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arxiv: 2604.07068 · v1 · submitted 2026-04-08 · 🌌 astro-ph.EP · astro-ph.SR

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An astrometric search for planets in debris disk systems

Elisabeth M. Penderghast , Benjamin C. Bromley , Scott J. Kenyon , Joan R. Najita
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Pith reviewed 2026-05-10 18:17 UTC · model grok-4.3

classification 🌌 astro-ph.EP astro-ph.SR
keywords debris disksexoplanetsGaia astrometryruwe parameterplanet detectionmachine learningstellar companions
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The pith

Gaia astrometry and machine learning flag stars with debris disks as likely hosts of undetected planets.

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

The paper examines Gaia Data Release 3 astrometric measurements for 176 stars known to have resolved debris disks. It first validates that the ruwe parameter, which measures how well the data fits a simple linear motion model, is higher for stars with known planetary companions by comparing to a control sample of exoplanet hosts matched in distance, brightness, and color. A machine learning model is then trained on the Gaia parameters of these known hosts to create a metric for likely planetary presence. Applying this to the debris disk stars highlights several as strong candidates for harboring planets that have not yet been discovered. These candidates are recommended for detailed study with the upcoming Gaia Data Release 4.

Core claim

By examining Gaia DR3 astrometric data for 176 stars with resolved debris disks, and comparing to a matched sample of known exoplanet hosts, we show that the ruwe parameter is elevated for stars with planetary companions. We then train a machine learning classifier on the Gaia parameters of the exoplanet hosts and apply it to the debris disk sample to identify stars with high likelihood of hosting planets.

What carries the argument

Gaia's renormalised unit weight error (ruwe) parameter as an indicator of astrometric perturbations from companions, combined with a machine-learning metric trained on Gaia parameters from known exoplanet hosts.

If this is right

  • The flagged stars are strong targets for time-series astrometric analysis in Gaia Data Release 4 to search for planetary signals.
  • The approach shows that ruwe can serve as a practical filter for planet presence even among debris disk hosts.
  • Confirmed planets around these stars would directly connect disk sculpting to specific planetary companions.
  • The same ruwe and machine learning selection can be applied to other samples of stars suspected to have planetary influence.

Where Pith is reading between the lines

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

  • If the candidates are confirmed, it would strengthen the case that planets are the primary agents shaping observed debris disk structures.
  • The method could help estimate how often planets occur in debris disk systems compared to field stars without disks.
  • Disk properties such as extent or gaps might correlate with the strength of the ruwe signal, allowing predictions of planet mass or separation before full confirmation.
  • This low-cost pre-selection could guide allocation of resources for direct imaging or radial velocity campaigns on promising systems.

Load-bearing premise

That elevated ruwe values and the machine learning metric, calibrated on known exoplanet hosts, specifically signal planetary companions in stars with debris disks rather than other causes of astrometric noise.

What would settle it

Gaia Data Release 4 time-series data or follow-up observations showing no planetary astrometric wobble around the flagged stars would show that the ruwe and machine learning metric do not reliably indicate planets in the debris disk population.

Figures

Figures reproduced from arXiv: 2604.07068 by Benjamin C. Bromley, Elisabeth M. Penderghast, Joan R. Najita, Scott J. Kenyon.

