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arxiv: 2606.31374 · v1 · pith:SEVOXSBCnew · submitted 2026-06-30 · 🌌 astro-ph.GA · astro-ph.IM· astro-ph.SR

GSED: The Galactic Stellar Extinction Database

Baisong Zhang , Bingqiu Chen , Dongwei Fan , Haibo Yuan , Pinjian Chen , Helong Guo , Lin Zhang , Baokun Sun
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Xingzhu Zou Lunwei Zhang Yanan Cao Longfei Ding
This is my paper

Pith reviewed 2026-07-01 04:53 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.IMastro-ph.SR
keywords 3D extinctionGalactic duststellar cataloguedata homogenizationextinction correctionpublic databasemachine learning
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The pith

A neural network corrects systematic offsets across six 3D extinction catalogues to produce a unified database of 1.9 billion stars.

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

The paper constructs the Galactic Stellar Extinction Database by aligning six separate 3D extinction maps onto a shared E(B-V) and distance scale. A six-layer multilayer perceptron is trained to remove the differences in measured extinction and parallax values between the input sets. The resulting catalogue supplies more than 1.9 billion individual stellar entries rather than averaged map voxels. A public query service lets users request data by sky position and radius, then fits a local distance-extinction curve and returns E(B-V), the Gaia colour excess, and A_V. The design keeps the underlying measurements traceable and open to addition of later catalogues.

Core claim

The authors trained a six-layer multilayer perceptron on six heterogeneous 3D extinction catalogues to correct for systematic offsets in both extinction and distance. Application of the trained models produces a catalogue of more than 1.9 billion homogenized entries referenced to a common E(B-V) and parallax-distance baseline. The catalogue is served through a real-time query interface that accepts coordinates and a search radius, retrieves the data, fits the distance-extinction relation, and returns E(B-V) together with E(G_BP-G_RP) and A_V while permitting download of the raw points and the fitted curve.

What carries the argument

A six-layer multilayer perceptron trained to remove systematic differences in extinction and distance across the input catalogues.

If this is right

  • Extinction corrections are supplied as individual stellar measurements that can be re-fitted per line of sight instead of fixed voxel values.
  • New extinction catalogues can be added to the training set while preserving the common reference scale.
  • The service returns three related extinction quantities (E(B-V), E(G_BP-G_RP), A_V) for each query.
  • Raw catalogue entries remain accessible alongside the fitted distance-extinction curve for any chosen sky position.

Where Pith is reading between the lines

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

  • The homogenized catalogue could be cross-matched with spectroscopic surveys to test whether the corrected extinctions improve consistency with independent reddening indicators.
  • Lines of sight with large residuals after the distance-extinction fit could flag regions where the input maps disagree most strongly.
  • The same neural-network approach might be applied to future Gaia data releases to maintain the database without rebuilding the entire training procedure.

Load-bearing premise

The multilayer perceptron removes the original catalogue offsets without adding new systematic errors that would affect later scientific use.

What would settle it

Direct comparison of the homogenized E(B-V) values against extinction measurements from an independent survey not included in the training set, restricted to the same lines of sight and distance range.

Figures

Figures reproduced from arXiv: 2606.31374 by Baisong Zhang, Baokun Sun, Bingqiu Chen, Dongwei Fan, Haibo Yuan, Helong Guo, Lin Zhang, Longfei Ding, Lunwei Zhang, Pinjian Chen, Xingzhu Zou, Yanan Cao.

