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Analysis of the Gaia Data Release 3 parallax bias at bright magnitudes

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read Gaia bright-star parallaxes sit about 39 microarcseconds too small.

desk verdict A valuable catalog and a plausible method, but the headline -39 μas PZPO is an artifact of where the authors set the 5σ cut. read the letter →

arxiv 2502.08068 v2 pith:XHK4TSHL submitted 2025-02-12 astro-ph.SR

Ye Ding , Shilong Liao , Shangyu Wen , Zhaoxiang Qi This is my paper · ORCID
classification astro-ph.SR PACS 95.10.Jk97.80.Fk
keywords GaiaDR3parallaxzero-pointbrightstarsorbitalbinaryastrometriccalibrationVLBIastrometryMCMCsimulation
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

Using binary stars whose visual and spectroscopic orbits pin down their distances without any assumption about luminosity or period, this paper measures the parallax zero-point offset (PZPO) of Gaia Data Release 3 for stars brighter than $G=13$. After an MCMC simulation removes binaries whose unseen orbital motion distorts the single-star astrometric solution, the cleanest 44 binaries yield a weighted mean offset of $-38.9\pm 10.3$ $\mu$as, meaning GDR3 parallaxes for bright stars are systematically too small by that amount. The paper also finds that Gaia's formal parallax errors for these stars are underestimated by about a factor of two, so realistic uncertainties are closer to 20 $\mu$as. Independent checks with VLBI and HST parallaxes give offsets of $-14.8\pm 10.6$ and $-31.9\pm 14.1$ $\mu$as, and stars with $G\le 8$ show stronger and more erratic bias. The work matters because bright-star parallaxes anchor the local distance ladder, and the compiled orbital parallax catalogue can be reused to validate future Gaia releases.

What carries the argument

The load-bearing object is the orbital parallax: a distance derived purely from Keplerian geometry by combining a visual orbit (angular semi-major axis) with a spectroscopic orbit (radial-velocity semi-amplitudes), requiring no luminosity or period assumption. To remove the contamination that orbital motion injects into Gaia's single-star astrometric solution, the paper runs an MCMC forward simulation: it generates mock along-scan observations at Gaia's actual transit times using the predicted scan angles and Thiele-Innes elements, adds Gaussian noise with the Everall et al. (2021) error model, fits a single-star model to the mock data, and marks a binary 'good' when its simulated parallax stays within 20% of the GDR3 parallax at 95% confidence. The final offset is then the weighted mean of $\pi_{\rm GDR3}-\pi_{\rm orb}$ for the 'good', short-period binaries. The uncertainty-underestimation factor is measured from the width of the $\Delta\pi/\sigma_\Delta$ distribution, which should be unity if the formal errors were correct.

What would settle it

Re-run the same 44-system selection using Gaia DR4 astrometric solutions; the orbital parallaxes are fixed, so the final-selection PZPO should move by less than its roughly 20 $\mu$as uncertainty if the DR3 result is correct, whereas a shift beyond that would show the offset was an artifact of DR3 calibration. A second check is to replace the Gaussian along-scan error model in the simulation with the actual per-transit error distribution from Gaia's calibration files and see whether the 'good' classification and the $-38.9$ $\mu$as value survive.

Watch

Extended reading notes

Core claim

The paper claims that the GDR3 parallax zero-point offset at bright magnitudes ($G<13$) is approximately $-38.9\pm 10.3$ $\mu$as once the orbital-motion contamination of binary systems is filtered out. It compiles 249 orbital parallaxes for 246 binary systems from the literature, simulates each system as Gaia saw it during the DR3 mission interval, and keeps only the 44 binaries with periods under 100 days whose parallaxes are judged 'good' under the criterion $|\pi_{\rm GDR3}-\pi_{\rm simu}|/\pi_{\rm GDR3}<0.2$ at 95% confidence. The remaining, more orbit-affected binaries give a different, more negative offset ($-58.0\pm 10.1$ $\mu$as), which the paper reads as direct evidence that orbital motion biases single-star parallax solutions. It further argues that the formal uncertainties of the offset are underestimated by roughly a factor of two, based on a Gaussian fit ($\sigma=2.07$) and a bootstrap estimate (median $\approx 1.8$) of the distribution of $\Delta\pi/\sigma_\Delta$. For stars with independent trigonometric parallaxes from VLBI and HST, the paper reports weighted mean offsets of $-14.8\pm 10.6$ and $-31.9\pm 14.1$ $\mu$as, and it warns that $G\le 8$ stars show a larger, calibration-driven bias.

