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This paper shows that fitting for starspot crossings during exoplanet transits recovers transit depths to a median error of 0.78%, and that this approach beats simply masking the crossing whenever the spot's contamination of the transit dep

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2026-08-04 00:00 UTC pith:K4M4CMLB

load-bearing objection A useful validation study with a genuinely new degeneracy-grid method, but the headline fit-vs-mask thresholds are built on same-model injection-recovery and shouldn't be taken at face value for real spots. the 4 major comments →

arxiv 2511.03045 v2 pith:K4M4CMLB submitted 2025-11-04 astro-ph.EP astro-ph.SR

Quantifying the Impact of Starspot-Crossing Events on Retrieved Parameters from Transit Lightcurves

C. A. Murray , Z. Berta-Thompson This is my paper
classification astro-ph.EP astro-ph.SR
keywords starspotstransit light curvesspot-crossing eventstransit light source effectinjection-recoverytransmission spectroscopyexoplanet atmospheresdegeneracy mapping
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper addresses a practical question facing observers of cool stars with JWST: when a planet transits across a starspot, should the anomalous light curve be masked out or modeled explicitly? By injecting 1000 synthetic spot-crossing events into simulated single-wavelength transits and recovering them with a joint transit-plus-spot model, the authors find that fitting recovers the true transit depth to a median error of 78 parts per million (0.78%), with 80% of recoveries within 0.6%. For spots that inflate the transit depth by more than 1.3% through the Transit Light Source Effect, fitting is better than masking in over 95% of cases; for very small or low-contrast spots, masking is usually safer. The paper also shows that spot-crossing events, even when fitted, can inflate transit depth uncertainties by 10–100x at JWST-like precision, and introduces a method using the observable spot-crossing epoch, duration, and bump amplitude to carve out the degenerate spot parameter space and provide informative priors for Markov Chain Monte Carlo sampling, demonstrated on the JWST transit of Kepler-51d.

Core claim

The central claim is that starspot-crossing events, traditionally treated as nuisance signals to be masked, can be modeled directly in single-wavelength transit light curves to recover both the true transit depth and meaningful constraints on spot properties. Using spherical-harmonic surface maps with Gaussian smoothing to generate and fit the events, the authors find that for high signal-to-noise crossings (SNR above 4), spot longitudes are recovered tightly (80% within 1 degree), while latitude, radius, and contrast remain entangled in a known degeneracy. The recovered transit depth has a median error of 78.3 ppm, and the key decision rule emerges from the contamination factor epsilon: whe

What carries the argument

The central object is the time-dependent transit depth formula D(t) = [1 - g(t) C] epsilon D_true, where g(t) is the fraction of the planet's shadow covered by the spot, C is the spot contrast relative to the photosphere, and epsilon is the unocculted-spot contamination factor. This identity connects the shape of the spot-crossing bump directly to spot properties and to the depth inflation that masking would leave uncorrected. The paper uses this relationship both to interpret injection-recovery results and to build the degeneracy grid: for each spot radius, latitude, and longitude, they precompute the predicted spot-crossing epoch, duration, and bump amplitude, then filter the grid by the t

Load-bearing premise

The quantitative accuracy claims (0.78% depth error, 80% within 0.6%) assume that the forward model used to generate the synthetic spot-crossings—a smooth spherical-harmonic surface with Gaussian smoothing—is representative of real starspots; if real spot edges and intensity profiles differ from this smooth representation, the numbers will not transfer directly to real data.

What would settle it

Inject synthetic spot-crossing events into simulated light curves using a hard-edged circular spot model (rather than the smooth spherical-harmonic model), then attempt to recover the transit depth with the paper's fitting setup; if the median depth error exceeds the claimed 0.78% or the >95% improvement-over-masking threshold fails for epsilon >1.3%, the model-dependence of the results is demonstrated.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Observers with high-signal-to-noise spot-crossings on JWST-like precision should fit the crossing rather than mask it, avoiding reliance on imperfect stellar spectral models for Transit Light Source Effect corrections.
  • The recovered spot contrast from a fitted crossing can seed an empirical correction for unocculted spots of the same temperature, requiring only a fit to the remaining covering fraction.
  • The 1.3% contamination threshold and the caution for spots with contrast below 5% or covering fraction below 2% give a practical decision rule for when fitting is likely to help or harm depth recovery.
  • The degeneracy-carving method converts three easy-to-measure quantities (epoch, duration, bump amplitude) into informative priors, substantially speeding up MCMC sampling for spot-crossing fits.
  • The finding that depth uncertainties inflate by 10–100x at JWST precision calls for conservative margin in exposure time calculations for spotty stars.

