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Evaluating the Limits of Rotation Period Recovery through Gyrochronology Criteria

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A gyrochronology-based filter recovers correct rotation periods from blended TESS light curves in 88% of cases with periods under 12 days, and sets a practical reliability threshold at roughly 8 days.

desk verdict The 88% recovery rate in the abstract is conditional on blends passing the gyrochronology filter and on periods under 12 days; across all simulated blends the full-sample recall is far lower, so the abstract overstates the method. read the letter →

arxiv 2507.08266 v1 pith:33NUSDMY submitted 2025-07-11 astro-ph.SR astro-ph.EPastro-ph.IM

Mariel Lares-Martiz , Terry D. Oswalt , Derek L. Buzasi , Kylie R. Boyer , Luca Guida , Ryan J. Reynolds This is my paper · ORCID
classification astro-ph.SRastro-ph.EPastro-ph.IM
keywords stellarrotationgyrochronologywidebinarystarsTESSlight-curveblendingperiodogramanalysisages
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tackles a concrete problem: TESS's large pixels mean many stellar light curves are blends of two or more variable stars, and standard deblending tools assume the contaminant is constant, which fails when both stars rotate. The authors argue that wide binaries, whose two components must share an age, supply a built-in check: the rotation periods of both components must lie on a common gyrochronology curve, an empirical period–age relation calibrated on open clusters. Using simulated blends built from real Kepler light curves with known rotation periods, they show their coevality filter picks out the true periods 88% of the time for periods under 12 days, and that for real TESS observations the filter rescues many pairs whose initial dominant periodogram peaks look inconsistent. A sympathetic reader would care because the result offers a physically motivated way to vet rotation periods in the crowded, short-baseline TESS regime, where automated pipelines and human decisions currently lean on the dominant periodogram peak alone.

What carries the argument

The machinery is the Coevality Criterion (CC): a rule that accepts a pair of candidate rotation periods only if both components fall within 2 sigma of the same gyrochrone, a third-degree polynomial fit to color–period data of a reference open cluster, so that they imply a common age. For pairs that fail the CC on their dominant periodogram peaks, the authors add a reconciliation step: take the three strongest Lomb–Scargle peaks from each component's periodogram, test every pairwise combination against each cluster gyrochrone, and when several combinations pass, pick the one with the highest combined Lomb–Scargle power. The calibrated 2-sigma thresholds (roughly 1.3–3.6 days depending on cluster) and the polynomial degree are the tunable parts that set how strict the filter is.

What would settle it

A concrete check: take wide binaries with independently known ages (for example, from asteroseismology) and uncontaminated rotation periods measured from high-cadence ground-based or Kepler short-cadence photometry, and ask whether a large majority of these genuinely coeval pairs land inside the paper's 2-sigma gyrochrone windows. If more than a small fraction of such pairs fall outside the windows, the filter's thresholds or the cluster calibration, rather than blending, are the cause of the failures; likewise, running the blending simulation on pairs with metallicities outside the calibrating clusters' range would show whether the 88% recovery rate is a property of the method or of the calibration sample.

Watch

Extended reading notes

Core claim

The paper's central claim is that gyrochronology, the empirical relation between stellar rotation period, color (mass), and age, can serve as a filter for selecting true rotation periods from blended light curves of wide binaries. For each binary the authors fit third-degree polynomial gyrochrones to five open clusters (Pleiades, Praesepe, NGC 6811, Ruprecht 147, and M67), define a 2-sigma window around each gyrochrone, and declare a pair coeval-consistent if a choice of two periodogram peaks places both components inside one such window at a common age. In simulated blends of 89 real Kepler wide binaries, 34 pairs satisfied the criterion and 64.7% of the individual periods were recovered, rising to 88% (21 of 24 stars) for periods shorter than 12 days, the regime where TESS is most sensitive. Applied to 360 TESS wide binaries, only 52 initially agreed with gyrochronology, but after searching the three strongest periodogram peaks per component, 269 pairs became consistent. The paper concludes that periods below about 8 days are reliably recoverable from blended TESS data, that periods beyond about 10 days usually remain unresolved, and that the assignment 'brightest star gets the strongest peak' holds often but not always.

