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arxiv: 2606.19862 · v1 · pith:G3HGT3DZnew · submitted 2026-06-18 · 🌌 astro-ph.GA

Multi-band Structural Analysis of KiDS-selected Low Surface Brightness Galaxies with Hyper Suprime-Cam Imaging

Dipanjan Mitra , Kanak Saha This is my paper

Pith reviewed 2026-06-26 17:09 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords low surface brightness galaxiesLSBGSersic indexstructural parametersHSC imagingKiDS surveygalaxy colorssurface brightness
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The pith

KiDS-selected LSBG candidates are confirmed as genuine low surface brightness galaxies through HSC multi-band analysis.

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

The paper performs a uniform structural analysis of 205 LSBG candidates originally selected from the KiDS survey by fitting single-component Sersic models to deep HSC G, R, and I-band images. The fits show Sersic indices peaking near 0.7 in every band and a median B-band central surface brightness of 24.55 mag arcsec^{-2}, placing the objects firmly in the low surface brightness regime. The sample is 87 percent red, yet the red and blue subsamples display statistically indistinguishable distributions in Sersic index, size, axis ratio, and surface brightness, with no correlation between color and axis ratio. These results establish the KiDS candidates as a reliable structural reference catalog of LSBGs inside the HSC footprint.

Core claim

Single-component Sersic modeling with GALFIT on HSC G, R, and I images of 205 KiDS-selected LSBG candidates yields Sersic index distributions that peak near n ≈ 0.7 across all bands, a median B-band central surface brightness of 24.55 mag arcsec^{-2}, and a strong red dominance (178 red versus 27 blue) with no significant structural differences between the color subsamples and no color-axis ratio correlation, thereby confirming that the candidates are predominantly genuine low surface brightness galaxies.

What carries the argument

Single-component Sersic profile fitting with GALFIT applied to deep multi-band HSC imaging to extract Sersic index, effective radius, axis ratio, and central surface brightness for each galaxy.

If this is right

  • Structural parameters remain consistent across the G, R, and I bands for the full sample.
  • Red and blue LSBGs share similar Sersic indices, sizes, axis ratios, and surface brightnesses.
  • Absence of color-axis ratio correlation makes dust reddening unlikely as the main cause of the red colors.
  • The resulting catalog supplies a well-characterized structural reference set of LSBGs inside the HSC footprint.

Where Pith is reading between the lines

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

  • The structural similarity across color classes may imply common formation pathways that do not depend strongly on recent star formation.
  • Applying multi-component fits to the same HSC images could test whether the single-Sersic assumption introduces systematic offsets in the derived parameters.
  • Cross-matching the catalog with spectroscopic or HI surveys could quantify how representative the KiDS selection is of the full LSBG population.

Load-bearing premise

Single-component Sersic modeling with GALFIT provides an adequate description of the light distribution without significant bias from multi-component structure, sky subtraction errors, or selection effects in the original KiDS catalog.

What would settle it

A substantial fraction of the 205 candidates showing central surface brightness brighter than 23 mag arcsec^{-2} or Sersic indices well above 1 in independent deeper imaging or spectroscopy would indicate they are not predominantly genuine LSBGs.

Figures

Figures reproduced from arXiv: 2606.19862 by Dipanjan Mitra, Kanak Saha.