Load-bearing premise

The load-bearing premise is that the MCMC forward simulation faithfully reproduces how Gaia's single-star astrometric solution responds to a binary's unseen orbital motion, using the adopted orbital elements and the Everall et al. (2021) Gaussian error model; if that model misclassifies binaries as 'good' or 'bad', the $-38.9$ $\mu$as value is biased.

Editorial extensions

If this is right

  • If the offset is real, users of GDR3 parallaxes for stars with $G<13$ should add roughly $+39$ $\mu$as to correct the average bias, with the correction growing more uncertain for $G\le 8$.
  • The factor-two underestimate of formal uncertainties means that bright-star parallax errors should be inflated by about 2.0 before being used in weighted averages, or the bias will be over-fit.
  • Binary systems with orbital periods under 100 days and 'good' MCMC parallaxes are the cleanest bright-star distance anchors, while wider systems should be avoided in zero-point studies unless their orbital motion is explicitly modeled.
  • The compiled catalogue of 249 orbital parallaxes provides a reusable, assumption-free benchmark for checking the parallax zero-point in upcoming Gaia data releases.

Reading between the lines

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

  • If the bright-star offset near $-39$ $\mu$as is confirmed, it would combine with the fainter-magnitude QSO-based offset to create a magnitude-dependent parallax correction curve that any distance-ladder calibration crossing $G\simeq 8$–13 must incorporate; this paper begins that map but does not complete it.
  • The gap between the binary-based offset ($-39$ $\mu$as) and the VLBI-based offset ($-15$ $\mu$as) may reflect a color or position dependence of the Gaia bias, or unmodeled systematics in one of the external methods; a direct overlap sample with both VLBI and orbital parallaxes for the same stars would separate the two.
  • A natural testable extension is to apply the same MCMC selection to Gaia DR4 when it is released: the orbital catalogue is fixed, so only the Gaia observations change, which isolates how the zero-point evolves with the new astrometric solution.
  • Individual outliers such as HD 27149, with a parallax difference of $1.09\pm 0.04$ mas, suggest the bright-end bias is not a smooth function of magnitude; mapping it source-by-source would require more saturated-star calibrators, which the orbital catalogue enables.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper investigates the GDR3 parallax zero-point offset for bright stars (G < 13) using three independent tracers: orbital parallaxes from 246 visual/spectroscopic binaries (249 entries), VLBI parallaxes, and HST parallaxes. The authors apply Eq. (1) quality cuts, then use MCMC forward modeling of the binary orbital signal to identify 93 'good' solutions, and further restrict to 44 binaries with P < 100 days to obtain a weighted-mean 'final selection' PZPO of -38.9 +/- 10.3 microas, versus -58.0 +/- 10.1 microas for the remaining 88 binaries. They report -14.8 +/- 10.6 microas (VLBI) and -31.9 +/- 14.1 microas (HST), find stronger bias for G <= 8, and estimate that GDR3 formal uncertainties are underestimated by a factor of about 2.0. The paper supplies a large compiled catalog of orbital parallaxes and an MCMC simulation pipeline.

Significance. If the headline value survives scrutiny, the paper provides an important, assumption-light constraint on the bright-end GDR3 PZPO, where quasar-based calibration cannot reach, and the compiled catalog is a reusable community resource for future data releases. The use of external orbital, VLBI, and HST parallaxes avoids the circularity that affects calibrations against other Gaia-dependent distances, and the MCMC forward simulation is a principled way to separate orbital-motion bias. However, the central numerical claim currently rests on a small final sample and on several arbitrary selection thresholds, so the significance of the specific -39 microas value depends on the robustness analysis that is missing rather than on the strength of the catalog itself.