Where Pith is reading between the lines

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

  • Because the injection-recovery statistics are generated with the same smooth spherical-harmonic model used for fitting, the quantitative error numbers (0.78% depth error, 80% within 0.6%) are likely optimistic for real spot morphologies; repeating the test with hard-edged spot models would gauge how much the biases depend on the model assumption.
  • The three-observable degeneracy grid could be extended to multi-wavelength data: since spot contrast is wavelength-dependent while radius and position are not, a simultaneous fit across channels may break the radius-latitude degeneracy that persists in single-band fits.
  • The decision rule (fit vs mask) is derived for a specific stellar geometry; for real systems with different limb-darkening, impact parameter, or spot distributions, the 1.3% threshold may shift, so the paper's prescription is best re-calibrated per system via injection-recovery on the actual light curve.
  • The idea that spot-crossings can provide empirical contrast spectra without stellar models suggests a broader program: stacking many transits of the same star could build a map of the stellar surface and its temperature variations, turning stellar noise into a tool for studying stellar activity.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper presents a systematic injection-recovery study of starspot-crossing events (SCEs) in single-wavelength transit light curves, using the starry forward model and the authors' chromatic fitting tool. A population of 1000 synthetic SCEs is generated with a range of spot contrasts, radii, positions, and noise levels, then recovered via MAP optimization (with 13 starting positions) and, for five representative cases, full MCMC sampling. For high-SNR events (SNR≥4), the authors report median transit-depth errors of 78.3 ppm (0.78%) and state that 80% of depths are recovered within 253 ppm (or, in the abstract, 0.6%). They further compare fitting versus masking and conclude that when the transit light source effect (TLSE) exceeds 1.3%, fitting improves depth recovery in >95% of cases. The paper also introduces a degeneracy-grid method that maps SCE observables to spot parameter constraints and demonstrates it on a real JWST transit of Kepler-51d. The central recommendation is that modeling SCEs is generally preferable to masking, and that spot contrasts can be inferred empirically without recourse to stellar atmosphere models.

Significance. If the quantitative results hold, the paper provides practical guidance for the exoplanet community: for high-SNR JWST-like transits, fitting SCEs rather than masking them can improve transit-depth accuracy, and the recovered spot contrast offers a data-driven path to correcting the TLSE. The degeneracy-grid approach is a useful, reproducible tool for deriving informative priors and visualizing degenerate spot solutions. The paper is well organized, clearly describes its methods, and makes its software available. However, the headline numbers rest on a same-model injection/recovery design, which limits their transferability to real stellar surfaces; the abstract and body also contain a numerical inconsistency in the quoted depth-accuracy percentile. These issues affect the paper's central claims and require attention.

major comments (4)
  1. [§3.2, §4.1, §7.2] Both injection and recovery use starry with spherical-harmonic order 30 and Gaussian smoothing 1/30, so the recovery model is identical to the generative model. This inverse-crime setup likely yields optimistic accuracies. §7.2 shows that starry and spotrod produce substantially different spot parameters for Kepler-51d (Rspot 12° vs 17.4°, latitude −9.7° vs −30.3°), demonstrating model dependence. The headline numbers (78.3 ppm, 80% within 253 ppm, >95% for ϵ>1.013) are therefore not directly transferable to real data. Please add cross-model tests (e.g., inject with spotrod, recover with starry) or explicitly present the results as conditional on the starry model family.
  2. [Abstract, §4.1, §8] The abstract states that 80% of depths are recovered within 0.6%, while the body states 80% within 253 ppm 'or 0.6%'. Given D_true=10,000 ppm, 253 ppm equals 2.53%, not 0.6%. This numerical inconsistency affects the central quantitative claim. Please correct the abstract or the body so the numbers agree and the percentage is computed consistently relative to D_true.
  3. [§4.2, Abstract] The statement that spots with C<5% or f<2% are likely to be better masked is presented as a finding, but the injected population has C~U(0.05,1), so C<5% is never tested. The f<2% statement is also based on extrapolation from the tested population. This claim appears in the abstract and conclusions. Please either include simulations for C<5% or clearly label this as an untested extrapolation.
  4. [§5.1] The uncertainty-inflation factors (10–100× for JWST-like precision) are derived from the standard deviation of MAP point estimates within bins, not from posterior sampling. Only five events have full MCMC, and those chains show Gelman-Rubin >1.1 for degenerate parameters. The claim that SCEs inflate depth uncertainties by 10–100× is therefore not robustly supported. Recommend computing posterior uncertainties for a larger subset or presenting this as a qualitative observation.
minor comments (4)
  1. [Table 4, Abstract] The abstract says 'on average' but Table 4 reports medians. Use 'median' for consistency.
  2. [Figure 13] Panel (f) colorbar label is unclear; 'b spot' likely should be 'ΔD_spot'. Consider clarifying in the caption.
  3. [§6.3] The degeneracy grid uses hard-edged circular spots while the injection-recovery uses smoothed starry spots. The paper could state more explicitly that the degeneracy-grid results are model-dependent and may differ from starry-based fits.
  4. [§8] The reference to '~200 ppt' from Rackham & Wit (2023) is ambiguous; specify whether ppt means parts per thousand or parts per million.