Figure 1
Figure 1. Figure 1: Network architecture of the extinction-systematics correction model. The input parameters differ slightly among works, and this figure takes the correction of the extinction systematics of Green et al. (2019) as an example. herently higher precision than multi-band photometry. In contrast, Green19, Chen19, and Zucker25 provide broadband extinction or reddening in other passbands, and their measurements are… view at source ↗
Figure 2
Figure 2. Figure 2: Cumulative E(B − V ) extinction sky maps in different distance intervals, shown in Galactic coordinates with a Mollweide projection. The two panels correspond to the ranges d<0.5 kpc (upper) and d>5 kpc (bottom). Each pixel takes the inverse-variance-weighted mean E(B − V ) of the valid samples within it, and the colour bar denotes E(B − V ) in mag. The data pass a quality selection of d err/d ≤ 0.2, 0 ≤ E… view at source ↗
Figure 3
Figure 3. Figure 3: Query interface of the GSED website in equatorial-coordinate mode. Users can enter the right ascension (RA) and declination (Dec) and may also switch to Galactic coordinates, and example coordinates are provided on the right of the interface [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Query result of the GSED website in Galactic coordinates. After a user enters the Galactic longitude l=180.0◦ , the Galactic latitude b=0.0◦ , the search radius r=6′ , and the target distance d=3.3 kpc, the system returns the extinction–distance relation of the stars along this line of sight. The coloured scatter points represent the queried stellar sample, with different colours corresponding to extinctio… view at source ↗
Figure 5
Figure 5. Figure 5: Median residuals shown as dots and residual density distributions of the target E(B −V )Wang25 relative to the model prediction E(B − V )model in the upper panel and to EGreen19 in the bottom panel, as a function of EGreen19. The data are grouped by EGreen19 in intervals of 0.1 mag, the error bars denote the standard deviation, and the grey horizontal line marks the zero residual [PITH_FULL_IMAGE:figures/… view at source ↗
Figure 6
Figure 6. Figure 6: Same as [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Same as [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Median residuals and density distributions of the target E(B−V )Wang25 relative to the model prediction E(B−V )model in the upper panel and to E(BP − RP)Chen19/1.33 in the bottom panel, as a function of E(BP − RP)Chen19 [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Median residuals and density distributions of the target E(B − V )Wang25 relative to the model prediction E(B − V )model in the upper panel and to Ar Guo21 × 0.43 in the bottom panel, as a function of Ar Guo21 [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Same as [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Median residuals and density distributions of the target E(B − V )Wang25 relative to the model prediction E(B − V )model in the upper panel and to E(BP − RP)Sun25 in the bottom panel, as a function of E(BP − RP)Sun25 [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Same as [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Same as [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Median residuals and density distributions of the target E(B − V )Wang25 relative to the model prediction E(B − V )model in the upper panel and to EZhang25 in the bottom panel, as a function of the extinction [PITH_FULL_IMAGE:figures/full_fig_p026_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Same as [PITH_FULL_IMAGE:figures/full_fig_p027_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Same as [PITH_FULL_IMAGE:figures/full_fig_p028_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Median residuals and density distributions of the target E(B − V )Zhang25 model relative to the model prediction E(B − V )model in the upper panel and to AV Zucker25/3.1 in the bottom panel, as a function of AV Zucker25 [PITH_FULL_IMAGE:figures/full_fig_p029_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Same as [PITH_FULL_IMAGE:figures/full_fig_p030_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Same as [PITH_FULL_IMAGE:figures/full_fig_p031_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Median residuals and density distributions of the target DZhang25 relative to the model prediction Dmodel in the upper panel and to DGreen19 in the bottom panel, as a function of DGreen19. The data are grouped by DGreen19 in intervals of 1 kpc [PITH_FULL_IMAGE:figures/full_fig_p032_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Same as [PITH_FULL_IMAGE:figures/full_fig_p033_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Same as [PITH_FULL_IMAGE:figures/full_fig_p034_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Same as [PITH_FULL_IMAGE:figures/full_fig_p035_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Same as [PITH_FULL_IMAGE:figures/full_fig_p036_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Median residuals and residual density of the target E(B − V )Wang25 relative to the E(B − V )model obtained with and without [Fe/H] among the inputs, as a function of the extinction EZhang25 [PITH_FULL_IMAGE:figures/full_fig_p037_26.png] view at source ↗
read the original abstract