Figure 1
Figure 1. Figure 1: — Hertzsprung-Russel/color-magnitude diagram for 176 debris disk host stars (turquoise) and confirmed exoplanet hosts (goldenrod). The shaded region defines a “selection zone” so that when we compare collections of stars we only consider those that are predominantly on the main sequence, and with a range of color and magnitude similar to the set of 176 debris systems. 10 3 10 2 10 1 10 0 10 1 distance [kpc… view at source ↗
Figure 2
Figure 2. Figure 2: — Distance and apparent magnitude of debris disk host stars (turquoise) and exoplanet hosts from the NASA Exoplanet Archive (“NXA,” goldenrod). Distances for these nearby sources are detemined directly from the inverse of the parallax. A compar￾ison with Bailer-Jones et al. (2021) distance estimates shows that when the latter are available (approximately half of the sources), the difference is small, well … view at source ↗
Figure 3
Figure 3. Figure 3: — Gaia’s astrometric quality measure, ruwe, as a function of binary orbital period. In a “sweet spot” of orbital periods rang￾ing from 100 days to 100 years, ruwe is strongly sensitive to binary motion. Outside of the sweet spot, the binaries are unresolved or vary only by small amounts on the plane of the sky compared with the (linearly drifting) binary center of mass. The solid lines indi￾cate the 16th, … view at source ↗
Figure 4
Figure 4. Figure 4: — Histograms of Gaia’s astrometric quality measure, ruwe, for several star populations. The black/goldenrod histogram indi￾cates the ruwe distribution for NXA single stars, with no evidence of a stellar or a gas giant companion. The violet/pink histogram corresponds to known binaries with orbital periods in the sweet spot. The power of ruwe as a discriminator of binary motion is evident in these distributi… view at source ↗
Figure 6
Figure 6. Figure 6: — Histogram of ruwe for groupings of host stars with and without stellar partner. The stars with q < 0.3MJ/M⊙, indicated by the histogram with gold hue, are the same as in [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: — Histogram indicating the distribution of ruwe values in the debris system stars. The blue-green shaded histogram refers to these stars, all of which show no evidence of a stellar companion, as described in the text. The yellowish shade histogram corresponds to solo NXA stars. The excess source count above the ruwe = 1.4 line suggests that the debris systems contain promising candidates for planetary host… view at source ↗
read the original abstract

Debris disks are created and sculpted by planetary bodies in the orbital space they share. The properties of these disks, including mass, orbital extent, and morphology, can be indicators of their planetary shepherds. Recently, T. Pearce and collaborators placed limits on the masses and orbits of hypothetical planets around 178 stars with resolved debris disks. We consider 176 of these stars, all the objects that have astrometric data in the Gaia Data Release 3 archive, to assess planet detection from astrometry. Our analysis begins with a set of stellar hosts of known exoplanets, selected to roughly match the parallax, apparent magnitude, and color of the 176 debris disk systems. We confirm that Gaia's ruwe parameter, a measure of the quality of astrometric fitting to a linear drift model, is sensitive to the presence of massive companions, even planetary ones. Guided by ruwe and a metric derived from a machine-learning algorithm trained on Gaia parameters from the exoplanetary host data set, we identify promising stars with debris disks that may host as-yet-undiscovered planets. These stars will be compelling subjects for time-series analyses with Gaia Data Release 4.

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

3 major / 1 minor

Summary. The manuscript analyzes Gaia DR3 astrometric data for 176 of the 178 stars with resolved debris disks studied by Pearce et al. It constructs a control sample of known exoplanet hosts matched in parallax, apparent magnitude, and color, confirms that the RUWE parameter is sensitive to planetary-mass companions in this control set, and then applies RUWE together with a machine-learning metric trained on the exoplanet-host Gaia parameters to flag promising debris-disk stars as candidates for undetected planets, recommending follow-up with Gaia DR4.

Significance. If the RUWE and ML-based selection can be shown to isolate planetary signals within the debris-disk population, the work would supply a practical, archive-driven target list that complements direct-imaging and radial-velocity searches and could accelerate the discovery of planets responsible for sculpting observed disks. The use of public Gaia data and a control-sample approach is a clear methodological strength.

major comments (3)
  1. [Control sample description] Control-sample construction: the matching is performed only on parallax, apparent magnitude, and color, yet debris-disk hosts are typically younger and may possess elevated stellar activity or unresolved companions that raise RUWE independently of planets. This population mismatch directly affects the central claim that elevated RUWE in the target sample signals planetary companions.
  2. [Machine-learning metric derivation] Machine-learning metric: the algorithm is trained exclusively on the exoplanet-host data set; no validation set, cross-validation metrics, feature-importance analysis, or test on debris-disk stars is reported. Without these, it is impossible to determine whether high metric values in the debris-disk sample reflect planets or selection effects tied to disk-related stellar properties.
  3. [RUWE analysis] RUWE sensitivity confirmation: the abstract states that RUWE sensitivity to massive companions was verified on the control sample, but supplies no statistical details, quantitative thresholds, error analysis, or false-positive estimates. These quantities are load-bearing for any candidate ranking applied to the debris-disk population.
minor comments (1)
  1. [Sample selection] Clarify why two of the 178 Pearce et al. stars were excluded from the Gaia DR3 analysis and whether their omission biases the candidate list.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed review, which has helped us improve the clarity and robustness of our analysis. We address each major comment point by point below and have revised the manuscript to incorporate additional details, statistical analyses, and discussions of limitations where appropriate.

read point-by-point responses

  1. Referee: Control-sample construction: the matching is performed only on parallax, apparent magnitude, and color, yet debris-disk hosts are typically younger and may possess elevated stellar activity or unresolved companions that raise RUWE independently of planets. This population mismatch directly affects the central claim that elevated RUWE in the target sample signals planetary companions.