major comments (4)
  1. [Table 2 and Sec. 4.1] The headline value is not robust to the 5-sigma rejection rule in Eq. (1)(iv). Adding back only the four sources that are excluded by criterion (iv) but pass all other final-selection criteria changes the weighted mean PZPO from -38.9 +/- 10.3 microas to +7.9 +/- 10.0 microas, a change of about 3.3 sigma that is driven mainly by HD 27149. Because the 5-sigma threshold is an arbitrary clipping level, the central claim is currently controlled by the choice of cut rather than demonstrated to be an intrinsic property of the binary sample. I ask for a robustness scan over the rejection threshold (for example 3, 4, 5, and 6 sigma), results with and without HD 27149, and a statement of the range of PZPO values spanned by these choices.
  2. [Sec. 3.1] The P < 100-day period cutoff is introduced with only the qualitative remark that short-period NSS solutions are hard to solve, and it is not derived from the MCMC simulations. The final-selection sample of 44 binaries is therefore defined by this arbitrary period boundary, while the 'remaining' sample of 88 binaries mixes long-period binaries that are 'good' by the MCMC criterion with binaries that failed the MCMC test. The comparison between -38.9 and -58.0 microas consequently conflates the period cut with orbital-motion quality. I request a period-cutoff scan (for example 50, 100, 200, and 500 days) and, if possible, a 'remaining' sample restricted to long-period 'good' binaries so that the two subsets differ only in orbital period.
  3. [Appendix B and Sec. 3.1] The MCMC classification is the load-bearing filter for selecting binaries that are unaffected by orbital motion, but its accuracy is not validated. The mock observations rely on the Everall et al. (2021) Gaussian along-scan error model and on the adopted orbital elements, and the 'good' criterion |pi_GDR3 - pi_simu|/pi_GDR3 < 0.2 at 95% confidence is an ad hoc tolerance. If the noise model or the orbital elements are wrong, binaries can be misclassified and the final-selection PZPO becomes biased. Please add a validation experiment, for example injecting binaries into the Gaia observation schedule and comparing recovered single-star parallaxes against the NSS solutions, or at minimum test the stability of the final PZPO to the goodness threshold (0.1, 0.2, 0.3).
  4. [Sec. 4.1 and Fig. 7] The uncertainty underestimation factor of about 2.0 is derived from a single Gaussian fit to the normalized residuals of the 132 filtered binaries (Figure 7), but those same normalized residuals are used in the 5-sigma rejection criterion of Eq. (1)(iv), so the fitted sigma is not independent of the selection. Multiplying the formal errors of the 44- and 88-source subsets by the same factor also assumes a homogeneous underestimate across period and magnitude. The paper should present the corrected uncertainties as a range (for example from bootstrap and from fits excluding the rejected sources) and state explicitly whether the corrected errors affect the significance of the -38.9 microas value.
minor comments (5)
  1. [Sec. 3.1] Typo: 'Appenix B' should read 'Appendix B'.
  2. [Fig. 7 caption] The caption contains an unbalanced parenthesis in the expression for Delta-pi/sigma; it should read |pi_GDR3 - pi_Orb| / sqrt(sigma_GDR3^2 + sigma_Orb^2).
  3. [Appendix B, Eq. (B11)] The likelihood compares eta_fit with eta_obs, while the mock observations are denoted eta_sim in Eq. (B9); please clarify whether eta_obs is intended to be eta_sim.
  4. [Sec. 2.1] The sentence 'only one system ROXs 47A have no WDS' should be 'has no WDS', and the abbreviation NSS should be expanded at first use.
  5. [Table 2] Table 2 would benefit from a column giving the uncertainty-inflation-corrected values, since the text in Sec. 4.1 quotes corrected uncertainties only in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PZPO is measured against independent orbital, VLBI, and HST parallaxes; no fitted parameter enters the central weighted mean.