Circularity Check

1 steps flagged

No significant circularity: the headline numbers are injection-recovery measurements, not identities; the paper's own §4.2 circularity caveat is acknowledged and does not drive the central derivation.

specific steps
  1. other [Section 4.2, paragraph after Figure 8 (small/low-contrast spot discussion)]
    "We acknowledge that this is a circular problem – here we must model the spot-crossing event first to derive ϵ/C/f. However, if we have additional observations of the system, such as the rotational photometric variability, this could provide some constraint on the spot parameters without fitting the SCE."

    The paper's fit-versus-mask guidance for small or low-contrast spots depends on knowing the TLSE contamination factor ϵ, spot contrast C, and covering fraction f, yet those quantities are themselves outputs of the SCE fit. This is an operational circularity in applying the recommendation, not a mathematical identity: the headline recovery statistics (median 78.3 ppm depth error, 80% within 253 ppm, >95% improvement for ϵ≥1.013) are measured injection-recovery outcomes, not definitions. The authors explicitly flag the issue and suggest independent rotation-modulation priors as an escape, so it is a disclosed limitation rather than a hidden reduction.

full rationale

An honest reading of the derivation chain: the central quantitative results — median |ΔD|=78.3 ppm/0.78%, 80% within 253 ppm, spot-parameter percentile accuracies, and the >95% improvement over masking for ϵ≥1.013 — are empirical outputs of a 1000-injection recovery experiment (Section 4, Tables 4, Figures 4–8). The recovered depth is not the injected depth by construction; the spread is measured and large for low-SNR/limb cases (Figures 14–15), so this is a benchmark rather than an identity. The masking comparator is defined from the independently derived TLSE formula (Eq. 9) as ϵD_true, and the fitted depth is compared to it; the improvement fraction is a measured statistic, not a tautology. The one explicitly circular passage is in §4.2, where the fit-vs-mask decision for small/low-contrast spots requires ϵ/C/f that must themselves come from the SCE fit; the authors flag this and propose independent rotational-variability priors. This is a practical workflow caveat and does not reduce any headline number to its input. Self-citations to chromatic, chromatic fitting, and the Libby-Roberts et al. (2025) Kepler-51d analysis are software/data citations, not imported uniqueness theorems; the paper explicitly frames itself as the first comprehensive validation of its own code, which is a testable, open-source pipeline. The same-model starry injection/recovery is the main validity limitation (an inverse-crime): §7.2 itself shows hard-edged spotrod and smoothed starry diverge (R=12° vs 17.4°, ϕ=-9.7° vs -30.3°), so the numerical accuracies are model-relative. But model-relative benchmarking is a soundness/external-validity concern, not circularity. Internal inconsistency between '0.6%' and 253 ppm (2.5% of 10,000 ppm) is a reporting error, not a circular step.

Axiom & Free-Parameter Ledger

5 free parameters · 5 axioms · 0 invented entities

The quantitative claims are conditioned on a synthetic population chosen by hand (Eq. 1) and on starry's smooth spot model. The paper introduces no new physical entity; its new content is an analysis methodology and validation statistics.