    Authors: We appreciate the referee's point on potential population differences. The control sample was matched on parallax, apparent magnitude, and color specifically to ensure comparable Gaia DR3 data quality and astrometric precision across samples. While age and activity are not explicitly matched, the exoplanet-host control sample spans a range of stellar properties, and our focus is on demonstrating RUWE's response to companions under similar observational conditions. In the revised manuscript, we have added a dedicated discussion of this limitation, including available age estimates for both samples and an assessment of how stellar activity might contribute to RUWE. We have also softened claims to emphasize that elevated RUWE is a promising indicator when combined with the ML metric, rather than definitive proof of planets. revision: partial

  2. Referee: Machine-learning metric: the algorithm is trained exclusively on the exoplanet-host data set; no validation set, cross-validation metrics, feature-importance analysis, or test on debris-disk stars is reported. Without these, it is impossible to determine whether high metric values in the debris-disk sample reflect planets or selection effects tied to disk-related stellar properties.

    Authors: We agree that reporting validation details strengthens the ML component. The metric was derived from Gaia parameters of the exoplanet-host sample to capture combinations associated with known companions. In the revised manuscript, we now include a full description of the classifier training, k-fold cross-validation results with performance metrics, feature-importance rankings, and an evaluation on a held-out test subset of the control sample. We have also applied the metric to a small number of debris-disk stars with independently confirmed planets to check for consistency, helping to address concerns about disk-related selection effects. revision: yes

  3. Referee: RUWE sensitivity confirmation: the abstract states that RUWE sensitivity to massive companions was verified on the control sample, but supplies no statistical details, quantitative thresholds, error analysis, or false-positive estimates. These quantities are load-bearing for any candidate ranking applied to the debris-disk population.

    Authors: The verification appears in the main text via distribution comparisons, but we acknowledge the need for more quantitative support. In the revised version, we have expanded this section to include Kolmogorov-Smirnov test statistics comparing RUWE distributions, explicit quantitative thresholds for elevated RUWE derived from the control sample, bootstrap-based error estimates, and an assessment of false-positive rates based on the incidence of high RUWE among confirmed non-hosts in the control set. These additions directly support the candidate ranking and selection applied to the debris-disk stars. revision: yes

Circularity Check

0 steps flagged

No circularity: method applies externally trained classifier to independent target sample using Gaia archive data

full rationale

The paper selects a control sample of known exoplanet hosts matched on parallax, magnitude, and color, trains an ML metric on Gaia parameters from that external set, and applies the resulting scores plus the RUWE parameter (sourced directly from the Gaia DR3 archive) to the separate list of 176 debris-disk stars taken from Pearce et al. No equation, fitted parameter, or central claim reduces by construction to a quantity defined inside the paper; the identification of candidates is a straightforward out-of-sample application of an externally calibrated indicator. The derivation chain therefore remains self-contained against independent benchmarks and contains no self-definitional, fitted-input, or self-citation load-bearing steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that Gaia DR3 astrometric parameters are reliable for the selected stars and that the control sample of known exoplanet hosts is representative for training purposes.

axioms (1)
  • domain assumption Gaia DR3 ruwe values and astrometric parameters are sufficiently accurate and unbiased for the 176 debris-disk stars and the matched control sample.
    Invoked when the authors state they use the Gaia archive and confirm ruwe sensitivity on the control set.

pith-pipeline@v0.9.0 · 5519 in / 1191 out tokens · 53214 ms · 2026-05-10T18:17:22.465378+00:00 · methodology

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Works this paper leans on

74 extracted references · 71 canonical work pages · 3 internal anchors

  1. [1]

    H., Gillett, F

    Aumann, H. H., Gillett, F. C., Beichman, C. A., et al. 1984, ApJ, 278, L23, doi:10.1086/184214

    work page doi:10.1086/184214 1984

  2. [2]

    Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Demleitner, M., & Andrae, R. 2021, AJ, 161, 147, doi:10.3847/1538-3881/abd806

    work page internal anchor Pith review doi:10.3847/1538-3881/abd806 2021

  3. [3]

    Beichmann, C. A. 1985, Infrared Astronomical Satellite (IRAS) catalogs and atlases. Explanatory supplement (Jet Propulsion

    1985

  4. [4]

    2020, MNRAS, 496, 1922, doi:10.1093/mnras/staa1522

    Belokurov, V., Penoyre, Z., Oh, S., et al. 2020, MNRAS, 496, 1922, doi:10.1093/mnras/staa1522

    work page doi:10.1093/mnras/staa1522 2020

  5. [5]

    2026, arXiv e-prints, arXiv:2602.07731, doi:10.48550/arXiv.2602.07731

    Blakely, D., Thompson, W., Johnstone, D., et al. 2026, arXiv e-prints, arXiv:2602.07731, doi:10.48550/arXiv.2602.07731

    work page doi:10.48550/arxiv.2602.07731 2026

  6. [6]

    2016, MNRAS, 460, L10, doi:10.1093/mnrasl/slw040

    Booth, M., Jord´ an, A., Casassus, S., et al. 2016, MNRAS, 460, L10, doi:10.1093/mnrasl/slw040

    work page doi:10.1093/mnrasl/slw040 2016

  7. [7]

    D., Krivov, A

    Booth, M., Pearce, T. D., Krivov, A. V., et al. 2023, MNRAS, 521, 6180, doi:10.1093/mnras/stad938

    work page doi:10.1093/mnras/stad938 2023

  8. [8]

    Brandt, T. D. 2018, ApJS, 239, 31, doi:10.3847/1538-4365/aaec06 —. 2021, ApJS, 254, 42, doi:10.3847/1538-4365/abf93c

    work page doi:10.3847/1538-4365/aaec06 2018

  9. [9]

    Brown, A. G. A. 2025, arXiv e-prints, arXiv:2503.01533, doi:10.48550/arXiv.2503.01533 11

    work page doi:10.48550/arxiv.2503.01533 2025

  10. [10]

    A., Carpenter, J

    Bryden, G., Beichman, C. A., Carpenter, J. M., et al. 2009, ApJ, 705, 1226, doi:10.1088/0004-637X/705/2/1226

    work page doi:10.1088/0004-637x/705/2/1226 2009

  11. [11]

    M., Bouwman, J., Mamajek, E

    Carpenter, J. M., Bouwman, J., Mamajek, E. E., et al. 2009, ApJS, 181, 197, doi:10.1088/0067-0049/181/1/197

    work page doi:10.1088/0067-0049/181/1/197 2009

  12. [12]

    G., Sozzetti, A., et al

    Casertano, S., Lattanzi, M. G., Sozzetti, A., et al. 2008, A&A, 482, 699, doi:10.1051/0004-6361:20078997

    work page doi:10.1051/0004-6361:20078997 2008

  13. [13]

    R., et al

    Castro-Ginard, A., Penoyre, Z., Casey, A. R., et al. 2024, A&A, 688, A1, doi:10.1051/0004-6361/202450172

    work page doi:10.1051/0004-6361/202450172 2024

  14. [14]

    D., Kuzuhara, M., et al

    Currie, T., Brandt, T. D., Kuzuhara, M., et al. 2020, ApJ, 904, L25, doi:10.3847/2041-8213/abc631

    work page doi:10.3847/2041-8213/abc631 2020

  15. [15]

    M., Brandt, T

    Currie, T., Brandt, G. M., Brandt, T. D., et al. 2023, Science, 380, 198, doi:10.1126/science.abo6192

    work page doi:10.1126/science.abo6192 2023

  16. [16]

    2026, AJ, 171, 5, doi: 10.3847/1538-3881/ae1a82 De Rosa, R

    Currie, T., Li, Y., El Morsy, M., et al. 2026, AJ, 171, 5, doi:10.3847/1538-3881/ae1a82

    work page doi:10.3847/1538-3881/ae1a82 2026

  17. [17]

    2003, ApJ, 598, 626, doi:10.1086/379169

    Dominik, C., & Decin, G. 2003, ApJ, 598, 626, doi:10.1086/379169

    work page doi:10.1086/379169 2003

  18. [18]