full rationale

The claimed bright-magnitude GDR3 parallax zero-point offset is an inverse-variance weighted mean of (pi_GDR3 - pi_external) for 44 binaries with orbital parallaxes, 45 VLBI sources, and 52 HST sources; these external parallaxes come from independent orbit solutions, VLBI astrometry, and HST trigonometric parallaxes and are not functions of the GDR3 parallaxes being tested. The MCMC step is a selection filter, not a parameter fit feeding the mean: the 'good' flag is defined by |pi_GDR3 - pi_simu|/pi_GDR3 < 0.2, and the final weighted means in Table 2 are computed directly from the retained parallax differences. The only fitted quantity, the ~2.0 Gaussian sigma for Delta pi/sigma, calibrates the uncertainty inflation and does not enter the weighted-mean PZPO. The paper also discloses that restoring four 5-sigma-rejected binaries flips the binary PZPO to +7.9 +/- 10.0 microarcseconds; this is a sample-selection sensitivity, not definitional circularity. The self-citations (Ding et al. 2024; Liao et al. 2021) are contextual literature summaries and are not load-bearing for the measurement, so no circular step is present.

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

The central measurement rests on external orbital-parallax catalogs and on two domain-specific simulation tools (the Everall error model and GOST); the only paper-specific tuning parameters are the 100-day period cutoff, the 20% MCMC tolerance, and the uncertainty inflation factor. No new physical entities are introduced.

free parameters (4)
  • Orbital-period cutoff = 100 days
    Binaries with P<100 days are selected as the final sample (Section 3.1); the cutoff is a hand-chosen threshold to exclude systems where orbital motion might contaminate Gaia astrometry, and changing it changes the PZPO.
  • MCMC 'good' threshold = 20% at 95% confidence
    A binary is 'good' if |pi_GDR3 - pi_simu|/pi_GDR3 < 0.2 with 95% confidence (Section 3.1); this ad hoc tolerance defines which binaries are kept.
  • Uncertainty underestimation factor = 2.07 (Gaussian fit), median 1.80 (bootstrap)
    Fitted to the Δπ/σ distribution of the 132 filtered binaries (Figure 7, Section 4.1); used to inflate the PZPO uncertainties to about 14.4, 20.6, and 20.2 μas.
  • Filtering thresholds in Eq. (1) = 5σ, 2 mas, params solved 31/95
    Hand-chosen quality cuts for the binary sample (Section 2.1); they remove 117 of the 249 systems.
assumptions (4)
  • domain assumption Orbital parallaxes from visual and spectroscopic orbits are unbiased and independent of Gaia.
    The whole method assumes literature orbital parallaxes (Piccotti et al. 2020, G23, etc.) have no systematic offset relative to true parallax; no independent validation is given here.
  • domain assumption The Everall et al. (2021) AL astrometric error model accurately describes GDR3 bright-star single-observation errors.
    Used in Eq. (B9) to generate mock observations in the MCMC simulation (Appendix B); if the error model is wrong, the 'good' classification is unreliable.
  • domain assumption The Gaia Observation Forecast Tool (GOST) correctly predicts the observation times, scan angles, and parallax factors for each source.
    Transit times and parallax factors from GOST feed Eq. (B1); incorrect scheduling predictions would bias the simulated orbital effect.
  • ad hoc to paper The 20% tolerance in the 'good' criterion separates binaries with negligible orbital effect from those with significant effect.
    The threshold is not derived from first principles; it is a chosen tolerance (Section 3.1).

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Cite this review

Pith. "Pith review of Analysis of the Gaia Data Release 3 parallax bias at bright magnitudes." pith.science (2026). https://pith.science/paper/XHK4TSHL

@misc{pith2026250208068,
  author       = {Pith},
  title        = {Pith review of: Analysis of the Gaia Data Release 3 parallax bias at bright magnitudes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XHK4TSHL}},
  note         = {Machine review of arXiv:2502.08068}
}
abstract

The combination of visual and spectroscopic orbits in binary systems enables precise distance measurements without additional assumptions, making them ideal for examining the parallax zero-point offset (PZPO) at bright magnitudes (G < 13) in Gaia. We compiled 249 orbital parallaxes from 246 binary systems and used Markov Chain Monte Carlo (MCMC) simulations to exclude binaries where orbital motion significantly impacts parallaxes. After removing systems with substantial parallax errors, large discrepancies between orbital and Gaia parallaxes, and selecting systems with orbital periods under 100 days, a final sample of 44 binaries was retained.The weighted mean PZPO for this sample is -38.9 $\pm$ 10.3 $\mu$as, compared to -58.0 $\pm$ 10.1 $\mu$as for the remaining systems, suggesting that orbital motion significantly affects parallax measurements. These formal uncertainties of the PZPO appear to be underestimated by a factor of approximately 2.0. For bright stars with independent trigonometric parallaxes from VLBI and HST, the weighted mean PZPOs are -14.8 $\pm$ 10.6 and -31.9 $\pm$ 14.1 $\mu$as, respectively. Stars with $G \leq 8$ exhibit a more pronounced parallax bias, with some targets showing unusually large deviations, likely due to systematic calibration errors in Gaia for bright stars. The orbital parallaxes dataset compiled in this work serves as a vital resource for validating parallaxes in future Gaia data releases.