free parameters (5)
  • Injection population bounds (C, Rspot, y, sigma) = C in [0.05,1], Rspot in [5,45] deg, y in [-0.8,0] Rstar, sigma in [1e-5,1e-2]
    Chosen by hand in Eq. (1); all recovery percentages (0.78%, 80% within 0.6%) are averages over this synthetic population, so they inherit these assumptions.
  • starry spot smoothing parameter = 1/30 (Gaussian sigma in spherical-harmonic map)
    Set in §4.1; low smoothing preserves contrast but leaves ringing artifacts. Changes SCE shape and thus recovery accuracy; not externally constrained.
  • Spherical harmonic degree = 30 for injection/MAP, 26 for MCMC
    Chosen for computational feasibility (§4.1, §5); spot morphology and minimum resolvable radius depend on this setting.
  • Spot radius lower-bound prior = 5 deg
    Imposed in retrieval (Table 1 and §4.1); the paper notes it can bias small-spot overcorrection (§4.2).
  • Kepler-51d observable uncertainties = sigma_deltaD = sqrt(2)*sigma; sigma_deltat = sqrt(2)*sigma_t
    Adopted in §7.2 to define the degeneracy-grid acceptance region; changing them changes the inferred R>=12 deg and lambda in [7,15] deg bounds.
axioms (5)
  • domain assumption Starspots are approximately circular, uniform-contrast features on a spherical surface (hard-edged in §6, Gaussian-smoothed spherical harmonics in §3).
    Used throughout; §7.2 explicitly shows this assumption shifts Kepler-51d spot latitude/radius posteriors between starry and spotrod.
  • domain assumption The stellar surface and spot do not evolve during the ~4-hour transit; rotation period fixed to 1000 d and single wavelength.
    Stated in §3.2; excludes spots rotating in/out and spot evolution, which real JWST transits can show.
  • domain assumption Limb darkening affects spot, transit chord, and photosphere in the same way; no second-order spot limb-darkening terms.
    Stated in §3.3.1; if spots limb-darken differently, delta D_spot predictions change.
  • domain assumption The recovery model's functional form (TransitSpotModel in starry) is the true data-generating process for injections.
    Sections 3-4 use starry for both injection and recovery; this same-model benchmark may overstate transferability to real light curves.
  • domain assumption All spots on the host star share a common temperature/contrast spectrum.
    Invoked in §4.3 and §8 to extend an occulted spot's contrast to unocculted contamination.

pith-pipeline@v1.3.0-alltime-deepseek · 25465 in / 14692 out tokens · 132346 ms · 2026-08-04T00:00:14.959046+00:00 · methodology

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Cite this review

Pith. "Pith review of Quantifying the Impact of Starspot-Crossing Events on Retrieved Parameters from Transit Lightcurves." pith.science (2026). https://pith.science/paper/K4M4CMLB

@misc{pith2026251103045,
  author       = {Pith},
  title        = {Pith review of: Quantifying the Impact of Starspot-Crossing Events on Retrieved Parameters from Transit Lightcurves},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K4M4CMLB}},
  note         = {Machine review of arXiv:2511.03045}
}
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read the original abstract

Starspot-crossing events (SCEs) in exoplanet transit lightcurves are becoming increasingly common as we focus on cooler host stars and observe higher precision photometric and spectroscopic lightcurves. In this work, we explore how these events affect our retrievals of transit depths and the accuracy with which we can derive spot properties. We inject and recover synthetic SCEs in photometric lightcurves using starry. We find that for high signal-to-noise ratio SCEs, we constrain the spot longitudes tightly (>80% within 1{\deg} of the true value) but degeneracies complicate retrieving spot contrasts, radii, and latitudes (within 17%, 19%, and 9{\deg} respectively). On average, the difference between injected and recovered transit depths is 0.78% or 78.3 ppm. In most (80%) injections, we recover the transit depth to within 0.6%. For transit depths inflated >1.3% by the transit light source effect (TLSE), fitting for a spot crossing improves the transit depth retrieval over masking the SCE in >95% of cases. However, we find that for spots with small contrasts (<5%) and/or covering fractions (<2%), we are likely to overcorrect for the TLSE, recovering a worse transit depth than simply masking. In addition, even when fitted, we find SCEs can inflate the uncertainties on recovered transit depths significantly, especially for JWST-like precisions. Finally, we present a new method, using SCE observables, of carving out the degenerate spot parameter space that provides informative priors for Markov Chain Monte Carlo sampling, demonstrating this technique on a real SCE observed in Kepler-51d's lightcurve.

Figures

Figures reproduced from arXiv: 2511.03045 by C. A. Murray, Z. Berta-Thompson.

