processing steps. Raw cutouts from the DES cutout server are first co-added to generate detection images, which are used to identify sources. A series of cuts are applied to eliminate images not suited for stacking. Source masks are generated for the surviving cutouts and the images are stacked in a myr… view at source ↗
Figure 2
Figure 2. r-band magnitude (in AB mag) versus size (in arcsec) distribution for SourceExtractor-detected sources in all 98,713 cutout positions. We used the hexbin plot on the left to generate cuts to separate stars and galaxies. The scatter plot on the right shows the resulting source classification, with gray dots representing the “other” sources. We used the Photutils (Bradley et al. 2022) segmentation module to generate m… view at source ↗
Figure 3
Figure 3. An example set of masked images created for one r-band cutout. This same set of images is created for each image for all five bands. Images are 1 × 1 arcminute squares [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Stacked r-band images generated from the weighted average of 98,713 individual cutouts. In this series of masked images, a 15-pixel dilation factor was applied to the masks for each source. Images are 1 × 1 arcminute squares. As discussed in Section 3.2, there are four…
Figure 5
Figure 5. Figure 5: Pixel distributions for stacked images with all sources masked (source mask dilation factor = 15 pixels), calculated within a 25 arcsecond circular aperture shown in [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Stacked r-band image with all detected sources masked (source mask dilation factor = 15 pixels). The white circular aperture (radius ≈ 25”) encloses the pixels used in the brightness calculation. Edge pixels, which are slightly brighter due to undetected sources, are e…
Figure 7
Figure 7. Figure 7: Extrapolation of IGL values (dashed lines) from measured galaxy brightness (red points) as a function of source mask dilation size. The shaded blue region represents 1σ fit error. Larger Y -band errors may stem from a shorter exposure time compared to griz bands. -0.25…
Figure 8
Figure 8. Figure 8: Distributions of pixel values in sixteen sets of jackknife images. of the standard deviations. Conversely, we take the error from the jackknife analysis to be the standard deviation of the average brightnesses calculated in the circular aperture in each of the sixteen …
Figure 9
Figure 9. Figure 9: Measured IGL values from this work (black circles) compared to previously reported measurements from HST/CANDELS (gray shaded region) (Saldana-Lopez et al. 2021), Fermi-LAT (brown shaded region) (Desai et al. 2019), GAMA/COSMOS/HST (blue triangles) (Driver et al. 2016)…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

38 extracted references · 2 canonical work pages

  1. [1]

    Abbott, T. M. C., Abdalla, F. B., Allam, S., et al. 2018, ApJS, 239, 18, doi: 10.3847/1538-4365/aae9f0

    doi:10.3847/1538-4365/aae9f0 2018

  2. [2]

    A., Ansoldi, S., Antonelli, L

    Acciari, V. A., Ansoldi, S., Antonelli, L. A., et al. 2019, MNRAS, 486, 4233, doi: 10.1093/mnras/stz943

    doi:10.1093/mnras/stz943 2019

  3. [3]

    G., Bazer-Bachi, A

    Aharonian, F., Akhperjanian, A. G., Bazer-Bachi, A. R., et al. 2006, Nature, 440, 1018, doi: 10.1038/nature04680

    doi:10.1038/nature04680 2006

  4. [4]

    2019, arXiv e-prints, arXiv:1902.05569, doi: 10.48550/arXiv.1902.05569

    Akeson, R., Armus, L., Bachelet, E., et al. 2019, arXiv e-prints, arXiv:1902.05569, doi: 10.48550/arXiv.1902.05569

    arXiv doi:10.48550/arxiv.1902.05569 2019

  5. [5]

    Bernstein, R. A. 2007, ApJ, 666, 663, doi: 10.1086/519824

    doi:10.1086/519824 2007

  6. [6]

    2002, in Astronomical Society of the Pacific Conference Series, Vol

    Bertin, E., Mellier, Y., Radovich, M., et al. 2002, in Astronomical Society of the Pacific Conference Series, Vol. 281, Astronomical Data Analysis Software and Systems XI, ed. D. A. Bohlender, D. Durand, & T. H. Handley, 228

