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REVIEW 4 major objections 8 minor 101 references

The Maunder Model and Catalog: Stellar Rotation, Bimodal Activity, and Magnetic Braking in Kepler Main-Sequence Stars

T0 review · 4 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The Maunder catalog shows that for Kepler main-sequence stars whose light curves carry two distinct rotation-like signals, spectroscopic v sin i measurements identify the longer period as the true rotation, implying that classical…

desk verdict A large, carefully built Kepler rotation catalog, but the headline claim that ~26,000 bimodal stars were mis-assigned rests on an inclination prior that the paper itself undermines. read the letter →

arxiv 2608.06604 v1 pith:KTQKE4EU submitted 2026-08-06 astro-ph.SR

Ilay Kamai , Lavi Somers , Hagai B. Perets This is my paper · ORCID
classification astro-ph.SR
keywords stellarrotationperiodsKeplermain-sequencestarsmachinelearningperiodestimationjoint-embeddingself-supervisedbimodalsignalsAPOGEEvsinimagneticbrakinggyrochronology
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 builds a machine-learning pipeline, The Maunder, to measure rotation periods for 148,746 main-sequence stars in the Kepler field, and releases a catalog of 119,428 periods it considers reliable. Its central discovery comes from sliding-window inference: for 21.5% of the sample the model does not settle on one period but alternates between two, and a comparison with APOGEE projected rotation velocities shows that for non-harmonic double signals the longer period is the true spin, while the shorter one is an alias. The authors conclude that roughly 26,000 stars in earlier single-pass periodogram analyses have been systematically assigned the shorter, wrong rotation period. If correct, this corrects about 26,000 mis-perioded stars and anchors population studies of gyrochronology, magnetic braking, and binary synchronization on the largest Kepler main-sequence sample with per-star calibrated uncertainties.

What carries the argument

The load-bearing mechanism is rolling-window inference plus adjudication by projected rotation velocity. The model predicts period quantiles on 450-day windows slid across each roughly four-year light curve with a 90-day stride; a 2-means split in $\log_{10} P$ classifies a star as bimodal when two period clusters are separated by at least 0.20 dex with a clean partition, and splits near a 2:1 ratio are set aside as harmonic aliases. For distinct bimodals, a hierarchical forward model of APOGEE $v \sin i$ — built following the Masuda–Winn approach with isotropic inclinations, a 1.5 km/s detection truncation, and a macroturbulence-plus-measurement floor calibrated on a ~2,790-star unimodal control — decides which mode is the rotation: the long mode reproduces the observed $v \sin i$ distribution, while the short mode predicts an unphysical excess of rapid rotators. The six-channel input (two flux normalizations, two activity proxies windowed by a scaffold period, plus autocorrelation and Lomb–Scargle channels) and the hybrid objective (a joint-embedding self-supervised loss on all stars combined with a conformalized quantile-regression loss on 41,650 cross-catalog consensus labels) supply the calibrated per-star intervals that make the catalog usable.

What would settle it

The most direct settlement is to measure rotation periods for a sample of distinct bimodals by an independent technique, such as high-resolution time-series spectroscopy of spot-induced line-profile variations: if a substantial fraction rotate at the short mode, the central claim fails. A cheaper calculation is to refit the $v \sin i$ noise model to the bimodal sample itself and check whether the long-mode advantage ($D = 0.10$ versus $0.48$) survives down to the control calibration residual of $0.12$.

Watch

Extended reading notes

Core claim

The central claim is that rolling-window period inference exposes a bimodality that single-pass period searches cannot see, and that the two modes are not both physical: for distinctly separated, non-harmonic bimodals (25,357 of the 31,953 bimodal stars), the longer mode is the true rotation period, and the shorter mode is an alias. The evidence is a hierarchical forward model of APOGEE $v \sin i$ distributions: assuming isotropic inclinations and a noise floor calibrated on about 2,790 unambiguous-period control stars, adopting the long mode as the rotation reproduces the observed $v \sin i$ of the 390 distinct bimodals with APOGEE data as well as the control does (KS distance 0.10 versus 0.12), while the short mode predicts a large overabundance of rapid rotators ($D = 0.48$). The paper adopts the long mode for distinct bimodals in the released catalog, flags 2:1 harmonic splits as ambiguous because $v \sin i$ cannot resolve them, and publishes both candidate modes for every star so users can revert the choice. With a confidence-interval cut the catalog yields 119,428 stars, and the paper uses it to recover the metallicity dependence of rotation at fixed mass, the jump in equatorial velocity and specific angular momentum across the Kraft break, the empirical gyrochronology sequences, and a candidate population of hierarchical triples among synchronized binaries.