Figure 1
Figure 1. Figure 1: Sky footprint of the final KiDS–HSC overlap sample in the northern survey region. Green shaded areas indicate the KiDS-North DR5 footprint, while orange shaded regions show the HSC-Wide coverage. Blue star symbols mark the positions of visually confirmed KiDS low surface brightness galaxy candidates with available HSC imaging. The strong concentration of sources within the shared KiDS–HSC footprint highlig… view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the image preprocessing and source-masking procedure applied prior to structural fitting. From left to right: (1) original HSC science cutout centered on the target galaxy; (2) background-subtracted image; (3) source segmentation map produced by the detection algorithm, with the target galaxy highlighted in blue; (4) binary mask generated from the segmentation map, where contaminating foreg… view at source ↗
Figure 3
Figure 3. Figure 3: Gallery of rejected LSBG candidates from the initial automated selection. Columns from left to right dis￾play coadd stamps in the HSC-G, HSC-R, and HSC-I bands, followed by the three-color RGB composite image. The ex￾cluded sources represent key instrumental and pipeline arti￾facts: (i) local sky-subtraction failures with unphysical back￾ground step gradients, (ii) optical scattered light and halos from ne… view at source ↗
Figure 4
Figure 4. Figure 4: Representative GALFIT S´ersic fits for an LSBG candidate in the HSC G, R, and I bands (left to right). For each band, the panels show the observed image, the best-fit￾ting PSF-convolved S´ersic model, the residual image, and the azimuthally averaged surface-brightness profile. Blue points represent the observed profile, while the red curve shows the corresponding S´ersic model. The vertical dashed line mar… view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of the estimated Johnson B-band central surface brightness (µ0,B) for the final LSBG candi￾date sample. The solid black histogram shows the sample distribution, while the gold vertical line marks the median value, µ0,B = 24.55 mag arcsec−2 . The blue dash-dotted, red dashed, and green dotted lines indicate the Freeman disk value (µ0,B = 21.65 mag arcsec−2 ), the classical low surface brightnes… view at source ↗
Figure 6
Figure 6. Figure 6: S´ersic index (n) versus axis ratio (b/a) for the HSC-KiDS sample (cyan circles) compared with the SMUDGes catalog (D. Zaritsky et al. 2023) (gray points). The top and right panels show the corresponding normalized distributions. Both samples occupy similar structural pa￾rameter space, with most galaxies clustering around n ∼ 0.7 and b/a ∼ 0.7. bution should not be interpreted directly as the intrinsic sha… view at source ↗
Figure 8
Figure 8. Figure 8: Normalized distributions of mean effective surface brightness, ⟨µe⟩, for the final LSBG candidate sample derived from HSC G-band (left), R-band (center), and I-band (right) GALFIT S´ersic models. Vertical solid lines indicate the median values in each band, while the dashed black histogram in the central panel shows the original KiDS r-band distribution for comparison. The median effective surface brightne… view at source ↗
Figure 9
Figure 9. Figure 9: The joint distribution of effective radius (Re) and mean effective surface brightness (⟨µe⟩) for the LSBG sample across the HSC G (left), R (center), and I (right) bands. In each panel, the main scatter plot is accompanied by its corresponding marginalized top and right step-filled histograms showing the localized distribution profiles. The vertical dashed and horizontal dotted lines mark the respective sa… view at source ↗
Figure 10
Figure 10. Figure 10: Object-by-object comparison of structural parameters measured from the original KiDS imaging and the deeper HSC data. The left panel compares effective radii, while the right panel compares mean effective surface brightnesses. The dashed lines indicate the one-to-one relation. The measurements exhibit strong correlations, with Spearman rank coefficients of ρs = 0.86 for effective radius and ρs = 0.96 for … view at source ↗
Figure 11
Figure 11. Figure 11: Optical color-color distribution of the final HSC-KiDS LSBG sample (orange circles) compared with the field LSBG sample of J. P. Greco et al. (2018) (light blue points). The vertical red dash-dotted line marks the (g − i) = 0.64 division used by J. P. Greco et al. (2018) to separate blue and red LSBGs. The horizontal dotted and vertical black dashed lines indicate the median colors of our sample, (g − r) … view at source ↗
Figure 12
Figure 12. Figure 12: Representative RGB cutouts of blue (g−i < 0.64) and red (g−i > 0.64) LSBG candidates from the final HSC–KiDS sample. The upper row shows blue systems, while the lower row shows red systems. The images illustrate the range of colors and morphologies present within the sample. The orientation is indicated by the North (N) and East (E) arrows in the upper right corner. A horizontal scale bar corresponding to… view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of structural properties between blue (g − i < 0.64) and red (g − i > 0.64) LSBGs. Panels show the distributions of S´ersic index, axis ratio, effective radius, and mean effective surface brightness derived from the HSC R-band GALFIT models. Dashed vertical lines mark the median values of each population. The inset in each panel reports the Kolmogorov-Smirnov statistic and corresponding p-value… view at source ↗
Figure 14
Figure 14. Figure 14: Optical color (g − i) as a function of axis ra￾tio (b/a) for the final HSC–KiDS LSB galaxy sample. Blue and red points denote galaxies with (g − i) < 0.64 and (g − i) > 0.64, respectively. The vertical green dashed line marks the color division at (g −i) = 0.64. No significant cor￾relation is observed between color and axis ratio (ρs = 0.01, p = 0.88), indicating that the red colors are unlikely to be pri… view at source ↗
Figure 16
Figure 16. Figure 16: HSC I-R-G RGB images of the two high-S´ersic-index (n > 2) outliers identified in the sample. Both galaxies exhibit compact central components embed￾ded within diffuse stellar envelopes, which likely contribute to their elevated S´ersic indices. The orientation is indicated by the North (N) and East (E) arrows in the upper right corner. A horizontal scale bar corresponding to 10′′ is shown in each image p… view at source ↗
read the original abstract