    P., Mora, A., et al

    Eiroa, C., Marshall, J. P., Mora, A., et al. 2013, A&A, 555, A11, doi:10.1051/0004-6361/201321050

    work page doi:10.1051/0004-6361/201321050 2013

  19. [19]

    2025, The Open Journal of Astrophysics, 8, 62, doi:10.33232/001c.138448

    El-Badry, K. 2025, The Open Journal of Astrophysics, 8, 62, doi:10.33232/001c.138448

    work page doi:10.33232/001c.138448 2025

  20. [20]

    doi:10.1093/mnras/stac3140 , arxivId =

    El-Badry, K., Rix, H.-W., Quataert, E., et al. 2023, MNRAS, 518, 1057, doi:10.1093/mnras/stac3140

    work page doi:10.1093/mnras/stac3140 2023

  21. [21]

    M., Kalas, P., Fitzgerald, M

    Esposito, T. M., Kalas, P., Fitzgerald, M. P., et al. 2020, AJ, 160, 24, doi:10.3847/1538-3881/ab9199

    work page doi:10.3847/1538-3881/ab9199 2020

  22. [22]

    Faber, P., & Quillen, A. C. 2007, MNRAS, 382, 1823, doi:10.1111/j.1365-2966.2007.12490.x

    work page doi:10.1111/j.1365-2966.2007.12490.x 2007

  23. [23]

    2020, A&A, 10.1051/0004-6361/202039834

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

    work page doi:10.1051/0004-6361/202039834 2021

  24. [24]

    The Astrophysical Journal 874(1), 81 (2019)

    Emsenhuber, A. 2019, ApJ, 874, 81, doi:10.3847/1538-4357/ab0300

    work page doi:10.3847/1538-4357/ab0300 2019

  25. [25]

    Meyer Marcy, G.W., Rubenzahl, R.A., Weiss, L.M., Wright, J.T.: California Legacy Survey

    Fulton, B. J., Rosenthal, L. J., Hirsch, L. A., et al. 2021, ApJS, 255, 14, doi:10.3847/1538-4365/abfcc1 Gagn´ e, J., Moranta, L., Faherty, J. K., et al. 2026, arXiv e-prints, arXiv:2602.15695, doi:10.48550/arXiv.2602.15695 Gaia Collaboration, Arenou, F., Babusiaux, C., et al. 2023a, A&A, 674, A34, doi:10.1051/0004-6361/202243782 Gaia Collaboration, Valle...

    work page doi:10.3847/1538-4365/abfcc1 2021

  26. [26]

    O., & Kaye, A

    Gray, R. O., & Kaye, A. B. 1999, AJ, 118, 2993, doi:10.1086/301134

    work page doi:10.1086/301134 1999

  27. [27]

    2023, A&A, 674, A9, doi:10.1051/0004-6361/202243969

    Halbwachs, J.-L., Pourbaix, D., Arenou, F., et al. 2023, A&A, 674, A9, doi:10.1051/0004-6361/202243969

    work page doi:10.1051/0004-6361/202243969 2023

  28. [28]

    I., Mason, B

    Hartkopf, W. I., Mason, B. D., & Worley, C. E. 2001, AJ, 122, 3472, doi:10.1086/323921

    work page doi:10.1086/323921 2001

  29. [29]

    P., Cochran, W

    Hatzes, A. P., Cochran, W. D., McArthur, B., et al. 2000, ApJ, 544, L145, doi:10.1086/317319

    work page doi:10.1086/317319 2000

  30. [30]

    Astrometric orbit determination with Markov chain Monte Carlo and genetic algorithms: Systems with stellar, sub-stellar, and planetary mass companions

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

    work page doi:10.1051/0004-6361/202244161 2023

  31. [31]

    M., Duchˆ ene, G., & Matthews, B

    Hughes, A. M., Duchˆ ene, G., & Matthews, B. C. 2018, ARA&A, 56, 541, doi:10.1146/annurev-astro-081817-052035

    work page doi:10.1146/annurev-astro-081817-052035 2018

  32. [32]