Load-bearing premise

The conclusion that the long mode is the true rotation for roughly 26,000 stars rests on a single noise model for APOGEE projected rotation velocities, calibrated on 2,790 unambiguous stars and applied unchanged to 390 bimodal stars; if those two populations differ in inclination distribution, detection floor, or line-broadening behavior, the long-mode verdict is not established.

Editorial extensions

If this is right

  • About 26,000 main-sequence stars with distinct bimodal signals carry rotation periods that classical single-pass periodogram analyses systematically set to a shorter alias; the catalog instead adopts the long mode.
  • The confidence-filtered catalog of 119,428 stars, with calibrated per-star uncertainties, provides rotation periods for the largest Kepler main-sequence population, supporting population-level gyrochronology, spin-orbit, and magnetic-braking studies.
  • At fixed stellar mass above 0.85 $M_{\odot}$, median rotation period increases monotonically with metallicity (for example from 14.0 to 21.5 days at 1.0–1.15 $M_{\odot}$), a trend opposite to the age–metallicity relation, indicating metallicity-dependent magnetic braking.
  • Equatorial velocities and specific angular momenta traced directly from periods and radii rise steeply across the Kraft break, from roughly 10–20 km/s at 1.3 $M_{\odot}$ to about 100 km/s at 1.6 $M_{\odot}$.
  • A regime of short rotation periods at wide orbital separations among known binaries is interpreted as hierarchical triples, with elevated astrometric noise and one confirmed triple system (KID 6525196) supporting the identification.

Reading between the lines

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

  • The mis-assignment rate should be concentrated rather than uniform: because the paper's own spot-lifetime analysis ties bimodality to short-lived spots, earlier catalogs' fast-rotator populations made of low-coherence, low-amplitude stars are the most likely to harbor alias-contaminated periods, and a star-by-star comparison of classical periods against the long modes would show where the damage i
  • The $v \sin i$ adjudication is statistical, resting on only 390 stars with APOGEE data; as larger spectroscopic surveys provide $v \sin i$ for the remaining ~25,000 distinct bimodals, the long-mode rule could gain per-star verification or reveal sub-populations, such as genuinely mode-switching stars, that violate it.
  • The hierarchical-triple interpretation of the wide-orbit, short-rotation regime predicts that the short-period component is itself a close binary and that the wide companion should be visible in radial velocities; a handful of RV epochs for regime-B stars would settle the interpretation.
  • Even if the $v \sin i$ adjudication were weakened, the catalog would not lose all value: the consensus-grounded periods and calibrated intervals would still support relative population comparisons, and only the period-source choice for distinct bimodals would need revisiting.
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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 / 8 minor

Summary. The paper presents The Maunder, a machine-learning pipeline that predicts rotation periods for 148,746 main-sequence stars in the Kepler field. The model uses a hybrid objective: a self-supervised joint-embedding loss applied to all light curves, plus a supervised quantile-regression loss trained on cross-catalog consensus labels from four period catalogs. Rolling-window inference yields per-star period quantiles and identifies 31,953 bimodal stars (21.5%). Using APOGEE vsini and Berger et al. radii, the authors build a hierarchical forward model and argue that for distinct (non-harmonic) bimodals the longer period is the true rotation, implying that classical single-pass periodograms systematically lock onto shorter aliases for roughly 26,000 stars. A CI-based filter produces a 'highly reliable subset' of 119,428 stars, which is then used to recover gyrochronology sequences, a metallicity-rotation trend at fixed mass, equatorial velocities across the Kraft break, and candidate hierarchical triples among binaries. The paper also releases a per-star and a window-level catalog with quantiles, bimodality flags, and uncertainty diagnostics.