We present a homogeneous multi-band structural analysis of 205 KiDS-selected low surface brightness galaxy (LSBG) candidates using deep Hyper Suprime-Cam (HSC) $G$, $R$, and $I$-band imaging. Structural parameters were derived using single-component S\'ersic modeling with GALFIT. The sample is dominated by diffuse systems with low S\'ersic indices, with the distributions consistently peaking near $n\approx0.7$ across all bands. The estimated $B$-band central surface brightness distribution has a median value of $\tilde{\mu}_{0,B}=24.55$ mag arcsec$^{-2}$, indicating that the galaxies lie firmly within the low surface brightness regime. The catalog is strongly dominated by red systems, comprising 178 red LSBGs (87.3$\%$) and 27 blue LSBGs (12.7$\%$). Despite this color bimodality, the red and blue subsamples show similar structural properties, with no statistically significant differences in S\'ersic index, effective radius, axis ratio, or surface brightness distributions. The absence of a correlation between color and axis ratio further suggests that dust reddening is unlikely to be the primary driver of the red colors. Overall, the sample provides a well-characterized structural reference set of LSBGs in the HSC footprint and confirms that the KiDS selected candidates are predominantly genuine low surface brightness galaxies.

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

2 major / 2 minor

Summary. The paper presents a homogeneous multi-band structural analysis of 205 KiDS-selected LSBG candidates using deep HSC G, R, and I imaging. Structural parameters are obtained via single-component Sérsic modeling with GALFIT, yielding a sample dominated by low-n (n≈0.7) diffuse systems with median μ0,B = 24.55 mag arcsec⁻². The catalog is 87% red and 13% blue, with no significant structural differences between color subsamples and no color–axis ratio correlation; the authors conclude that the KiDS candidates are predominantly genuine LSBGs and provide a well-characterized reference set in the HSC footprint.

Significance. If the single-Sérsic parameters are shown to be robust, the work supplies a useful homogeneous structural catalog of LSBGs overlapping the HSC footprint, with quantified color and structural distributions that can serve as a reference for future studies. The reported similarity in structural properties between red and blue subsamples and the lack of color–axis ratio correlation are potentially valuable observational constraints.