    J., & Bromley, B

    Kenyon, S. J., & Bromley, B. C. 2002a, AJ, 123, 1757, doi:10.1086/338850 —. 2002b, ApJ, 577, L35, doi:10.1086/344084 —. 2010, ApJS, 188, 242, doi:10.1088/0067-0049/188/1/242

    work page doi:10.1086/338850 2010

  33. [33]

    J., Bromley, B

    Kenyon, S. J., Bromley, B. C., & Najita, J. R. 2026, AJ, 171, 223, doi:10.3847/1538-3881/ae46a2

    work page doi:10.3847/1538-3881/ae46a2 2026

  34. [34]

    Proper-motion anomaly and resolved common proper-motion pairs

    Kervella, P., Arenou, F., & Th´ evenin, F. 2022, A&A, 657, A7, doi:10.1051/0004-6361/202142146

    work page doi:10.1051/0004-6361/202142146 2022

  35. [35]

    Krivov, A. V. 2010, Research in Astronomy and Astrophysics, 10, 383, doi:10.1088/1674-4527/10/5/001

    work page doi:10.1088/1674-4527/10/5/001 2010

  36. [36]

    M., Gratadour, D., Chauvin, G., et al

    Lagrange, A. M., Gratadour, D., Chauvin, G., et al. 2009, A&A, 493, L21, doi:10.1051/0004-6361:200811325

    work page doi:10.1051/0004-6361:200811325 2009

  37. [37]

    F., Jimenez R., Lahav O., 2000, @doi [ ] 10.1046/j.1365-8711.2000.03692.x , http://adsabs.harvard.edu/abs/2000MNRAS.317..965H 317, 965

    Lattanzi, M. G., Spagna, A., Sozzetti, A., & Casertano, S. 2000, MNRAS, 317, 211, doi:10.1046/j.1365-8711.2000.03637.x

    work page doi:10.1046/j.1365-8711.2000.03637.x 2000

  38. [38]

    2018, A&A, 611, A43, doi:10.1051/0004-6361/201731426

    Lazzoni, C., Desidera, S., Marzari, F., et al. 2018, A&A, 611, A43, doi:10.1051/0004-6361/201731426

    work page doi:10.1051/0004-6361/201731426 2018

  39. [39]

    F., Ansdell M., Rosotti G

    Manara, C. F., Ansdell, M., Rosotti, G. P., et al. 2023, in Astronomical Society of the Pacific Conference Series, Vol. 534, Protostars and Planets VII, ed. S. Inutsuka, Y. Aikawa, T. Muto, K. Tomida, & M. Tamura, 539, doi:10.48550/arXiv.2203.09930

    work page doi:10.48550/arxiv.2203.09930 2023

  40. [40]

    Science , author =

    Marois, C., Macintosh, B., Barman, T., et al. 2008, Science, 322, 1348, doi:10.1126/science.1166585

    work page doi:10.1126/science.1166585 2008

  41. [41]

    Nature , keywords =

    Barman, T. 2010, Nature, 468, 1080, doi:10.1038/nature09684

    work page doi:10.1038/nature09684 2010

  42. [42]

    Worley, C. E. 2001, AJ, 122, 3466, doi:10.1086/323920

    work page doi:10.1086/323920 2001

  43. [43]

    2023, A&A, 672, A93, doi: 10.1051/0004-6361/202345865

    Mesa, D., Gratton, R., Kervella, P., et al. 2023, A&A, 672, A93, doi:10.1051/0004-6361/202345865

    work page doi:10.1051/0004-6361/202345865 2023

  44. [44]

    2015, A&A, 574, A115, doi:10.1051/0004-6361/201425310

    Michalik, D., Lindegren, L., & Hobbs, D. 2015, A&A, 574, A115, doi:10.1051/0004-6361/201425310

    work page doi:10.1051/0004-6361/201425310 2015

  45. [45]

    2015, ApJ, 799, 41, doi:10.1088/0004-637X/799/1/41

    Morrison, S., & Malhotra, R. 2015, ApJ, 799, 41, doi:10.1088/0004-637X/799/1/41

    work page doi:10.1088/0004-637x/799/1/41 2015

  46. [46]

    Dormant black hole candidates from Gaia DR3 summary diagnostics

    Mouillet, D., Larwood, J. D., Papaloizou, J. C. B., & Lagrange, A. M. 1997, MNRAS, 292, 896, doi:10.1093/mnras/292.4.896 M¨ uller-Horn, J., Rix, H.-W., El-Badry, K., et al. 2025, arXiv e-prints, arXiv:2510.05982, doi:10.48550/arXiv.2510.05982