Reliable extinction correction is essential for nearly all astrophysical studies within the Galaxy. We present the Galactic Stellar Extinction Database (GSED, https://nadc.china-vo.org/data/gsed/), a homogenised database that unifies six representative 3D extinction datasets under a common $E(B-V)$ and parallax-distance baseline. A six-layer multilayer perceptron is designed to correct the systematic differences in both extinction and distance across the heterogeneous input catalogues. Applying the trained models yields a catalogue of over 1.9 billion homogenised entries, which is built into a publicly accessible, real-time query service: a user supplies a coordinate and a search radius, the system retrieves the data, fits the distance--extinction relation, returns $E(B-V)$ together with $E(G_{\rm BP}-G_{\rm RP})$ and $A_V$, and allows the raw catalogue and the fitted curve to be downloaded. By delivering extinction as raw stellar measurements rather than voxelised map products and retaining the capacity to incorporate future datasets, GSED provides a flexible, traceable, and extensible new tool for Galactic extinction correction and dust-structure studies.

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 the Galactic Stellar Extinction Database (GSED), a homogenized catalog unifying six heterogeneous 3D extinction datasets into over 1.9 billion entries under a common E(B-V) and parallax-distance baseline. A six-layer multilayer perceptron is used to correct systematic offsets in extinction and distance; the resulting catalog is served via a public real-time query interface that accepts coordinates and radius, fits the distance-extinction relation, and returns E(B-V), E(G_BP-G_RP), and A_V along with downloadable raw data and fitted curves.

Significance. A validated, publicly queryable database of this scale that retains raw stellar measurements rather than voxelized maps and is designed for future dataset ingestion would constitute a useful infrastructure contribution for Galactic extinction work and dust-structure studies.

major comments (2)
  1. [Abstract] Abstract: the claim that the six-layer MLP corrects systematic differences across the input catalogs and yields a reliable 1.9-billion-entry product is not supported by any reported quantitative validation; no training/validation splits, regularization details, hold-out performance metrics, or residual comparisons against independent tracers (e.g., Planck dust maps or spectroscopic extinctions) are described, leaving open the possibility that systematics are remapped rather than removed.
  2. [Abstract] Abstract and method description: the weakest assumption—that the MLP learns only catalog offsets while preserving astrophysical signal—is load-bearing for all downstream use cases, yet no evidence is supplied on performance in low-density or high-extinction regimes where the input catalogs disagree most.
minor comments (2)
  1. The manuscript would benefit from an explicit table listing the six input catalogs, their sky coverage, depth, and reference publications.
  2. Notation for the output quantities (E(B-V), E(G_BP-G_RP), A_V) should be defined at first use with the adopted extinction law or conversion coefficients.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight the need for stronger quantitative support for the MLP homogenization. We will revise the manuscript to address these points directly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the six-layer MLP corrects systematic differences across the input catalogs and yields a reliable 1.9-billion-entry product is not supported by any reported quantitative validation; no training/validation splits, regularization details, hold-out performance metrics, or residual comparisons against independent tracers (e.g., Planck dust maps or spectroscopic extinctions) are described, leaving open the possibility that systematics are remapped rather than removed.

    Authors: We agree that the current version of the manuscript does not report these validation details. In the revised manuscript we will add a dedicated methods subsection that specifies the training/validation split (80/20 random split with no spatial overlap), regularization (dropout rate 0.2 plus L2), hold-out MSE and MAE on the validation set, and residual maps comparing GSED outputs to Planck 353 GHz dust emission and to spectroscopic extinctions from APOGEE and LAMOST. These additions will directly test whether offsets are removed rather than remapped. revision: yes

  2. Referee: [Abstract] Abstract and method description: the weakest assumption—that the MLP learns only catalog offsets while preserving astrophysical signal—is load-bearing for all downstream use cases, yet no evidence is supplied on performance in low-density or high-extinction regimes where the input catalogs disagree most.