major comments (2)
  1. [Methods / GALFIT modeling description] The confirmation that KiDS candidates are genuine LSBGs (median μ0,B = 24.55) and the utility of the catalog as a reference set rest directly on the reliability of the GALFIT single-Sérsic parameters. No fit-quality metrics (reduced χ², residual maps, or visual inspection results), formal error estimation procedures, or tests for sky-subtraction systematics in extended low-surface-brightness systems are described; such information is required to assess possible biases in μ0, Re, and n for diffuse objects.
  2. [Results / red vs. blue comparison] The claim of no statistically significant differences between red and blue subsamples in Sérsic index, effective radius, axis ratio, and surface brightness is derived from the same single-component fits. Without reported goodness-of-fit diagnostics or multi-component tests, it is unclear whether unmodeled substructure (faint disks or envelopes) could introduce systematic offsets that affect the red/blue comparison.
minor comments (2)
  1. [Abstract] The abstract states the red fraction as 87.3% and blue as 12.7%; consistent decimal precision should be used throughout.
  2. [Results] The B-band central surface brightness is reported as a median but the conversion from the fitted GRI bands is not detailed; a brief description of the color term or k-correction applied would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of the GALFIT analysis and the robustness of our red/blue comparisons. We address each major comment below and will revise the manuscript to incorporate additional details and tests.

read point-by-point responses

  1. Referee: [Methods / GALFIT modeling description] The confirmation that KiDS candidates are genuine LSBGs (median μ0,B = 24.55) and the utility of the catalog as a reference set rest directly on the reliability of the GALFIT single-Sérsic parameters. No fit-quality metrics (reduced χ², residual maps, or visual inspection results), formal error estimation procedures, or tests for sky-subtraction systematics in extended low-surface-brightness systems are described; such information is required to assess possible biases in μ0, Re, and n for diffuse objects.

    Authors: We agree that the current manuscript lacks explicit reporting of fit-quality metrics, error procedures, and sky-subtraction tests, which limits assessment of parameter reliability for these diffuse systems. In the revised version we will add a dedicated subsection to the Methods describing: (i) the distribution of reduced χ² values across the sample, (ii) results from visual inspection of residual maps for the full sample (or a statistically representative subset), (iii) the formal uncertainties returned by GALFIT together with any additional Monte-Carlo error estimation we performed, and (iv) the sky-subtraction approach used with the deep HSC data and any dedicated tests (e.g., varying sky annuli or comparing with independent sky estimates) to quantify possible biases in μ0, Re, and n. These additions will directly address the referee’s concern. revision: yes

  2. Referee: [Results / red vs. blue comparison] The claim of no statistically significant differences between red and blue subsamples in Sérsic index, effective radius, axis ratio, and surface brightness is derived from the same single-component fits. Without reported goodness-of-fit diagnostics or multi-component tests, it is unclear whether unmodeled substructure (faint disks or envelopes) could introduce systematic offsets that affect the red/blue comparison.

    Authors: We acknowledge that the absence of goodness-of-fit diagnostics makes it difficult to exclude the possibility that unmodeled substructure could introduce differential biases between the red and blue subsamples. In the revision we will (i) present a quantitative comparison of residual-map statistics (e.g., median residual flux and χ² per degree of freedom) between the two color subsamples, (ii) perform a limited multi-component (Sérsic + exponential) test on a random subset of ~30 galaxies from each color class to assess whether single-Sérsic parameters are systematically affected, and (iii) qualify the red/blue comparison statements to note the limitations of the single-component modeling. These steps will strengthen the robustness of the reported similarity in structural properties. revision: yes

Circularity Check

0 steps flagged

Purely observational characterization; no derivations or fitted predictions

full rationale

The paper reports GALFIT single-Sersic fits to HSC imaging of KiDS candidates and tabulates resulting parameter distributions (n≈0.7, median μ0,B=24.55, red/blue fractions, lack of color–axis-ratio correlation). These are direct measurements and summary statistics, not quantities derived from equations, first-principles models, or prior fits that are then re-presented as predictions. No self-citations, uniqueness theorems, or ansatzes appear in the load-bearing steps. The central claim (KiDS candidates are genuine LSBGs forming a reference set) follows immediately from the measured surface-brightness values falling below the conventional LSB threshold; the step is empirical classification, not circular.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that single-Sersic modeling accurately captures the structure of LSBGs and that the KiDS selection reliably identifies genuine LSBGs; no free parameters or invented entities are introduced beyond standard photometric fitting.