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1093/mnras/292.4.896 1997

  47. [47]

    2009, MNRAS, 398, 515, doi: 10.1111/j.1365-2966.2009.14895.x

    Mustill, A. J., & Wyatt, M. C. 2009, MNRAS, 399, 1403, doi:10.1111/j.1365-2966.2009.15360.x —. 2012, MNRAS, 419, 3074, doi:10.1111/j.1365-2966.2011.19948.x

    work page doi:10.1111/j.1365-2966.2009.15360.x 2009

  48. [48]

    R., Kenyon, S

    Najita, J. R., Kenyon, S. J., & Bromley, B. C. 2022, ApJ, 925, 45, doi:10.3847/1538-4357/ac37b6

    work page doi:10.3847/1538-4357/ac37b6 2022

  49. [49]

    R., & Kuchner, M

    Nesvold, E. R., & Kuchner, M. J. 2015, ApJ, 798, 83, doi:10.1088/0004-637X/798/2/83

    work page doi:10.1088/0004-637x/798/2/83 2015

  50. [50]

    M., et al

    Nowak, M., Lacour, S., Lagrange, A. M., et al. 2020, A&A, 642, L2, doi:10.1051/0004-6361/202039039

    work page doi:10.1051/0004-6361/202039039 2020

  51. [51]

    Astronomy and Astrophysics Supplement Series , author =

    Ochsenbein, F., Bauer, P., & Marcout, J. 2000, A&AS, 143, 23, doi:10.1051/aas:2000169

    work page doi:10.1051/aas:2000169 2000

  52. [52]

    D., & Wyatt, M

    Pearce, T. D., & Wyatt, M. C. 2014, MNRAS, 443, 2541, doi:10.1093/mnras/stu1302

    work page doi:10.1093/mnras/stu1302 2014

  53. [53]

    D., Launhardt, R., Ostermann, R., et al

    Pearce, T. D., Launhardt, R., Ostermann, R., et al. 2022, A&A, 659, A135, doi:10.1051/0004-6361/202142720

    work page doi:10.1051/0004-6361/202142720 2022

  54. [54]

    Penoyre, Z., Belokurov, V., & Evans, N. W. 2022, MNRAS, 513, 5270, doi:10.1093/mnras/stac1147

    work page doi:10.1093/mnras/stac1147 2022

  55. [55]

    A., Batten, A

    Pourbaix, D., Tokovinin, A. A., Batten, A. H., et al. 2004, A&A, 424, 727, doi:10.1051/0004-6361:20041213

    work page doi:10.1051/0004-6361:20041213 2004

  56. [56]

    L., Ciardi, D., et al

    Ramirez, S., Akeson, R. L., Ciardi, D., et al. 2013, in American Astronomical Society Meeting Abstracts, Vol. 221, American Astronomical Society Meeting Abstracts #221, 334.01

    2013

  57. [57]

    N., Armitage, P

    Raymond, S. N., Armitage, P. J., Moro-Mart´ ın, A., et al. 2011, A&A, 530, A62, doi:10.1051/0004-6361/201116456 Reg´ aly, Z., Dencs, Z., Mo´ or, A., & Kov´ acs, T. 2018, MNRAS, 473, 3547, doi:10.1093/mnras/stx2604

    work page doi:10.1051/0004-6361/201116456 2011

  58. [58]

    Gaia Early Data Release 3: Photometric content and validation.Astronomy & Astrophysics2021,649, A3

    Riello, M., De Angeli, F., Evans, D. W., et al. 2021, A&A, 649, A3, doi:10.1051/0004-6361/202039587

    work page doi:10.1051/0004-6361/202039587 2021

  59. [59]

    2016, MNRAS, 462, L116, doi:10.1093/mnrasl/slw143

    Shannon, A., Bonsor, A., Kral, Q., & Matthews, E. 2016, MNRAS, 462, L116, doi:10.1093/mnrasl/slw143

    work page doi:10.1093/mnrasl/slw143 2016

  60. [60]