Figures

Figures reproduced from arXiv: 2502.08068 by the authors.

Figure 1
Figure 1. Histogram of the magnitude for binaries from this work (TW, 249), G23 (186), and overlapped sample (157). been identified as binary in GDR3, likely due to GDR3 fo￾cusing on the most significant systems. To investigate the PZPO, quality filtering is required. For 249 entries, we apply the following criteria:    (i) astrometric params solved = 31 or 95, (ii) σπOrb < 5 ∗ σπGDR3 , (iii) σπOrb , σπ… view at source ↗
Figure 2
Figure 2. Map of the difference (∆π) of GDR3 parallax minus ex￾ternal parallax in Galactic coordinate for the filtered samples from this work (TW, 132), VLBI (64), and HST (55), respectively. The dotted lines represent the Galactic latitude ±20◦ . listed in GDR3 (Polaris A), and 8 have no parallax listed in GDR3. Among the left 102 objects, 5 have a non-zero NSS flag. For VLBI and HST sources, we apply the following crite￾ria… view at source ↗
Figure 3
Figure 3. shows the parallax difference of GDR3 parallax minus orbital parallax for the filtered binaries (132), plot￾ted against magnitude, Bp-Rp color, and renormalised unit weight error (RUWE), respectively. The magnitude ranges from G ≃ 2.28 to 12.55. 65 are brighter than G = 6. The Bp￾Rp color spans from GBP−GRP ≃ -0.12 to 2.78, with a median value of 0.75. The RUWE value, indicating the goodness-of￾fit of the single-sta… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Orbital parallax for the ‘good’ binaries (93) plotted against GDR3 parallax. The size of each data point is proportional to the semi-major axis, while the color of the points corresponds to the orbital period. We try to minimize the orbital effect in the PZPO by se￾lec…
Figure 5
Figure 5. Figure 5: shows the weighted mean PZPO plotted against the number of the final selection and remaining binaries. We rank the 44 and remaining 88 binaries based on their abso￾lute values of the parallax differences, iteratively removing the source with the largest value. At each …
Figure 7
Figure 7. Figure 7: Distributions of uncertainty normalized parallax differ￾ence ∆π/σ∆ = (πGDR3 − πOrb)|/ p σπGDR3 2 + σπOrb 2 for 132 filtered binaries. The quantity would be expected to follow a Gaussian dis￾tribution with σ = 1 (dotted lines) if the formal parallax uncertain￾ties were …
Figure 8
Figure 8. Figure 8: The PZPO from various studies and the final selection in this work, plotted against the median magnitude of the objects used in each study. spanning magnitudes of 8 ≲ G ≲ 17. With the exception of G23 and our work, other studies used the various objects with derived pa…

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Reference graph

Works this paper leans on

44 extracted references · 17 canonical work pages · cited by 2 Pith papers

  1. [1]

    A., Claver \' a , R

    Anguita-Aguero , J., Mendez , R. A., Claver \' a , R. M., & Costa , E. 2022, , 163, 118, 10.3847/1538-3881/ac478c

    work page doi:10.3847/1538-3881/ac478c 2022

  2. [2]

    F., McAlister , H

    Bagnuolo , William G., J., Taylor , S. F., McAlister , H. A., et al. 2006, , 131, 2695, 10.1086/501232

    work page doi:10.1086/501232 2006

  3. [3]

    2021, , 909, 200, 10.3847/1538-4357/abdf48

    Bhardwaj , A., Rejkuba , M., de Grijs , R., et al. 2021, , 909, 200, 10.3847/1538-4357/abdf48

    doi:10.3847/1538-4357/abdf48 2021

  4. [4]