axioms (1)
  • domain assumption Single-component Sersic profiles adequately describe the light distribution of the LSBG sample
    Invoked for all structural parameter derivation via GALFIT.

pith-pipeline@v0.9.1-grok · 5788 in / 1246 out tokens · 51462 ms · 2026-06-26T17:09:01.091610+00:00 · methodology

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Works this paper leans on

52 extracted references · 46 canonical work pages · 3 internal anchors

  1. [1]

    Adams, E. A. K., vSiljeg, B., Ponomareva, A., et al. 2026, Astronomy & Astrophysics, doi: 10.1051/0004-6361/202557163

    work page doi:10.1051/0004-6361/202557163 2026

  2. [2]

    The Hyper Suprime-Cam SSP Survey: Overview and Survey Design

    Aihara, H., Arimoto, N., Armstrong, R., et al. 2018, PASJ, 70, S4, doi: 10.1093/pasj/psx066

    work page internal anchor Pith review doi:10.1093/pasj/psx066 2018

  3. [3]

    , keywords =

    Aihara, H., AlSayyad, Y., Ando, M., et al. 2022, PASJ, 74, 247, doi: 10.1093/pasj/psab122 Astropy Collaboration. 2013, Astronomy & Astrophysics, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration. 2018, The Astronomical Journal, 156, 123, doi: 10.3847/1538-3881/aabc4f Astropy Collaboration. 2022, The Astrophysical Journal, 935, 167, doi: 10....

    work page doi:10.1093/pasj/psab122 2022

  4. [4]

    2016, Journal of Open Source Software, 1, 58, doi:10.21105/joss.00058

    Barbary, K. 2016, The Journal of Open Source Software, 1, 58, doi: 10.21105/joss.00058

    work page doi:10.21105/joss.00058 2016

  5. [5]

    2023, The Astrophysical Journal Supplement Series, 267, 10, doi: 10.3847/1538-4365/acd3e7

    Yamanoi, H. 2023, The Astrophysical Journal Supplement Series, 267, 10, doi: 10.3847/1538-4365/acd3e7

    work page doi:10.3847/1538-4365/acd3e7 2023

  6. [6]

    Rauscher, B. J. 1999, MNRAS, 302, L55, doi: 10.1046/j.1365-8711.1999.02282.x

    work page doi:10.1046/j.1365-8711.1999.02282.x 1999

  7. [7]

    1996, , 117, 393, 10.1051/aas:1996164

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164 2 https://hsc-release.mtk.nao.ac.jp/doc/ 3 https://kids.strw.leidenuniv.nl/

    work page doi:10.1051/aas:1996164 1996

  8. [8]

    , archivePrefix = "arXiv", eprint =

    Blanton, M. R., & Moustakas, J. 2009, ARA&A, 47, 159, doi: 10.1146/annurev-astro-082708-101734

    work page doi:10.1146/annurev-astro-082708-101734 2009

  9. [9]

    1997, PASP, 109, 745

    Bothun, G., Impey, C., & McGaugh, S. 1997, PASP, 109, 745

    1997

  10. [10]

    S., & Boylan-Kolchin , M

    Bullock, J. S., & Boylan-Kolchin, M. 2017, ARA&A, 55, 343, doi: 10.1146/annurev-astro-091916-055313

    work page doi:10.1146/annurev-astro-091916-055313 2017

  11. [11]

    2001, AJ, 122, 2318

    Burkholder, V., Impey, C., & Sprayberry, D. 2001, AJ, 122, 2318

    2001

  12. [12]