Significance. If the central long-mode claim holds, the paper identifies a population-level systematic error in previous rotation catalogs and provides the largest Kepler main-sequence rotation catalog with per-star calibrated uncertainties. The catalog design is thoughtful: the release of both per-star and window-level predictions, the explicit bimodality diagnostics, and the candid discussion of limitations (no direct error metric for the ambiguous majority, conformal coverage only on exchangeable stars, harmonic-aliased failures that survive the CI cut) are all strengths. However, the long-mode conclusion rests on an isotropy assumption that conflicts with the paper's own evidence that low-coherence (and hence bimodal) stars are preferentially low-inclination; this is load-bearing. The circular validation of the filtered catalog against the same catalogs that produced the training labels further limits the strength of the reliability claims. The catalog could still be a valuable resource even if the mode-adjudication claim needs revision, but the current evidence does not secure the headline result.

major comments (4)
  1. [Sec. 4.2 and Sec. 4.3, Fig. 9] The paper's own analysis implies that the bimodal sample violates the isotropy assumption of the vsini forward model. In Sec. 4.2, the authors argue from photometric amplitudes and planet-host fractions that low-coherence stars are preferentially low-inclination, and in Fig. 10 bimodals are shown to be disproportionately low-coherence. The forward model in Fig. 9 instead assumes cos i ~ U(0,1) for the bimodal population. If bimodals inherit a low-inclination bias, the short-mode hypothesis predicts an excess of low sini values, which the isotropic model interprets as an 'unphysical excess of rapid rotators' (D=0.48); the long mode's good fit (KSD=0.10) may then be an artifact of applying an isotropic model to a biased sample. The control calibration on N~2,790 unimodal stars does not test this, because those stars are not representative of the bimodal population. The headline claim that ~26,000 stars are mis-assigned by single-pass periodograms is therefore not secured by the presented evidence.
  2. [Sec. 4.3 and Appendix 7.2] The hierarchical forward model used to adjudicate the two modes is not described in enough detail to be reproduced. The text says the model follows Masuda & Winn (2020) and the appendix lists calibrated values (sigma_0 = 1.02, v_mac = 2.53, f = 0.05), but the likelihood, the prior on inclination, the treatment of the 1.5 km/s detection truncation, and the procedure for calibrating the noise parameters on the unimodal control are not given. Because this model is the sole quantitative evidence for the paper's central claim, the missing equations and algorithmic details are a load-bearing gap.
  3. [Sec. 4.4, Fig. 12] The reliability of the filtered subset is validated by agreement with the same catalogs that supplied the consensus training labels. The period distribution after the CI80/Prot<0.4 cut is compared with McQuillan et al. (2014) and Santos et al. (2021), both of which are among the four input catalogs used to build the labels. The held-out test set is likewise drawn from the same consensus construction, so the RMSE and coverage numbers do not provide independent evidence for the unlabeled majority. The paper correctly states in the limitations that no direct error metric exists for the ambiguous majority, but the specific claim in Sec. 4.4 that the filtered catalog 'agrees with previous catalogs' should be re-framed as a consistency check rather than independent validation.
  4. [Sec. 4.3, Fig. 9] The extrapolation from N=390 distinct bimodals with APOGEE vsini to all 25,357 distinct bimodals is not justified. APOGEE targets are subject to selection effects (brightness, temperature, and survey footprint), and no test is presented that the 390-star subset is representative of the full distinct-bimodal population in period, amplitude, coherence, or inclination. A selection-bias analysis or a demonstration that the long-mode preference is homogeneous across the parameter space of bimodals is needed before the population-level claim is supported.
minor comments (8)
  1. [Abstract] The phrase 'pointing on the role of metallicity' should be 'pointing to the role of metallicity'; the same wording appears in the main text.
  2. [Sec. 2.1] The definition of the consensus label is clear, but it is not stated whether the final average period is computed in linear or logarithmic space; please state this explicitly.
  3. [Sec. 4.1] The phrase 'they didn't use the same training labels' is informal for a journal paper; use 'they did not use'.
  4. [Fig. 6 caption] The term 'planet host stars' is undefined in the caption; please specify the source of the planet-host sample (e.g., confirmed/candidate Kepler planets) and the cross-match used.
  5. [Sec. 4.3] The harmonic-alias classification uses the threshold |Delta log10 P - log10 2| < 0.06 dex; please add a sentence justifying this tolerance.
  6. [Sec. 5] The catalog is stated to be 'available online upon publication'; for a catalog paper, please provide the expected archive/DOI or an anonymous access link for reviewers.
  7. [Sec. 4.6] The age normalization is taken from A. Sussholz et al. (2026), an arXiv preprint; please ensure the description of TAMS(M) and the YREC grid interpolation is self-contained or include a reference to the published version.
  8. [Throughout] There are minor typographical and formatting issues, including the missing space in 'The Maunderprovides' in the abstract and inconsistent use of 'vsini' versus 'v sin i' in equations and text.

Circularity Check

2 steps flagged · score 4.0 of 10

Supervised labels, RMSE reporting, and filtered-catalog validation all derive from the same consensus catalogs (including the authors' earlier catalog), so part of the accuracy and reliability argument is self-consistent by construction; the central APOGEE vsini long-mode test is external and independent.