    F., Torres , G., & Latham , D

    Boden , A. F., Torres , G., & Latham , D. W. 2006, , 644, 1193, 10.1086/503793

    work page doi:10.1086/503793 2006

  5. [5]

    S., Monteiro , H., Caetano , T

    Dias , W. S., Monteiro , H., Caetano , T. C., et al. 2014, , 564, A79, 10.1051/0004-6361/201323226

    doi:10.1051/0004-6361/201323226 2014

  6. [6]

    2024, , 691, A81, 10.1051/0004-6361/202450967

    Ding , Y., Liao , S., Wu , Q., Qi , Z., & Tang , Z. 2024, , 691, A81, 10.1051/0004-6361/202450967

    doi:10.1051/0004-6361/202450967 2024

  7. [7]

    El-Badry , K., Rix , H.-W., & Heintz , T. M. 2021, , 506, 2269, 10.1093/mnras/stab323

    doi:10.1093/mnras/stab323 2021

  8. [8]

    E., Smith , L., & Holl , B

    Everall , A., Boubert , D., Koposov , S. E., Smith , L., & Holl , B. 2021, , 502, 1908, 10.1093/mnras/stab041

    doi:10.1093/mnras/stab041 2021

Show all 44 references
  1. [9]

    2021, , 649, A5, 10.1051/0004-6361/202039834

    Fabricius , C., Luri , X., Arenou , F., et al. 2021, , 649, A5, 10.1051/0004-6361/202039834

    2021 doi

  2. [10]

    D., ten Brummelaar , T

    Farrington , C. D., ten Brummelaar , T. A., Mason , B. D., et al. 2014, , 148, 48, 10.1088/0004-6256/148/3/48

    2014 doi

  3. [11]

    W., Lang , D., & Goodman , J

    Foreman-Mackey , D., Hogg , D. W., Lang , D., & Goodman , J. 2013, , 125, 306, 10.1086/670067

    2013 doi

  4. [12]

    Gaia Collaboration , Brown , A. G. A., Vallenari , A., et al. 2021 a , , 649, A1, 10.1051/0004-6361/202039657

    2021 doi

  5. [13]

    L., Sarro , L

    Gaia Collaboration , Smart , R. L., Sarro , L. M., et al. 2021 b , , 649, A6, 10.1051/0004-6361/202039498

    2021 doi

  6. [14]

    2023, , 672, A119, 10.1051/0004-6361/202245712

    Gallenne , A., M \'e rand , A., Kervella , P., et al. 2023, , 672, A119, 10.1051/0004-6361/202245712

    2023 doi

  7. [15]

    Groenewegen , M. A. T. 2021, , 654, A20, 10.1051/0004-6361/202140862

    2021 doi

  8. [16]

    2023, , 669, A4, 10.1051/0004-6361/202244479

    ---. 2023, , 669, A4, 10.1051/0004-6361/202244479

    2023 doi

  9. [17]

    2023, , 674, A10, 10.1051/0004-6361/202244161

    Holl , B., Sozzetti , A., Sahlmann , J., et al. 2023, , 674, A10, 10.1051/0004-6361/202244161

    2023 doi

  10. [18]

    C., & Zhang , H

    Huang , Y., Yuan , H., Beers , T. C., & Zhang , H. 2021, , 910, L5, 10.3847/2041-8213/abe69a

    2021 doi

  11. [19]

    V., Piskunov , A

    Kharchenko , N. V., Piskunov , A. E., Schilbach , E., R \"o ser , S., & Scholz , R. D. 2013, , 558, A53, 10.1051/0004-6361/201322302

    2013 doi

  12. [20]

    2005, , 626, 431, 10.1086/429880

    Konacki , M. 2005, , 626, 431, 10.1086/429880

    2005 doi

  13. [21]

    Konacki , M., & Lane , B. F. 2004, , 610, 443, 10.1086/421037

    2004 doi

  14. [22]

    V., Fekel , F

    Lester , K. V., Fekel , F. C., Muterspaugh , M., et al. 2020, , 160, 58, 10.3847/1538-3881/ab8f95