    2024, The Open Journal of Astrophysics, 7, 89, doi: 10.33232/001c.124536

    Chicoine, N., Prat, J., Zacharegkas, G., et al. 2024, The Open Journal of Astrophysics, 7, 89, doi: 10.33232/001c.124536

    work page doi:10.33232/001c.124536 2024

  13. [13]

    2018, ApJ, 868, 96, doi: 10.3847/1538-4357/aae7c8

    Cohen, Y., Dokkum, P. V., Danieli, S., et al. 2018, The Astrophysical Journal, 868, doi: 10.3847/1538-4357/aae7c8

    work page doi:10.3847/1538-4357/aae7c8 2018

  14. [14]

    Conroy, C., Schiminovich, D., & Blanton, M. R. 2010, The Astrophysical Journal, 718, 184, doi: 10.1088/0004-637X/718/1/184 de Blok, W. J. G., & McGaugh, S. S. 1997, MNRAS, 290, 533, doi: 10.1093/mnras/290.3.533 de Jong, J. T. A., Verdoes Kleijn, G. A., Kuijken, K. H., &

    work page doi:10.1088/0004-637x/718/1/184 2010

  15. [15]

    Valentijn, E. A. 2013, Experimental Astronomy, 35, 25, doi: 10.1007/s10686-012-9306-1

    work page doi:10.1007/s10686-012-9306-1 2013

  16. [16]

    Disney, M. J. 1976, Nature, 263, 573, doi: 10.1038/263573a0

    work page doi:10.1038/263573a0 1976

  17. [17]

    I., et al

    Du, W., Wu, H., Lam, M. I., et al. 2015, AJ, 149, 199, doi: 10.1088/0004-6256/149/6/199

    work page doi:10.1088/0004-6256/149/6/199 2015

  18. [18]

    Freeman, K. C. 1970, ApJ, 160, 811 13

    1970

  19. [19]

    2011, ApJ, 728, 74, doi: 10.1088/0004-637X/728/2/74

    Padilla, N. 2011, ApJ, 728, 74, doi: 10.1088/0004-637X/728/2/74

    work page doi:10.1088/0004-637x/728/2/74 2011

  20. [20]

    W., & Driver, S

    Graham, A. W., & Driver, S. P. 2005, PASA, 22, 118, doi: 10.1071/AS05001

    work page doi:10.1071/as05001 2005

  21. [21]

    P., Greene, J

    Greco, J. P., Greene, J. E., Strauss, M. A., et al. 2018, The Astrophysical Journal, 857, 104

    2018

  22. [22]

    R., et al

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357–362, doi: 10.1038/s41586-020-2649-2

    work page doi:10.1038/s41586-020-2649-2 2020

  23. [23]

    2022, The Astrophysical Journal Supplement Series, 262, 39, doi: 10.3847/1538-4365/ac85b1

    Huang, Q., & Fan, L. 2022, The Astrophysical Journal Supplement Series, 262, 39, doi: 10.3847/1538-4365/ac85b1

    work page doi:10.3847/1538-4365/ac85b1 2022

  24. [24]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    work page doi:10.1109/mcse.2007.55 2007

  25. [25]

    1997, ARA&A, 35, 267, doi: 10.1146/annurev.astro.35.1.267

    Impey, C., & Bothun, G. 1997, ARA&A, 35, 267, doi: 10.1146/annurev.astro.35.1.267

    work page doi:10.1146/annurev.astro.35.1.267 1997

  26. [26]

    2001, SciPy: Open source scientific tools for Python, https://www

    Jones, E., Oliphant, T., Peterson, P., et al. 2001, SciPy: Open source scientific tools for Python, https://www. scipy. org

    2001

  27. [27]

    G., Maddox, S

    Lambas, D. G., Maddox, S. J., & Loveday, J. 1992, MNRAS, 258, 404, doi: 10.1093/mnras/258.2.404

    work page doi:10.1093/mnras/258.2.404 1992

  28. [28]