Load-bearing premise

The method assumes that rotation–age relations fitted to five open clusters (Pleiades, Praesepe, NGC 6811, Ruprecht 147, and M67) hold for arbitrary field wide binaries, including pairs whose ages or metallicities fall outside that cluster set; if that transfer fails, the filter will systematically reject true periods and accept false ones.

Editorial extensions

If this is right

  • If the central claim is right, automated pipelines that assume the dominant periodogram peak is the target's rotation period will mis-assign periods for many blended wide binaries; applying a gyrochronology consistency check before accepting periods would reduce false positives.
  • TESS rotation samples should treat periods longer than about 10 days from blended or crowded-field observations with caution, since recovery drops sharply beyond that threshold even when the true signal exists.
  • The recovered TESS sample's periods clustering near 3–8 days, combined with the kinematic youth of the agreeing pairs, implies that gyrochronology-validated TESS periods are most dependable for young (about 1 Gyr or younger) thin-disk stars.
  • Wide binaries validated by the CC gain credibility as gyrochronological age probes, since pairs passing the criterion scatter about a common gyrochrone comparably to cluster members.

Reading between the lines

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

  • Beyond the paper: the 88% figure comes from blends of only two stars whose true periods are known; real TESS blends can include more than two unresolved sources, so the method's field success likely bounds the two-source case, and the approach might extend to higher-order blends by adding more candidate-peak combinations.
  • Beyond the paper: because the CC is calibrated on cluster gyrochrones, it is a consistency filter, not an independent age measure; pairs that fail the CC could be reanalyzed with gyrochrone-free tests (such as comparing spot-modulation amplitude ratios or using two-color photometry) to decide whether the failure is physical or methodological.
  • Beyond the paper: a directly testable extension is to run the same simulated-blend pipeline on wide binaries spanning a range of metallicities to map where the 88% recovery rate degrades, which would quantify how much of the failure is due to the cluster calibration itself.
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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

5 major / 5 minor

Summary. The paper proposes a 'Coevality Criterion' (CC) that uses open-cluster gyrochrones as a physical prior to select among periodogram peaks in blended wide binaries, under the assumption that the two components share a common age. The method is tested on 89 simulated blends constructed by summing real Kepler light curves of wide binaries with known rotation periods, and then applied to roughly 360 TESS wide binaries. The headline claim is an 88% success rate for recovering true periods shorter than 12 days, together with a practical detection threshold of about 8–10 days for TESS blended observations.

Significance. The idea of using coevality as a physically motivated constraint on period selection is timely and potentially useful, since standard deblending tools usually assume a non-variable contaminant. The simulation design is a genuine strength: it uses externally known rotation periods from Gruner et al. (2023a), provides an external ground truth, and the paper honestly reports that only 34 of 89 simulated blends satisfy the CC and that only 44.1% of CC-passing pairs are fully correct. If the conditional success rates were reported transparently and the match criteria were made reproducible, the method would offer a valuable complement to existing TESS deblending approaches.