    2002

  7. [7]

    1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

    doi:10.1051/aas:1996164 1996

  8. [8]

    Biteau, J., & Williams, D. A. 2015, ApJ, 812, 60, doi: 10.1088/0004-637X/812/1/60

    doi:10.1088/0004-637x/812/1/60 2015

Show all 38 references
  1. [9]

    2022, astropy/photutils: 1.5.0, 1.5.0, Zenodo, doi: 10.5281/zenodo.6825092

    Bradley, L., Sip˝ ocz, B., Robitaille, T., et al. 2022, astropy/photutils: 1.5.0, 1.5.0, Zenodo, doi: 10.5281/zenodo.6825092

    2022 doi

  2. [10]

    A., O’Brien, R., et al

    Carleton, T., Windhorst, R. A., O’Brien, R., et al. 2022, AJ, 164, 170, doi: 10.3847/1538-3881/ac8d02

    2022 doi

  3. [11]

    C., Magnier, E

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

    2016 doi arXiv

  4. [12]

    Cooray, A. 2016, R. Soc. Open Sci., 3, 150555, doi: 10.1098/rsos.150555 DES Collaboration. 2021, A&AS, 255, 20, doi: 10.3847/1538-4365/ac00b3

    2016 doi

  5. [13]

    2019, ApJL, 874, L7, doi: 10.3847/2041-8213/ab0c10 Dor´ e, O., Bock, J., Ashby, M., et al

    Desai, A., Helgason, K., Ajello, M., et al. 2019, ApJL, 874, L7, doi: 10.3847/2041-8213/ab0c10 Dor´ e, O., Bock, J., Ashby, M., et al. 2014, arXiv e-prints, arXiv:1412.4872, doi: 10.48550/arXiv.1412.4872

    2019 doi arXiv

  6. [14]

    Driver, S. P. 2021, arXiv e-prints, arXiv:2102.12089, doi: 10.48550/arXiv.2102.12089

    2021 doi arXiv

  7. [15]

    P., & Robotham, A

    Driver, S. P., & Robotham, A. S. G. 2010, MNRAS, 407, 2131, doi: 10.1111/j.1365-2966.2010.17028.x

    2010

  8. [16]

    P., Andrews, S

    Driver, S. P., Andrews, S. K., Davies, L. J., et al. 2016, ApJ, 827, 108, doi: 10.3847/0004-637X/827/2/108

    2016 doi

  9. [17]

    Dwek, E., & Arendt, R. G. 1998, ApJL, 508, L9, doi: 10.1086/311714

    1998 doi

  10. [18]

    2013, Astroparticle Physics, 43, 112, doi: 10.1016/j.astropartphys.2012.09.003 Euclid Collaboration, Mellier, Y., Abdurro’uf, et al

    Dwek, E., & Krennrich, F. 2013, Astroparticle Physics, 43, 112, doi: 10.1016/j.astropartphys.2012.09.003 Euclid Collaboration, Mellier, Y., Abdurro’uf, et al. 2024, arXiv e-prints, arXiv:2405.13491, doi: 10.48550/arXiv.2405.13491

    2013 doi

  11. [19]

    T., Honscheid, K., et al

    Flaugher, B., Diehl, H. T., Honscheid, K., et al. 2015, ApJ, 150, 150, doi: 10.1088/0004-6256/150/5/150 Gr´ eaux, L., Biteau, J., & Nievas Rosillo, M. 2024, ApJL, 975, L18, doi: 10.3847/2041-8213/ad85c9

    2015 doi

  12. [20]

    G., & Dwek, E

    Hauser, M. G., & Dwek, E. 2001, ARA&A, 39, 249, doi: 10.1146/annurev.astro.39.1.249 13

    2001 doi

  13. [21]

    G., Arendt, R

    Hauser, M. G., Arendt, R. G., Kelsall, T., et al. 1998, ApJ, 508, 25, doi: 10.1086/306379 Ivezi´ c,ˇZ., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c

    1998 doi

  14. [22]