Figure 1
Figure 1. Figure 1: We present five samples from Section 3.2 to demonstrate a range of light curve uncertainties and spot parameters. Upper: The starry stellar surface maps for each injection scenario with quadratic limb-darkening and the spot. There are slight ringing artifacts from the choice to reduce the smoothing parameter as discussed in Section 4.1. The transit chord is shown by the shaded gray region on each map. Midd… view at source ↗
Figure 2
Figure 2. Figure 2: For the 1000 injected spot samples; Left: radius of the spot (in degrees), Rspot, against the contrast between spot and quiescent photosphere. Right: the projected location of the spot centers on the surface of the star. The top half of the star is shaded as we only inject spots into one half to avoid the symmetrical degeneracy in recovery. Below y ≤ −0.8 is also shaded as the largest spot we inject, Rspot… view at source ↗
Figure 3
Figure 3. Figure 3: For the fourth spot-crossing scenario in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Recovered vs injected spot contrast, radius, latitude, longitude, and transit depth, for (a) all SCEs and (b) SCEs with SNR≥4. The points marked with crosses are spots whose projections overlap with the stellar limb. Limb spots are much more difficult to fit and have intrinsically much more uncertainty. The colorbar represents the log(SNR) of each SCE. 4.2. Retrieval of transit parameters when fitting vs m… view at source ↗
Figure 5
Figure 5. Figure 5: For SNR<4 (dashed lines) and ≥4 (solid lines) we show five recovered parameters: spot contrast (top left), radius (top right), latitude (middle left), longitude (middle right) and planetary radius (bottom left). On the left axis (black) we plot the percentile of the absolute difference be￾tween injected, i, and recovered, r. On the right axis (colors) we show the percentage difference from the injected val… view at source ↗
Figure 6
Figure 6. Figure 6: The injected (purple) and recovered (gold) centre positions of the 694 SCEs with SNR≥4. A line is plotted joining the two positions, with its color determined by the distance between injected and recovered (in planetary radii, Rp). The spots that occur on or near the stellar limb have the largest differences between the injected and recovered positions. The planet size is plotted in the lower right corner … view at source ↗
Figure 8
Figure 8. Figure 8: The spot contrast and covering fraction, f, for all SCEs with SNR≥ 4. Scenarios where fitting for SCEs improves the transit depth recovery, compared to masking, are marked in purple and vice versa in gold. The dotted lines correspond to ϵ = 1.087 (dark purple), 1.027 (medium pur￾ple), and 1.013 (dark gold). When ϵ > 1.087 [1.027, 1.013], fitting improves recovery of the transit depth in 100% [98%, 95%] of … view at source ↗
Figure 7
Figure 7. Figure 7: A comparison of the recovered transit depths, D, from fitting the spot-crossing vs. masking. In the masking case we assume that we do not have any prior information on the stellar contamination and so the recovered depth would simply be the contaminated depth ϵDtrue. Upper: The recov￾ered depths from fitting, Dobs, against the injected contami￾nated depths. Spots on the limb are marked by crosses. The dott… view at source ↗
Figure 9
Figure 9. Figure 9: Five star plots: The five injection-recovery scenarios shown in [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Top: Recovered transit depth as a function of injected per-point uncertainties divided by the square-root of the number of in-transit data points, σ/√ Ntransit (to remove dependence on cadence). The SCE injection-recovery results (Section 4, SNR>4) are binned in logσ=0.5 steps; the median recovered transit depths and their standard deviations within each bin are shown in purple. Results from full MCMC sam… view at source ↗
Figure 11
Figure 11. Figure 11: Spot-crossing duration, ∆tspot, and spot￾crossing epoch, tspot, plotted as a function of ϕspot, λspot, and Rspot for the 1◦ -increment grid described in Section 6.2.1. predicted observables lie within those error bounds. This filtering step yields a sub-grid of degenerate so￾lutions, each with a spot contrast, radius, latitude, and longitude consistent (within some uncertainty) with the observed event. Th… view at source ↗
Figure 12
Figure 12. Figure 12: Animation demonstrating the degeneracy region as a function of spot ϕspot (left), λspot (middle), and Rspot (right), while keeping the other two parameters fixed in each case. Top: The base transit lightcurve in dotted black and the spot crossing in purple. The resulting ∆tspot and tspot (and error regions) are indicated with vertical dotted purple lines. Middle: the star with the spot shown in black, the… view at source ↗
Figure 13
Figure 13. Figure 13: (a) JWST white light curve for Kepler-51d. Best-fit transit and 2-spot model (starry), with a 2nd order polynomial, is shown in orange. The dotted line is the same transit model without the spot-crossings, rescaled to the same contaminated transit depth as the starry model. We label tspot, the spot-crossing epoch, and ∆tspot, the spot-crossing duration, along with their uncertainties. (b) Residuals betwee… view at source ↗
Figure 14
Figure 14. Figure 14: The five largest outliers in recovered depth (in descending order). The layout of this plot is the same as [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The five scenarios with the smallest recovered (most over-corrected) transit depths. The layout of this plot is the same as [PITH_FULL_IMAGE:figures/full_fig_p020_15.png] view at source ↗

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