major comments (5)
  1. [Abstract and §3.2] The headline 88% recovery rate is conditional on two filters: the 34 simulated blends that pass the CC, and true periods under 12 days. Section 3.2 reports 21 recovered stars out of 24 in that subset, but it never gives the number of true periods under 12 days in the full 89-pair sample or the recovery rate over all 89 blends. Table 2 implies that at most 15 of 89 simulated blends (about 17%) are fully correct. The abstract's sentence 'recovers correct rotation periods with an 88% success rate for periods <12 days' should therefore be reworded to state the conditioning explicitly, and the paper should report full-sample recall, including the denominator of true short-period stars in all 89 blends.
  2. [§3.2, Table 2] The 'Matched' column in Table 2 is never defined. Without a period-matching tolerance, a statement such as 'correctly recovering 88% of the rotation periods' is not reproducible or falsifiable. The authors should state the matching rule: for example, relative or absolute tolerance in period, whether harmonics and aliases count as matches, and whether matching is evaluated per component or only for the pair as a whole. This matters especially because Figure 4 explicitly marks 2:1, 1:1, and 0.5:1 period ratios, so it is unclear whether a recovered period at a harmonic of the true period would be counted as correct.
  3. [§3.1] The controlled simulation degrades continuous Kepler light curves with Gaussian white noise and smoothing, but it does not truncate them to TESS's roughly 27-day sectors or impose TESS sampling and red-noise characteristics. Consequently, the 8–10 day detection threshold emphasized in the abstract and in Section 4 is not directly tested by the controlled experiment. The threshold in Section 4 is inferred from the real TESS sample, where there is no independent ground truth for the recovered periods. The authors should either add a simulation variant with sector-length light curves or soften the claim that the controlled test validates the TESS-specific threshold.
  4. [§2.1 and §4] The method selects period combinations that satisfy the CC and then measures its own success on the subset that passes the same CC; this is a potential circularity that the paper does not fully address. For the TESS sample, 'successful recovery' is by construction consistency with a gyrochrone, and the increase from 52 to 269 agreeing pairs after reconciliation is therefore not an independent validation of the recovered periods. The kinematic comparison in §4.1 is qualitative and not quantified. I recommend reporting an explicit null comparison: for example, the fraction of CC-passing pairs that would be obtained by random peak selection, or by always choosing LS1LS1, so that the added information from gyrochronology is demonstrated. The paper should also report recovery statistics for all 89 simulated blends, including pairs that fail the CC, rather than only for the selected subset.
  5. [§2 and §4] The CC relies on the assumption that the empirical gyrochronology relations calibrated on the Pleiades, Praesepe, NGC 6811, Ruprecht 147, and M67 apply to arbitrary field wide binaries. The paper acknowledges this limitation in Section 4, but it does not test the sensitivity of the results to the assumed cluster calibration or to the 2-sigma thresholds in Table 1. Because a mismatch between field stars and cluster gyrochrones would systematically cause both false rejections and false acceptances, I ask for a sensitivity test (for example, using 1-sigma and 3-sigma thresholds, or leaving one cluster out) to show that the qualitative conclusions are robust.
minor comments (5)
  1. [Figure 4 caption] The caption for Figure 4 states that the method recovers 88% of periods without noting that the denominator is 24 stars from the 34 CC-passing pairs; please add the conditional wording to the caption as well.
  2. [§3.2] The text reports a 64.7% per-star recovery rate among the 68 stars in the 34 coeval pairs, but Table 2 reports pair-level counts. The relationship between the 44 recovered stars and the 15 fully matched pairs should be stated explicitly, since a pair can be partially correct.
  3. [§3.1 and §3.2] The simulation prewhitens the first 10 dominant frequencies, but Table 2 and the reconciliation step only consider the first three Lomb-Scargle peaks; please clarify how the ten frequencies are reduced to the three candidate peaks used in the analysis.
  4. [General] No data or code availability statement is provided. Given the emphasis on reproducibility of the recovery rates, a link to the light-curve processing and period-selection code, or at least a clear statement of availability, would be valuable.
  5. [Throughout] There are several typographical issues, including 'T able' at the start of Section 2, '∼13.7,days' and '∼10–12,days' in Section 4, and inconsistent comma usage in numbers; a careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the recovery test uses external Kepler ground truth and independent kinematic validation, and the paper's caveats are explicitly acknowledged. Minor self-citations are not load-bearing.

full rationale

The paper's central derivation is not circular. The simulation supplies an external ground truth: 89 wide-binary pairs with periods from Gruner et al. (2023a) Kepler/K2 data are blended, degraded with noise, and the coevality criterion (CC) is then asked to select the true periods among periodogram peaks. Success is scored against the known input periods, not against CC consistency alone, so the output is not equivalent to the input by construction. Although the ground-truth sample was pre-selected to lie on the slow-rotator sequence at a consistent gyrochronology age, the algorithm demonstrably can and does choose incorrect combinations (only 15 of 34 CC-passing pairs are fully correct, 44.1%), which shows the test has independent content. The abstract's 88% figure is conditional on the 34 of 89 simulated pairs that satisfied the CC and on periods under 12 days (Section 3.2); this is a conditional-reporting/clarity issue rather than a definitional circularity. The paper also explicitly acknowledges in Section 5 that the post-CC scatter in the gyrochrone diagram 'reflects the selection imposed by our method.' The independent kinematic validation in Section 4.1 (thin-disk velocities and young-population expectations) is an external consistency check, not a self-referential loop. Self-citations, specifically Lares-Martiz et al. (2024) for TESS false periods and Oswalt et al. (2022) for a period-clustering observation, are incidental and not load-bearing. No step meets the standard of reducing a derived result to its own input by construction, so the circularity score is low despite the statistical-presentation concerns.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The CC rests on empirically calibrated gyrochronology (cluster polynomial fits and their 2-sigma scatter), plus the assumption that field wide binaries behave like cluster members. The simulation adds hand-chosen noise and peak-selection parameters. No new entities are postulated.