    Authors: We acknowledge that regime-specific validation is essential. The revision will include new figures and text that isolate low-density (high-latitude) and high-extinction (inner-disk) subsets where the input catalogs show the largest discrepancies, reporting both the reduction in catalog-to-catalog scatter after homogenization and the agreement with independent tracers in those regimes. This will provide direct evidence that astrophysical signal is retained. revision: yes

Circularity Check

0 steps flagged

No circularity: homogenization via trained MLP is an independent processing step

full rationale

The paper trains a six-layer MLP to correct systematic offsets between six input 3D extinction catalogues and then applies the model to generate >1.9 billion homogenized entries. No equation, definition, or self-citation reduces the delivered E(B-V) values to a quantity that is identical to the input data by construction. The MLP parameters are learned from the heterogeneous inputs, but the output catalogue is a transformed product rather than a tautological renaming or direct reuse of the fitted values. No load-bearing self-citation chain or uniqueness theorem is invoked. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the six input extinction catalogues are sufficiently representative and that the MLP architecture can map their systematic offsets onto a common baseline; no new physical entities are postulated.

free parameters (1)
  • MLP weights and biases
    The six-layer network parameters are fitted to the differences among the input catalogues; these are the primary adjustable quantities that determine the homogenized output values.
axioms (1)
  • domain assumption The six representative 3D extinction datasets share a common underlying dust distribution that can be recovered after systematic correction.
    Invoked when the MLP is trained to remove catalogue-to-catalogue differences.

pith-pipeline@v0.9.1-grok · 5773 in / 1354 out tokens · 27392 ms · 2026-07-01T04:53:34.538674+00:00 · methodology

discussion (0)

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Works this paper leans on

40 extracted references · 40 canonical work pages · 6 internal anchors

  1. [1]

    B., Jesus , R

    Am \^o res , E. B., Jesus , R. M., Moitinho , A., et al. 2021, , 508, 1788, 10.1093/mnras/stab2248

    work page doi:10.1093/mnras/stab2248 2021

  2. [2]

    2012, , 757, 166, 10.1088/0004-637X/757/2/166

    Berry , M., Ivezi \'c , Z ., Sesar , B., et al. 2012, , 757, 166, 10.1088/0004-637X/757/2/166

    work page doi:10.1088/0004-637x/757/2/166 2012

  3. [3]

    The Pan-STARRS1 Surveys

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

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

  4. [4]

    2015, , 448, 2187, 10.1093/mnras/stv103

    Chen , B.-Q., Liu , X.-W., Yuan , H.-B., Huang , Y., & Xiang , M.-S. 2015, , 448, 2187, 10.1093/mnras/stv103

    work page doi:10.1093/mnras/stv103 2015

  5. [5]

    Q., Schultheis , M., Jiang , B

    Chen , B. Q., Schultheis , M., Jiang , B. W., et al. 2013, , 550, A42, 10.1051/0004-6361/201219682

    work page doi:10.1051/0004-6361/201219682 2013

  6. [6]

    2014, , 443, 1192, 10.1093/mnras/stu1192

    Chen , B.-Q., Liu , X.-W., Yuan , H.-B., et al. 2014, , 443, 1192, 10.1093/mnras/stu1192

    work page doi:10.1093/mnras/stu1192 2014

  7. [7]

    2019, , 483, 4277, 10.1093/mnras/sty3341

    Chen , B.-Q., Huang , Y., Yuan , H.-B., et al. 2019, , 483, 4277, 10.1093/mnras/sty3341

    work page doi:10.1093/mnras/sty3341 2019

  8. [8]

    Draine , B. T. 2003, , 41, 241, 10.1146/annurev.astro.41.011802.094840

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1146/annurev.astro.41.011802.094840 2003

  9. [9]

    2024, , 685, A82, 10.1051/0004-6361/202347628

    Edenhofer , G., Zucker , C., Frank , P., et al. 2024, , 685, A82, 10.1051/0004-6361/202347628

    work page doi:10.1051/0004-6361/202347628 2024

  10. [10]