    2022, Astronomy & Astrophysics, doi: 10.1051/0004-6361/202142367

    Marca, A., Iodice, E., Cantiello, M., et al. 2022, Astronomy & Astrophysics, doi: 10.1051/0004-6361/202142367

    work page doi:10.1051/0004-6361/202142367 2022

  29. [29]

    1999, Philosophical Transactions of the Royal Society of London

    Margony, B. 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 357, 93–103, doi: 10.1098/rsta.1999.0316

    work page doi:10.1098/rsta.1999.0316 1999

  30. [30]

    1998, Astronomy and Astrophysics, 331,

    Masters, K. L., Nichol, R., Bamford, S., et al. 2010, MNRAS, 404, 792, doi: 10.1111/j.1365-2966.2010.16335.x

    work page doi:10.1111/j.1365-2966.2010.16335.x 2010

  31. [31]

    , keywords =

    Miyazaki, S., Komiyama, Y., Kawanomoto, S., et al. 2018, PASJ, 70, S1, doi: 10.1093/pasj/psx063

    work page doi:10.1093/pasj/psx063 2018

  32. [32]

    2024, Astronomy & Astrophysics, 681, A15, doi: 10.1051/0004-6361/202347667

    Montes, M., Trujillo, I., Karunakaran, A., et al. 2024, Astronomy & Astrophysics, 681, A15, doi: 10.1051/0004-6361/202347667

    work page doi:10.1051/0004-6361/202347667 2024

  33. [33]

    A., Koopmans, L

    Padilla, N. D., & Strauss, M. A. 2008, MNRAS, 388, 1321, doi: 10.1111/j.1365-2966.2008.13480.x

    work page doi:10.1111/j.1365-2966.2008.13480.x 2008

  34. [34]

    2018, Monthly Notices of the Royal Astronomical Society, 478, 4657–4668, doi: 10.1093/mnras/sty1139

    Pahwa, I., & Saha, K. 2018, Monthly Notices of the Royal Astronomical Society, 478, 4657–4668, doi: 10.1093/mnras/sty1139

    work page doi:10.1093/mnras/sty1139 2018

  35. [35]

    Y., Ho, L

    Peng, C. Y., Ho, L. C., Impey, C. D., & Rix, H.-W. 2002, AJ, 124, 266, doi: 10.1086/340952

    work page internal anchor Pith review doi:10.1086/340952 2002

  36. [36]

    Romanowsky, A., Tang, Y., & Bundy, K. A. 2025, Research Notes of the AAS, 9, doi: 10.3847/2515-5172/adee10

    work page doi:10.3847/2515-5172/adee10 2025

  37. [37]

    V., Navarro, J

    Sales, L. V., Navarro, J. F., Penafiel, L., et al. 2020, Monthly Notices of the Royal Astronomical Society, 494, 1848 S´ ersic, J. L. 1963, Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, 6, 41

    2020

  38. [38]

    2025, A&A, 701, A272, doi: 10.1051/0004-6361/202555232

    Stoppacher, D., Tissera, P., Rosas-Guevara, Y., Galaz, G., & O˜ norbe, J. 2025, A&A, 701, A272, doi: 10.1051/0004-6361/202555232

    work page doi:10.1051/0004-6361/202555232 2025

  39. [39]

    R., et al

    Strateva, I., Ivezic, Z., Knapp, G. R., et al. 2001, The Astronomical Journal, 122, 1861, doi: 10.1086/323301

    work page doi:10.1086/323301 2001

  40. [40]

    R., et al

    Su, H., Li, R., Napolitano, N. R., et al. 2026, A&A, 707, A354, doi: 10.1051/0004-6361/202556535

    work page doi:10.1051/0004-6361/202556535 2026

  41. [41]