free parameters (8)
  • gyrochrone polynomial coefficients for 5 open clusters = not tabulated; 5 third-degree polynomials
    The CC compares observed periods to third-degree polynomial fits of rotation period versus color for Pleiades, Praesepe, NGC 6811, Ruprecht 147, and M67 (Section 2). These coefficients are fitted to cluster data and define the expected gyrochronal ages.
  • 2-sigma deviation thresholds per cluster = 2.73, 1.32, 1.64, 2.78, 3.56 days (Table 1)
    The 2-sigma scatter of cluster members around each gyrochrone sets the pass/fail tolerance for the CC. The paper describes the threshold as chosen during 'parameter calibration' (Section 2.2).
  • polynomial degree = 3
    Chosen because higher degree fits did not improve the correlation coefficient (Section 2).
  • frequency separation threshold = 0.003 d^-1
    Prewhitened frequencies must be separated by at least this value, chosen to avoid peaks from differential rotation (Section 3.1).
  • injected white noise level = 0.5% standard deviation
    Gaussian noise added to simulated blended light curves to mimic TESS FFI precision for T~14.5-15 stars (Section 3.1).
  • Gaussian smoothing kernel = not specified
    Applied to simulated light curves to mimic instrumental trends; parameters are not given in the paper.
  • number of periodogram peaks per component = 3
    Reconciliation step evaluates only combinations of the top 3 LS peaks, a choice that bounds the search space and can miss true periods outside these peaks (Section 2.2).
  • tie-breaker rule (combined LS power ranking) = top-ranked pairing
    Added after observing that single-CC-passing pairs skew toward long periods; the rule increases the usable sample by 21% and biases selection toward the brighter component (Section 3.2).
assumptions (6)
  • domain assumption Gyrochronology relation is valid for field wide binary components
    Method assumes coeval binaries follow the same rotation-age relation as open clusters; paper notes gyrochronology is purely empirical with no physical model (Sections 1 and 2.1).
  • domain assumption Wide binary components are coeval
    The CC relies on components sharing a common age and rotational history (Section 2).
  • domain assumption The open cluster gyrochrones span the ages of the target WB sample
    Pairs with ages outside the cluster age range will fail the CC; acknowledged in Section 4 ('or because they don't have the ages of the open clusters used as references').
  • domain assumption The true periods of the simulated blends are among the top three periodogram peaks
    Reconciliation only searches LS1-LS3 for each component; if the true period is not among them, recovery is impossible (Section 2.2).
  • domain assumption White noise at 0.5% is a conservative proxy for TESS systematics
    Real TESS light curves contain red noise; assumed white noise gives a reasonable upper bound (Section 3.1).
  • domain assumption Gruner's 'S' classification correctly identifies slow-rotator, coeval systems
    Ground truth sample is restricted to Gruner et al. (2023a) systems classified as 'S', assuming this classification is correct (Section 3).

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Pith. "Pith review of Evaluating the Limits of Rotation Period Recovery through Gyrochronology Criteria." pith.science (2026). https://pith.science/paper/33NUSDMY

@misc{pith2026250708266,
  author       = {Pith},
  title        = {Pith review of: Evaluating the Limits of Rotation Period Recovery through Gyrochronology Criteria},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/33NUSDMY}},
  note         = {Machine review of arXiv:2507.08266}
}
abstract