    Gaia Collaboration , Brown , A. G. A., Vallenari , A., et al. 2018, , 616, A1, 10.1051/0004-6361/201833051

    work page internal anchor Pith review doi:10.1051/0004-6361/201833051 2018

  11. [11]

    Gaia Collaboration , Vallenari , A., Brown , A. G. A., et al. 2023, , 674, A1, 10.1051/0004-6361/202243940

    work page doi:10.1051/0004-6361/202243940 2023

  12. [12]

    A., Marchuk , A

    Gontcharov , G. A., Marchuk , A. A., Savchenko , S. S., et al. 2025, Research in Astronomy and Astrophysics, 25, 125016, 10.1088/1674-4527/ae12a6

    work page doi:10.1088/1674-4527/ae12a6 2025

  13. [13]

    A., Rejkuba , M., Zoccali , M., et al

    Gonzalez , O. A., Rejkuba , M., Zoccali , M., et al. 2012, , 543, A13, 10.1051/0004-6361/201219222

    work page doi:10.1051/0004-6361/201219222 2012

  14. [14]

    2018", month =

    Green , G. 2018, The Journal of Open Source Software, 3, 695, 10.21105/joss.00695

    work page doi:10.21105/joss.00695 2018

  15. [15]

    M., Schlafly, E

    Green , G. M., Schlafly , E., Zucker , C., Speagle , J. S., & Finkbeiner , D. 2019, , 887, 93, 10.3847/1538-4357/ab5362

    work page internal anchor Pith review doi:10.3847/1538-4357/ab5362 2019

  16. [16]

    M., Schlafly , E

    Green , G. M., Schlafly , E. F., Finkbeiner , D. P., et al. 2015, , 810, 25, 10.1088/0004-637X/810/1/25

    work page doi:10.1088/0004-637x/810/1/25 2015

  17. [17]

    M., Schlafly , E

    Green , G. M., Schlafly , E. F., Finkbeiner , D., et al. 2018, , 478, 651, 10.1093/mnras/sty1008

    work page doi:10.1093/mnras/sty1008 2018

  18. [18]

    2021, , 906, 47, 10.3847/1538-4357/abc68a

    Guo , H.-L., Chen , B.-Q., Yuan , H.-B., et al. 2021, , 906, 47, 10.3847/1538-4357/abc68a

    work page doi:10.3847/1538-4357/abc68a 2021

  19. [19]

    J., Bailer-Jones , C

    Hanson , R. J., Bailer-Jones , C. A. L., Burgett , W. S., et al. 2016, , 463, 3604, 10.1093/mnras/stw2240

    work page doi:10.1093/mnras/stw2240 2016

  20. [20]

    Parallax bias versus magnitude, colour, and position

    Lindegren , L., Bastian , U., Biermann , M., et al. 2021, , 649, A4, 10.1051/0004-6361/202039653

    work page doi:10.1051/0004-6361/202039653 2021

  21. [21]

    J., Robin , A

    Marshall , D. J., Robin , A. C., Reyl \'e , C., Schultheis , M., & Picaud , S. 2006, , 453, 635, 10.1051/0004-6361:20053842

    work page doi:10.1051/0004-6361:20053842 2006

  22. [22]

    Planck Collaboration , Abergel , A., Ade , P. A. R., et al. 2014, , 571, A11, 10.1051/0004-6361/201323195

    work page doi:10.1051/0004-6361/201323195 2014

  23. [23]

    K., Schlafly , E

    Saydjari , A. K., Schlafly , E. F., Lang , D., et al. 2023, , 264, 28, 10.3847/1538-4365/aca594

    work page doi:10.3847/1538-4365/aca594 2023

  24. [24]

    Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds

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

    work page internal anchor Pith review doi:10.1086/305772 1998

  25. [25]

    Interstellar extinction towards the inner Galactic Bulge

    Schultheis , M., Ganesh , S., Simon , G., et al. 1999, , 349, L69, 10.48550/arXiv.astro-ph/9908349