    2025, Astronomy & Astrophysics, doi: 10.1051/0004-6361/202556863

    Su, Y., Yi, Z., Li, S., et al. 2025, Astronomy & Astrophysics, doi: 10.1051/0004-6361/202556863

    work page doi:10.1051/0004-6361/202556863 2025

  42. [42]

    The Dark Energy Survey

    Tanoglidis, D., Drlica-Wagner, A., Wei, K., et al. 2021, The Astrophysical Journal Supplement Series, 252, 18, doi: 10.3847/1538-4365/abca89 The Dark Energy Survey Collaboration. 2005, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0510346

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.3847/1538-4365/abca89 2021

  43. [43]

    2024, A&A, 682, A4, doi: 10.1051/0004-6361/202347649 van der Burg, R

    Thuruthipilly, H., Junais, Pollo, A., et al. 2024, A&A, 682, A4, doi: 10.1051/0004-6361/202347649 van der Burg, R. F. J., Muzzin, A., & Hoekstra, H. 2016, A&A, 590, A20, doi: 10.1051/0004-6361/201628222 van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Computing in Science & Engineering, 13, 22–30, doi: 10.1109/mcse.2011.37 van Dokkum, P. G., Romano...

    work page doi:10.1051/0004-6361/202347649 2024

  44. [44]

    2017, Astronomy and Astrophysics, 608, doi: 10.1051/0004-6361/201730696

    Venhola, A., Peletier, R., Laurikainen, E., et al. 2017, Astronomy and Astrophysics, 608, doi: 10.1051/0004-6361/201730696

    work page doi:10.1051/0004-6361/201730696 2017

  45. [45]

    2026, The Astrophysical Journal, 997, 32, doi: 10.3847/1538-4357/ae20f5

    Wei, L., Huang, S., Li, J., et al. 2026, The Astrophysical Journal, 997, 32, doi: 10.3847/1538-4357/ae20f5

    work page doi:10.3847/1538-4357/ae20f5 2026

  46. [46]

    , keywords =

    Wright, A. H., Kuijken, K., Hildebrandt, H., et al. 2024, A&A, 686, A170, doi: 10.1051/0004-6361/202346730

    work page doi:10.1051/0004-6361/202346730 2024

  47. [47]

    Edge-on Low- surface-brightness Galaxy Candidates Detected from SDSS Images Using YOLO

    Xing, Y., Yi, Z., Liang, Z., et al. 2023, The Astrophysical Journal Supplement Series, 269, doi: 10.3847/1538-4365/ad0551

    work page doi:10.3847/1538-4365/ad0551 2023

  48. [48]

    2016, ApJS, 225, 11, doi: 10.3847/0067-0049/225/1/11

    Yagi, M., Koda, J., Komiyama, Y., & Yamanoi, H. 2016, ApJS, 225, 11, doi: 10.3847/0067-0049/225/1/11

    work page doi:10.3847/0067-0049/225/1/11 2016

  49. [49]

    2022, MNRAS, 513, 3972, doi: 10.1093/mnras/stac775

    Yi, Z., Li, J., Du, W., et al. 2022, MNRAS, 513, 3972, doi: 10.1093/mnras/stac775

    work page doi:10.1093/mnras/stac775 2022

  50. [50]

    G., Adelman , J., Anderson , Jr., J

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

    work page doi:10.1086/301513 2000

  51. [51]

    2006, A&A, 452, 857, doi: 10.1051/0004-6361:20054196

    Zackrisson, E., Bergvall, N., Marquart, T., & ¨Ostlin, G. 2006, A&A, 452, 857, doi: 10.1051/0004-6361:20054196

    work page doi:10.1051/0004-6361:20054196 2006

  52. [52]

    2023, The Astrophysical Journal Supplement Series, 267, 27, 10.3847/1538-4365/acdd71

    Zaritsky, D., Donnerstein, R., Dey, A., et al. 2023, ApJS, 267, 27, doi: 10.3847/1538-4365/acdd71

    work page doi:10.3847/1538-4365/acdd71 2023