Contamination from nearby sources often compromises stellar rotation periods derived from photometric light curves, particularly in data with large pixel scales such as TESS. This problem is compounded when both the target and contaminant are intrinsically variable, a scenario that challenges deblending algorithms, which often assume constant contaminants. We assess the reliability of rotation period detections using wide binary systems, whose components share a common age and rotational history. By applying gyrochronology constraints, we identify period combinations that yield consistent ages between components, helping to isolate true rotation signals. Simulating blends with degraded Kepler data, our method recovers correct rotation periods with an 88\% success rate for periods $<12$ days, where TESS detections are most reliable. Applying this framework to nearly 300 wide binaries observed by TESS, we find that despite significant contamination, a subset of pairs shows consistent gyrochronological ages. We establish a practical detection threshold for TESS blended observations, finding that periods shorter than $\sim8$ days are reliably recovered, while those longer than $\sim10$ days become significantly more challenging and often remain unresolved. As expected, rotation periods are more often recovered when the highest-amplitude periodogram peak is linked to the brighter star and the second to the dimmer star, although many cases deviate from this pattern, indicating it cannot always be assumed. Our results highlight the limitations of standard deblending methods and demonstrate that astrophysical constraints, such as gyrochronology, provide a valuable tool for extracting reliable rotation periods from complex photometric blends.

Figures

Figures reproduced from arXiv: 2507.08266 by the authors.

Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Example of blending in the pair TIC 189013224 and TIC 189013222. Top left: PDC–SAP light curves of TIC 189013224 (blue) and TIC 189013222 (red), showing their blended variability over the ∼ 25 day interval. Top right: Corresponding TESS target-pixel file (TPF) for the blend, with the approximate centroids of TIC 189013224 marked by a blue triangle and TIC 189013222 by a red inverted trian￾gle. Bottom: nearly identic… view at source ↗
Figure 3
Figure 3. CPD showing every possible pairing of the three dominant Lomb–Scargle (LS) peaks from the blended light curves of TIC 189013224 and TIC 189013222. Each panel is labeled “LSaSb,” where “a” denotes the rank of the LS periodogram peak assigned to Component 1 and “b” denotes the rank assigned to Component 2 (for example, “LS1LS2” means the first-ranked peak for the primary star and the second-ranked peak for the seconda… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Comparison between the recovered rotation pe￾riods from simulated blends and the original periods reported by D. Gruner et al. (2023a) for both components in WBs. Red dashed lines mark the 2:1, 1:1, and 0.5:1 period ratios. The shaded region, bounded by dashed lines at…
Figure 6
Figure 6. Figure 6: HR diagram of our 360 MS+MS WB pairs sam￾ple. Components of each pair are connected by lines. In this section, we evaluate the performance of the CC applied to real WBs observed by TESS [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: CPD before and after applying the CC method. ogy paradigm applies. All 360 WB pairs are in the K. El-Badry et al. (2021) catalog, showing R < 0.1, mean￾ing a high degree of confidence of being physical pairs. We constructed our light curves by creating custom masks for…
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: illustrates that for both components, the re￾covered rotation periods predominantly cluster between approximately 3 and 8 days (also noted by T. D. Os￾walt et al. 2022). Longer rotation periods become in￾creasingly challenging to recover reliably, as evidenced by the b…
Figure 10
Figure 10. Figure 10: Contamination ratio (Rcont) as a function of the magnitude difference (∆Gmag) between the components of each binary. To further assess the impact of contamination, Fig￾ure 10 shows the magnitude difference between the two components of each binary (∆Gmag) as a functio…
Figure 12
Figure 12. Figure 12: Occurrences of combinations amplitude peak (LS1) typically corresponds to the true dominant rotation signal, and the second-highest peak (LS2) captures the companion’s modulation. Nonethe￾less, the non-negligible occurrence of LS2LS1 and LS3LS1 combinations—where the …

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Works this paper leans on

38 extracted references · 8 canonical work pages

  1. [1]

    A., Tayar, J

    Avallone, E. A., Tayar, J. N., van Saders, J. L., et al. 2022, ApJ, 930, 7, doi: 10.3847/1538-4357/ac60a1

    doi:10.3847/1538-4357/ac60a1 2022

  2. [2]

    Barnes, S. A. 2003, ApJ, 586, 464, doi: 10.1086/367639

    doi:10.1086/367639 2003

  3. [3]

    2003, A&A, 410, 527, doi: 10.1051/0004-6361:20031213 12

    Bensby, T., Feltzing, S., & Lundstr¨ om, I. 2003, A&A, 410, 527, doi: 10.1051/0004-6361:20031213 12

    doi:10.1051/0004-6361:20031213 2003

  4. [4]