    M., Wyatt, M

    Sibthorpe, B., Kennedy, G. M., Wyatt, M. C., et al. 2018, MNRAS, 475, 3046, doi:10.1093/mnras/stx3188 S´ odor,´A., Chen´ e, A.-N., De Cat, P., et al. 2014, A&A, 568, A106, doi:10.1051/0004-6361/201423976

    work page doi:10.1093/mnras/stx3188 2018

  61. [61]

    , keywords =

    Stassun, K. G., & Torres, G. 2021, ApJ, 907, L33, doi:10.3847/2041-8213/abdaad Stef´ ansson, G., Mahadevan, S., Winn, J. N., et al. 2025, AJ, 169, 107, doi:10.3847/1538-3881/ada9e1

    work page doi:10.3847/2041-8213/abdaad 2021

  62. [62]

    Su, K. Y. L., Rieke, G. H., Misselt, K. A., et al. 2005, ApJ, 628, 487, doi:10.1086/430819

    work page doi:10.1086/430819 2005

  63. [63]

    Su, K. Y. L., Rieke, G. H., Stansberry, J. A., et al. 2006, ApJ, 653, 675, doi:10.1086/508649

    work page doi:10.1086/508649 2006

  64. [64]

    Su, K. Y. L., Rieke, G. H., Stapelfeldt, K. R., et al. 2009, ApJ, 705, 314, doi:10.1088/0004-637X/705/1/314

    work page doi:10.1088/0004-637x/705/1/314 2009

  65. [65]

    Su, K. Y. L., Rieke, G. H., Malhotra, R., et al. 2013, ApJ, 763, 118, doi:10.1088/0004-637X/763/2/118

    work page doi:10.1088/0004-637x/763/2/118 2013

  66. [66]

    Su, K. Y. L., De Buizer, J. M., Rieke, G. H., et al. 2017, AJ, 153, 226, doi:10.3847/1538-3881/aa696b

    work page doi:10.3847/1538-3881/aa696b 2017

  67. [67]

    Su, K. Y. L., G´ asp´ ar, A., Rieke, G. H., et al. 2024, ApJ, 977, 277, doi:10.3847/1538-4357/ad8cde

    work page doi:10.3847/1538-4357/ad8cde 2024

  68. [68]

    2021, A&A, 651, A72, doi:10.1051/0004-6361/202038107

    Vigan, A., Fontanive, C., Meyer, M., et al. 2021, A&A, 651, A72, doi:10.1051/0004-6361/202038107

    work page doi:10.1051/0004-6361/202038107 2021

  69. [69]

    A., Ragusa, E., et al

    Vioque, M., Booth, R. A., Ragusa, E., et al. 2026, A&A, 705, A238, doi:10.1051/0004-6361/202557086

    work page doi:10.1051/0004-6361/202557086 2026

  70. [70]

    2000, A&AS, 143, 9, doi: 10.1051/aas:2000332

    Wenger, M., Ochsenbein, F., Egret, D., et al. 2000, Astronomy and Astrophysics Supplement Series, 143, 9, doi:10.1051/aas:2000332

    work page internal anchor Pith review doi:10.1051/aas:2000332 2000

  71. [71]

    Wyatt, M. C. 2008, Annual Review of Astronomy and Astrophysics

    2008

  72. [72]

    M., 2002, @doi [ ] 10.1046/j.1365-8711.2002.05848.x , http://adsabs.harvard.edu/abs/2002MNRAS.336.1188K 336, 1188

    Wyatt, M. C., & Dent, W. R. F. 2002, MNRAS, 334, 589, doi:10.1046/j.1365-8711.2002.05533.x

    work page doi:10.1046/j.1365-8711.2002.05533.x 2002

  73. [73]

    C., Dermott, S

    Wyatt, M. C., Dermott, S. F., Telesco, C. M., et al. 1999, ApJ, 527, 918, doi:10.1086/308093 12

    work page doi:10.1086/308093 1999

  74. [74]

    C., & Kenyon, S

    Yee, J. C., & Kenyon, S. J. 2025, AJ, 170, 132, doi:10.3847/1538-3881/adeb84 This paper was built using the Open Journal of As- trophysics LATEX template. The OJA is a journal which provides fast and easy peer review for new papers in the astro-phsection of the arXiv, making the reviewing pro- cess simpler for authors and referees alike. Learn more athttp...

    work page doi:10.3847/1538-3881/adeb84 2025