  1. fitted input called prediction [Section 2.1 (Rotation Dataset) and Section 4.1 (Performance evaluation)]
    "Supervised labels are assigned only to stars with a consensus rotation period, which we define as agreement to within 20% between at least two of the reference catalogs of A. McQuillan et al. (2014), A. R. G. Santos et al. (2021), T. Reinhold et al. (2023), and I. Kamai & H. B. Perets (2025a). The final period label is the average period over all consensus catalogs."

    The 'true period' used for both supervised training and the headline RMSE (2.36 days) is defined as an average of the input catalogs, one of which is the authors' own Kamai & Perets (2025a) catalog. The held-out test RMSE therefore measures agreement with the same consensus-label construct that generated the target, not with an independent measurement of rotation period. The statement that the model is more precise than any individual catalog it learns from is partly a consequence of regressing to the average of those catalogs. The train/test split preserves some predictive content, so this is partial rather than total circularity.

  2. self definitional [Section 4.4 (Confidence intervals as a proxy to model uncertainty), Figure 12]
    "Another test for confidence intervals as uncertainty is shown in Figure 12, where we compare the resulting Prot distribution with the ones from A. McQuillan et al. (2014) and A. R. G. Santos et al. (2021). ... it seems like 0.4 is a reasonable upper limit on the 80% normalized CI, keeping 119,428 (~80%) samples that agree with previous catalogs."

    The 0.4 confidence-interval filter is declared reasonable because the filtered period distribution matches McQuillan et al. (2014) and Santos et al. (2021), which are exactly the catalogs used to construct the consensus training labels. The reliability claim for the 119,428-star filtered subset is therefore validated by agreement with the same inputs that trained the model. Because the filtered set also contains many unlabeled stars and the agreement is at distribution level rather than per-star, the circularity is partial, but the validation is not independent.

full rationale

The paper's central novel claim is the adjudication of distinct bimodals via APOGEE vsini: the hierarchical forward model is calibrated on a unimodal control sample and then applied to the bimodal sample, so the long-mode preference is anchored to an external spectroscopic dataset rather than to the consensus period labels. That part of the derivation is not circular, which prevents a score of 6 or higher. However, the supervised training target, the reported RMSE, and the filtered-catalog reliability assessment all reduce partly to the same consensus-catalog definition, and one of the four consensus catalogs is the authors' own prior work. The paper itself honestly notes that accuracy is only measured on the consensus subset and that conformal coverage carries no formal guarantee on the unlabeled population; those acknowledgements are in-scope and are weighed here. The skeptic's isotropy concern about the bimodal vsini test (low-coherence stars may be preferentially low-inclination, while the forward model assumes isotropic inclinations) is a substantive correctness risk but is not circularity, because the vsini comparison is an external test and the assumption is stated rather than smuggled in. Overall circularity is moderate: the catalog's internal validation is partly self-referential, but the headline long-mode result has independent empirical content.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claims rest on the consensus-label quality assumption, the vsini mode-adjudication model, the windowing/clustering definitions, and the transfer of the supervised mapping to the unlabeled majority. These are mostly domain assumptions rather than invented physical entities; no new particles, forces, or dimensions are introduced.