    M., et al

    Jennewein, D. M., et al. 2023, in The Sol Supercomputer at Arizona State University, PEARC ’23 (Association for Computing Machinery), 296–301, doi: 10.1145/3569951.3597573

    2023

  15. [23]

    P., Bellstedt, S., et al

    Koushan, S., Driver, S. P., Bellstedt, S., et al. 2021, MNRAS, 503, 2033, doi: 10.1093/mnras/stab540

    2021 doi

  16. [24]

    M., Carleton, T., Cohen, S

    Kramer, D. M., Carleton, T., Cohen, S. H., et al. 2022, ApJ, 940, L15, doi: 10.3847/2041-8213/ac9cca

    2022 doi

  17. [25]

    R., Postman, M., & Weaver, H

    Lauer, T. R., Postman, M., & Weaver, H. A. 2021, ApJ, 906, 77, doi: 10.3847/1538-4357/abc881

    2021 doi

  18. [26]

    2011, ApJ, 736, 119, doi: 10.1088/0004-637X/736/2/119

    Matsuoka, Y., Ienaka, N., Kawara, K., & Oyabu, S. 2011, ApJ, 736, 119, doi: 10.1088/0004-637X/736/2/119

    2011 doi

  19. [27]

    Matsuura, S., Arai, T., & Bock, J. J. 2017, ApJ, 839, 7, doi: 10.3847/1538-4357/aa6843

    2017 doi

  20. [28]

    2017, MNRAS, 470, 2133, doi: 10.1093/mnras/stx1295

    Mattila, K., Lehtinen, K., V¨ ais¨ anen, P., von Appen-Schnur, G., & Leinert, C. 2017, MNRAS, 470, 2133, doi: 10.1093/mnras/stx1295

    2017 doi

  21. [29]

    2021, ApJ, 913, 88, doi: 10.3847/1538-4357/abf2b4

    Meinke, J., B¨ ockmann, K., Cohen, S., et al. 2021, ApJ, 913, 88, doi: 10.3847/1538-4357/abf2b4

    2021 doi

  22. [30]

    2023, ApJ, 954, 119, doi: 10.3847/1538-4357/acdcf4

    Meinke, J., Cohen, S., Moore, J., et al. 2023, ApJ, 954, 119, doi: 10.3847/1538-4357/acdcf4

    2023 doi

  23. [31]

    A., Menanteau, F., et al

    Morganson, E., Gruendl, R. A., Menanteau, F., et al. 2018, PASP, 130, 074501, doi: 10.1088/1538-3873/aab4ef O’Brien, R., Carleton, T., Windhorst, R. A., et al. 2023, AJ, 165, 237, doi: 10.3847/1538-3881/acccee Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 64...

    2018 doi

  24. [32]

    R., Parker, J

    Postman, M., Lauer, T. R., Parker, J. W., et al. 2024, ApJ, 972, 95, doi: 10.3847/1538-4357/ad5ffc

    2024 doi

  25. [33]

    G., et al

    Saldana-Lopez, A., Dom ´ ınguez, A., P´ erez-Gonz´ alez, P. G., et al. 2021, MNRAS, 507, 5144, doi: 10.1093/mnras/stab2393

    2021 doi

  26. [34]

    J., Finkbeiner, D

    Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525, doi: 10.1086/305772

    1998 doi

  27. [35]

    Symons, T., Zemcov, M., Cooray, A., Lisse, C., & Poppe, A. R. 2023, ApJ, 945, 45, doi: 10.3847/1538-4357/acaa37

    2023 doi

  28. [36]

    L., Eisenhardt, P

    Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868, doi: 10.1088/0004-6256/140/6/1868

    2010 doi

  29. [37]

    G., Adelman, J., Anderson, John E., J., et al

    York, D. G., Adelman, J., Anderson, John E., J., et al. 2000, AJ, 120, 1579, doi: 10.1086/301513

    2000 doi

  30. [38]

    2017, Nat

    Zemcov, M., Immel, P., & Nguyen, C. 2017, Nat. Comm., 8, doi: 10.1038/ncomms15003

    2017 doi

Pith tools

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