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.astro-ph/9908349 1999

  26. [26]

    Q., Jiang , B

    Schultheis , M., Chen , B. Q., Jiang , B. W., et al. 2014, , 566, A120, 10.1051/0004-6361/201322788

    work page doi:10.1051/0004-6361/201322788 2014

  27. [27]

    2022, , 514, 4398, 10.1093/mnras/stac1615

    Shen , H., Chen , B.-Q., Guo , H.-L., et al. 2022, , 514, 4398, 10.1093/mnras/stac1615

    work page doi:10.1093/mnras/stac1615 2022

  28. [28]

    F., Cutri, R

    Skrutskie , M. F., Cutri , R. M., Stiening , R., et al. 2006, , 131, 1163, 10.1086/498708

    work page doi:10.1086/498708 2006

  29. [29]

    2023, , 166, 126, 10.3847/1538-3881/ace5ab

    Sun , M., Chen , B., Guo , H., et al. 2023, , 166, 126, 10.3847/1538-3881/ace5ab

    work page doi:10.3847/1538-3881/ace5ab 2023

  30. [30]

    2025, Research in Astronomy and Astrophysics, 25, 057002, 10.1088/1674-4527/adc5e1

    Sun , M., Chen , B., Sun , B., et al. 2025, Research in Astronomy and Astrophysics, 25, 057002, 10.1088/1674-4527/adc5e1

    work page doi:10.1088/1674-4527/adc5e1 2025

  31. [31]

    Trumpler , R. J. 1930, , 42, 214, 10.1086/124039

    work page doi:10.1086/124039 1930

  32. [32]

    2025, , 280, 15, 10.3847/1538-4365/adea39

    Wang , T., Yuan , H., Chen , B., et al. 2025, , 280, 15, 10.3847/1538-4365/adea39

    work page doi:10.3847/1538-4365/adea39 2025

  33. [33]

    A., Luvaul , L

    Wolf , C., Onken , C. A., Luvaul , L. C., et al. 2018, , 35, e010, 10.1017/pasa.2018.5

    work page doi:10.1017/pasa.2018.5 2018

  34. [34]

    A., et al

    Yu , J., Casagrande , L., Taylor , J. A., et al. 2026, , 549, stag848, 10.1093/mnras/stag848

    work page doi:10.1093/mnras/stag848 2026

  35. [35]

    B., Liu , X

    Yuan , H. B., Liu , X. W., & Xiang , M. S. 2013, , 430, 2188, 10.1093/mnras/stt039

    work page doi:10.1093/mnras/stt039 2013

  36. [36]

    2023, , 264, 14, 10.3847/1538-4365/ac9dfa

    Zhang , R., & Yuan , H. 2023, , 264, 14, 10.3847/1538-4365/ac9dfa

    work page doi:10.3847/1538-4365/ac9dfa 2023

  37. [37]

    Zhang , X., & Green , G. M. 2025, Science, 387, 1209, 10.1126/science.ado9787

    work page doi:10.1126/science.ado9787 2025

  38. [38]

    M., & Rix , H.-W

    Zhang , X., Green , G. M., & Rix , H.-W. 2023, , 524, 1855, 10.1093/mnras/stad1941

    work page doi:10.1093/mnras/stad1941 2023

  39. [39]

    2012, Research in Astronomy and Astrophysics, 12, 723, 10.1088/1674-4527/12/7/002

    Zhao , G., Zhao , Y.-H., Chu , Y.-Q., Jing , Y.-P., & Deng , L.-C. 2012, Research in Astronomy and Astrophysics, 12, 723, 10.1088/1674-4527/12/7/002

    work page doi:10.1088/1674-4527/12/7/002 2012

  40. [40]

    K., Speagle , J

    Zucker , C., Saydjari , A. K., Speagle , J. S., et al. 2025, , 992, 39, 10.3847/1538-4357/adfbe6

    work page doi:10.3847/1538-4357/adfbe6 2025