    S., & G¨ unther, H

    Binks, A. S., & G¨ unther, H. M. 2024, MNRAS, 533, 2162, doi: 10.1093/mnras/stae1850

    work page doi:10.1093/mnras/stae1850 2024

  5. [5]

    G., Palumbo, E

    Bouma, L. G., Palumbo, E. K., & Hillenbrand, L. A. 2023, The Astrophysical Journal Letters, 947, L3, doi: 10.3847/2041-8213/acc589

    doi:10.3847/2041-8213/acc589 2023

  6. [6]

    Quantifying the Limits of TESS Stellar Rotation Measurements with the K2-TESS Overlap

    Boyle, A. W., Mann, A. W., & Bush, J. 2025, arXiv e-prints, arXiv:2504.13262, doi: 10.48550/arXiv.2504.13262

    work page Pith review arXiv doi:10.48550/arxiv.2504.13262 2025

  7. [7]

    Brown, A. G. A., Vallenari, A., Prusti, T., et al. 2021, Astronomy and Astrophysics, doi: 10.1051/0004-6361/202039657e

    doi:10.1051/0004-6361/202039657e 2021

  8. [8]

    2011, A&A, 530, A138, doi: 10.1051/0004-6361/201016276

    Casagrande, L., Sch¨ onrich, R., Asplund, M., et al. 2011, A&A, 530, A138, doi: 10.1051/0004-6361/201016276

    doi:10.1051/0004-6361/201016276 2011

Show all 38 references
  1. [9]

    2025, A&A, 694, A120, doi: 10.1051/0004-6361/202452552

    Chen, T., & Prantzos, N. 2025, A&A, 694, A120, doi: 10.1051/0004-6361/202452552

    2025 doi

  2. [10]

    R., van Saders, J

    Claytor, Z. R., van Saders, J. L., Llama, J., et al. 2022, ApJ, 927, 219, doi: 10.3847/1538-4357/ac498f

    2022 doi

  3. [11]

    L., Ag¨ ueros, M

    Curtis, J. L., Ag¨ ueros, M. A., Douglas, S. T., & Meibom, S. 2019, ApJ, 879, 49, doi: 10.3847/1538-4357/ab2393

    2019 doi

  4. [12]

    L., Ag¨ ueros, M

    Curtis, J. L., Ag¨ ueros, M. A., Matt, S. P., et al. 2020, ApJ, 904, 140, doi: 10.3847/1538-4357/abbf58

    2020 doi

  5. [13]

    Dehnen, W., & Binney, J. J. 1998, MNRAS, 298, 387, doi: 10.1046/j.1365-8711.1998.01600.x

    1998

  6. [14]

    T., Ag¨ ueros, M

    Douglas, S. T., Ag¨ ueros, M. A., Covey, K. R., & Kraus, A. 2017, ApJ, 842, 83, doi: 10.3847/1538-4357/aa6e52

    2017 doi

  7. [15]

    T., Curtis, J

    Douglas, S. T., Curtis, J. L., Ag¨ ueros, M. A., et al. 2019, ApJ, 879, 100, doi: 10.3847/1538-4357/ab2468

    2019 doi

  8. [16]

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

    2021 doi

  9. [17]

    J., Cˆ ot´ e, P., McConnachie, A

    Fantin, N. J., Cˆ ot´ e, P., McConnachie, A. W., et al. 2019, ApJ, 887, 148, doi: 10.3847/1538-4357/ab5521

    2019 doi

  10. [18]

    A., & Janes, K

    Gruner, D., Barnes, S. A., & Janes, K. A. 2023a, A&A, 675, A180, doi: 10.1051/0004-6361/202346590

    doi

  11. [19]

    A., & Weingrill, J

    Gruner, D., Barnes, S. A., & Weingrill, J. 2023b, A&A, 672, A159, doi: 10.1051/0004-6361/202345942

    doi

  12. [20]

    Han, T., & Brandt, T. D. 2023, AJ, 165, 71, doi: 10.3847/1538-3881/acaaa7

    2023 doi

  13. [21]

    2025, arXiv e-prints, arXiv:2505.10376, doi: 10.48550/arXiv.2505.10376

    Hattori, S., Angus, R., Foreman-Mackey, D., et al. 2025, arXiv e-prints, arXiv:2505.10376, doi: 10.48550/arXiv.2505.10376