free parameters (5)
  • vsini forward-model noise floor sigma_0 = 1.02 km/s
    Calibrated on N~2,790 unimodal control stars to reproduce APOGEE vsini; then frozen and applied to bimodal mode comparison (Appendix Figure 18).
  • macroturbulence broadening v_mac = 2.53 km/s
    Same control calibration; part of the vsini likelihood.
  • multiplicative error f = 0.05
    Fixed multiplicative error in vsini model, calibrated on the unimodal control.
  • CI80/Prot reliability cutoff = 0.4
    Chosen in Section 4.4 so that the filtered distribution matches previous catalogs; a selection threshold that determines the 119,428-star subset.
  • bimodality split thresholds = separation >= 0.20 dex; minority >= 20%; silhouette > 0.60 or separation > 3x scatter
    Hand-selected criteria in Section 4.3 that define the 31,953 bimodal flags.
assumptions (5)
  • domain assumption Cross-catalog consensus labels (agreement within 20% between at least two of the four reference catalogs) are an unbiased ground truth for rotation periods on the labeled subset.
    Section 2.1 defines labels this way and Section 6 acknowledges shared catalog systematics (e.g., crowding) can propagate into labels.
  • ad hoc to paper The vsini forward model assumes isotropic inclinations (cos i uniform) and applies the control-calibrated macroturbulence, error floor, and truncation to the bimodal population.
    Section 4.3 and Appendix 7.3; needed to conclude the long mode is the true rotation; if wrong, the conclusion fails.
  • domain assumption Two 450-day windows drawn from the same star are views of the same underlying rotation signal suitable for joint-embedding self-supervision, and rolling windows with 90-day stride partition the light curve into independent period estimates.
    Sections 2.1, 3, 4 define the training and inference windows; period non-stationarity (spot evolution) is part of what the windowing is meant to expose.
  • domain assumption The model's self-supervised representation trained on all stars transfers the consensus-period mapping to the unlabeled, low-coherence majority, where no direct error metric exists.
    Section 6 explicitly states conformal coverage is only guaranteed for consensus-labeled stars and no direct error metric is available on the unlabeled set; the catalog's population-level claims rely on this transfer.
  • standard math Split conformalized quantile regression (Romano et al. 2019) gives marginally valid coverage under exchangeability.
    Section 3 uses it for calibrated intervals; standard result, but coverage on the unlabeled population is not formally guaranteed as the paper notes.

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Cite this review

Pith. "Pith review of The Maunder Model and Catalog: Stellar Rotation, Bimodal Activity, and Magnetic Braking in Kepler Main-Sequence Stars." pith.science (2026). https://pith.science/paper/KTQKE4EU

@misc{pith2026260806604,
  author       = {Pith},
  title        = {Pith review of: The Maunder Model and Catalog: Stellar Rotation, Bimodal Activity, and Magnetic Braking in Kepler Main-Sequence Stars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KTQKE4EU}},
  note         = {Machine review of arXiv:2608.06604}
}
abstract

We present The Maunder, a machine learning pipeline and resulting catalog of rotation periods for 148,746 main-sequence stars in the Kepler field. To overcome single-catalog systematics and the simulation-to-reality gap, our architecture employs a hybrid training objective: a joint-embedding self-supervised loss applied to all light curves, combined with a supervised loss trained strictly on cross-catalog consensus labels. By processing multi-scale time- and frequency-domain inputs over rolling windows, the model leverages conformalized quantile regression to output calibrated predictive intervals, providing statistically robust per-star rotation uncertainty metrics. This rolling-window inference reveals that 31,953 stars (21.5$\%$) exhibit bimodal rotational signals. By incorporating APOGEE $v \sin i$ measurements, we demonstrate that for distinct (non-harmonic) bimodals, the longer mode represents the true rotation, exposing a systematic failure mode wherein classical single-pass periodograms lock onto shorter aliases. Filtering by our calibrated confidence intervals yields a highly reliable subset of 119,428 stars. The catalog resolves various rotation-related phenomena: the metallicity dependence of rotation at fixed stellar mass, pointing on the role of metallicity in magnetic braking processes; tracing equatorial velocity and specific angular momentum directly across the Kraft break; recovery of empirical gyrochronology sequences and identification of hierarchical triple candidates among the synchronized-binary population. \emph{The Maunder} provides reliable rotation periods for the largest main-sequence population in \textit{Kepler}, allowing for population-level studies of rotation-based phenomena.

Figures

Figures reproduced from arXiv: 2608.06604 by the authors.

Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Diagram of our model. A light curve is split into two windows, processed by a shared-weights encoder. The two views are then sent into a DualFormer module and to prediction heads [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Testing scaffolding period, Pref, against the full model predictions. The right panel shows 460 samples for the held-out test set, where |Pref − Ptrue| ≥ 20%. The x-axis is Ptrue, and the y-axis shows Pref in red and predictions of the full model, which uses Pref as a scaffold, in blue. It can be seen that the blue points have lower error. The left panel shows the scaffold error as a function of the full model error… view at source ↗
Figures from the paper (21 more)
Figure 4
Figure 4. Figure 4: true period vs. predicted period (median quantile) for our model the trend in photometric amplitude, found in T. Mazeh et al. (2015) ( [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Analysis of coherence using the first and third ACF normalized peaks. The left panel is a peak-to-peak plot (PPP), which shows the relationship between peaks for different catalogs, including our new catalog. G. Basri et al. (2022) used such a plot to analyze spots’ li…
Figure 6
Figure 6. Figure 6: Left panel - photometric amplitude vs. Teff . The different colors represent different populations - high coherence unimodal stars (normalized ACF peak ≥ 0.2), low coherence unimodal stars (normalized ACF peak < 0.2), and planet host stars. The solid curves are the med…
Figure 7
Figure 7. Figure 7: Distribution of period ratios in bimodal stars [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Upper panel - full light curve of KID 892376. The shaded colors represent the different window predictions of our model (green - lower period). Lower panel - zoom in of two segments with very different periods. Gray dotted lines mark the predictions of A. McQuillan et …
Figure 9
Figure 9. Figure 9: Adjudicating the true rotation mode with APOGEE v sin i (N = 390 distinct bimodals). Grey: observed v sin i. Coloured: the hierarchical forward-model prediction (following K. Masuda & J. N. Winn 2020) when the long (teal) or short (red) mode is taken as the rotation pe…
Figure 10
Figure 10. Figure 10: Spot-group lifetime and bimodality. Left - The bimodal fraction rises monotonically as the spot-group lifetime in days, Lday (from the first normalized-ACF peak height following G. Basri et al. 2022), approaches unity; dashed line marks the overall fraction. Right - M…
Figure 11
Figure 11. Figure 11: Teff vs. Prot , colored by normalized 80% CI ( P0.9−P0.1 Prot ). Each panel uses a different upper bound on the normalized 80% CI. The number of points that satisfy the bound is written in each panel. The light blue line is the median Prot over bins of Teff [PITH_FU…
Figure 12
Figure 12. Figure 12: period distribution of A. McQuillan et al. (2014) (green), A. R. G. Santos et al. (2021) (red) and our main sequence predictions. The blue histogram shows the full main sequence dataset, and the orange histogram shows a filtered dataset, keeping only samples with CI80…
Figure 13
Figure 13. Figure 13: comparison between our periods and previous catalogs Color represents the normalized standard deviation of window predictions [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]
Figure 14
Figure 14. Figure 14: Teff vs. Prot of main sequence dataset filtered with CI80/Prot < 0.4. The dashed lines represent gyrochronology curves at different ages, using the model from L. G. Bouma et al. (2023). The difference between the panels is the coloring - the left panel uses no colors,…
Figure 15
Figure 15. Figure 15: scaled age vs. equatorial velocity (upper row) and SAM (lower row). Each panel shows a different mass bin. In each panel, the blue line is the median over scaled age bins [PITH_FULL_IMAGE:figures/full_fig_p016_15.png]
Figure 16
Figure 16. Figure 16: Adopted-Prot distributions (CI80/Prot < 0.4) in four [Fe/H] intervals, split into four stellar-mass bins. Vertical dotted lines mark the per-interval medians; legends give sample sizes and median periods, and each panel lists the metal-poor vs. metal-rich KS statistic…
Figure 17
Figure 17. Figure 17: Binaries in the Kepler field. Left - orbital vs. stellar period. Different colors and shapes represent different types of binaries. Regime A and regime B are detailed in the text. Right - Renormalized Unit Weighted Error (RUWE) of the entire binary population (gray) a…
Figure 18
Figure 18. Figure 18: Calibration of the v sin i forward model on the unimodal control (N = 2790). Under isotropic inclinations, the model with σ0 = 1.02, vmac = 2.53 km/s (macroturbulence and measurement floor), and a fixed multiplicative error f = 0.05, reproduces the observed APOGEE v s…
Figure 19
Figure 19. Figure 19: same as [PITH_FULL_IMAGE:figures/full_fig_p024_19.png]
Figure 20
Figure 20. Figure 20: same as [PITH_FULL_IMAGE:figures/full_fig_p024_20.png]
Figure 21
Figure 21. Figure 21: Photometric amplitude vs. Teff for planet hosts (red) and non-planet hosts (blue). This is a reproduction of [PITH_FULL_IMAGE:figures/full_fig_p025_21.png]
Figure 22
Figure 22. Figure 22: Dimensionality reduction of the 768 latent space dimensions of The Maunder using UMAP. Each panel colors the two UMAP dimensions with values of a different property [PITH_FULL_IMAGE:figures/full_fig_p026_22.png]
Figure 23
Figure 23. Figure 23: 3D UMAP colored by Prot [PITH_FULL_IMAGE:figures/full_fig_p027_23.png]
Figure 24
Figure 24. Figure 24: Difference between metallicity-period correlation (left panel) and metallicity-age correlation (right panel). Age is approximated through velocity dispersion - σ [PITH_FULL_IMAGE:figures/full_fig_p027_24.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.