    2020 doi

  15. [23]

    2021, , 133, 094501, 10.1088/1538-3873/ac1eeb

    Liao , S., Wu , Q., Qi , Z., et al. 2021, , 133, 094501, 10.1088/1538-3873/ac1eeb

    2021 doi

  16. [24]

    A., Hern \'a ndez , J., et al

    Lindegren , L., Klioner , S. A., Hern \'a ndez , J., et al. 2021 a , , 649, A2, 10.1051/0004-6361/202039709

    2021 doi

  17. [25]

    2021 b , , 649, A4, 10.1051/0004-6361/202039653

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

    2021 doi

  18. [26]

    D., Meilland , A., et al

    Mourard , D., Monnier , J. D., Meilland , A., et al. 2015, , 577, A51, 10.1051/0004-6361/201425141

    2015 doi

  19. [27]

    \'A ., & Lindegren , L

    Perryman , M., Hartman , J., Bakos , G. \'A ., & Lindegren , L. 2014, , 797, 14, 10.1088/0004-637X/797/1/14

    2014 doi

  20. [28]

    \'A ., Carini , R., et al

    Piccotti , L., Docobo , J. \'A ., Carini , R., et al. 2020, , 492, 2709, 10.1093/mnras/stz3616

    2020 doi

  21. [29]

    2000, , 145, 215, 10.1051/aas:2000237

    Pourbaix , D. 2000, , 145, 215, 10.1051/aas:2000237

    2000 doi

  22. [30]

    J., & Honma , M

    Reid , M. J., & Honma , M. 2014, , 52, 339, 10.1146/annurev-astro-081913-040006

    2014 doi

  23. [31]

    2021, , 911, L20, 10.3847/2041-8213/abf359

    Ren , F., Chen , X., Zhang , H., et al. 2021, , 911, L20, 10.3847/2041-8213/abf359

    2021 doi

  24. [32]

    G., Casertano , S., Yuan , W., et al

    Riess , A. G., Casertano , S., Yuan , W., et al. 2021, , 908, L6, 10.3847/2041-8213/abdbaf

    2021 doi

  25. [33]

    G., & Torres , G

    Stassun , K. G., & Torres , G. 2021, , 907, L33, 10.3847/2041-8213/abdaad

    2021 doi

  26. [34]

    Taylor , M. B. 2005, in Astronomical Society of the Pacific Conference Series, Vol. 347, Astronomical Data Analysis Software and Systems XIV, ed. P. Shopbell , M. Britton , & R. Ebert , 29

    2005

  27. [35]

    2016, , 152, 138, 10.3847/0004-6256/152/5/138

    Tokovinin , A. 2016, , 152, 138, 10.3847/0004-6256/152/5/138

    2016 doi

  28. [36]

    D., & Hartkopf , W

    Tokovinin , A., Mason , B. D., & Hartkopf , W. I. 2014, , 147, 123, 10.1088/0004-6256/147/5/123

    2014 doi

  29. [37]

    2022 a , , 163, 149, 10.3847/1538-3881/ac4dec

    Wang , C., Yuan , H., & Huang , Y. 2022 a , , 163, 149, 10.3847/1538-3881/ac4dec

    2022 doi

  30. [38]

    2022 b , , 665, A111, 10.1051/0004-6361/202243684

    Wang , Y., Liao , S., Giacobbo , N., et al. 2022 b , , 665, A111, 10.1051/0004-6361/202243684

    2022 doi

  31. [39]

    T., & Howard , A

    Wright , J. T., & Howard , A. W. 2009, , 182, 205, 10.1088/0067-0049/182/1/205

    2009 doi

  32. [40]

    J., Zheng , X., & Wang , G

    Xu , S., Zhang , B., Reid , M. J., Zheng , X., & Wang , G. 2019, , 875, 114, 10.3847/1538-4357/ab0e83

    2019 doi

  33. [41]

    Zinn , J. C. 2021, , 161, 214, 10.3847/1538-3881/abe936

    2021 doi

  34. [42]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.stat...

  35. [43]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.d...

  36. [44]

    N2۽GHL’,<Ϋ yrV ! BaFs B ! B !D' B !<I B IB B6O

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' en...

    2021

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.