    2025 doi arXiv

  14. [22]

    2020, Research Notes of the AAS, 4, 220, doi: 10.3847/2515-5172/abd106

    Hedges, C., Angus, R., Barentsen, G., et al. 2020, Research Notes of the AAS, 4, 220, doi: 10.3847/2515-5172/abd106

    2020 doi

  15. [23]

    2021, AJ, 162, 107, doi: 10.3847/1538-3881/ac0825

    Barentsen, G. 2021, AJ, 162, 107, doi: 10.3847/1538-3881/ac0825

    2021 doi

  16. [24]

    E., & Bell, K

    Higgins, M. E., & Bell, K. J. 2023, AJ, 165, 141, doi: 10.3847/1538-3881/acb20c

    2023 doi

  17. [25]

    B., Oswalt, T

    Holberg, J. B., Oswalt, T. D., Sion, E. M., & McCook, G. P. 2016, MNRAS, 462, 2295, doi: 10.1093/mnras/stw1357

    2016 doi

  18. [26]

    2024, Research Notes of the American Astronomical Society, 8, 132, doi: 10.3847/2515-5172/ad4a7c

    Lares-Martiz, M., Buzasi, D., Oswalt, T., et al. 2024, Research Notes of the American Astronomical Society, 8, 132, doi: 10.3847/2515-5172/ad4a7c

    2024 doi

  19. [27]

    A., et al

    Lu, Y., Angus, R., Ag¨ ueros, M. A., et al. 2020, AJ, 160, 168, doi: 10.3847/1538-3881/abada4

    2020 doi

  20. [28]

    S., Buzasi, D., Huber, D., et al

    Metcalfe, T. S., Buzasi, D., Huber, D., et al. 2023, AJ, 166, 167, doi: 10.3847/1538-3881/acf1f7

    2023 doi

  21. [29]

    B., Ball, W

    Nielsen, M. B., Ball, W. H., Standing, M. R., et al. 2020, A&A, 641, A25, doi: 10.1051/0004-6361/202037461 Nordstr¨ om, B., Mayor, M., Andersen, J., et al. 2004, A&A, 418, 989, doi: 10.1051/0004-6361:20035959

    2020 doi

  22. [30]

    D., Buzasi, D., Otani, T., et al

    Oswalt, T. D., Buzasi, D., Otani, T., et al. 2022, Zenodo, doi: 10.5281/zenodo.7406311

    2022 doi

  23. [31]

    M., Stauffer, J

    Rebull, L. M., Stauffer, J. R., Bouvier, J., et al. 2016, AJ, 152, 114, doi: 10.3847/0004-6256/152/5/114

    2016 doi

  24. [32]

    Santos, A. R. G., Breton, S. N., Mathur, S., & Garc ´ ıa, R. A. 2021, ApJS, 255, 17, doi: 10.3847/1538-4365/ac033f

    2021 doi

  25. [33]

    Santos, A. R. G., Cunha, M. S., Avelino, P. P., Garc ´ ıa, R. A., & Mathur, S. 2017, A&A, 599, A1, doi: 10.1051/0004-6361/201629923

    2017 doi

  26. [34]

    Santos, A. R. G., Garc ´ ıa, R. A., Mathur, S., et al. 2019, ApJS, 244, 21, doi: 10.3847/1538-4365/ab3b56

    2019 doi

  27. [35]

    1972, ApJ, 171, 565, doi: 10.1086/151310

    Skumanich, A. 1972, ApJ, 171, 565, doi: 10.1086/151310

    1972 doi

  28. [36]

    G., Oelkers, R

    Stassun, K. G., Oelkers, R. J., Pepper, J., et al. 2018, AJ, 156, 102, doi: 10.3847/1538-3881/aad050

    2018 doi

  29. [37]

    W., Winn, J

    Sullivan, P. W., Winn, J. N., Berta-Thompson, Z. K., et al. 2015, The Astrophysical Journal, 809, 77, doi: 10.1088/0004-637X/809/1/77

    2015 doi

  30. [38]

    2018,, v0.1

    Vanderspek, R., Doty, J., Fausnaugh, M., & et al. 2018,, v0.1

    2018

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.