On the Effect of Stellar Activity on Low-resolution Transit Spectroscopy and the use of High Resolution as Mitigation
Bibliographic record
Abstract
Abstract We present models designed to quantify the effects of stellar activity on exoplanet transit spectroscopy and atmospheric characterization at low (R = 100) and high (R = 100,000) spectral resolution. We study three model classes mirroring planetary system archetypes: a hot Jupiter around an early-K star (HD 189733 b); a mini-Neptune around an early-M dwarf (K2-18 b); and terrestrial planets around a late-M dwarf (TRAPPIST-1). We map photospheres with temperatures and radial velocities (RV) and integrate specific intensity stellar models. We obtain transit spectra affected by stellar contamination, the Rossiter–McLaughlin effect (RME), and center-to-limb variations (CLV). We find that, at low resolution, for later-type stars, planetary water features become difficult to distinguish from contamination. Many distributions of unocculted active regions can induce planetary-like features of similar amplitudes in the case of a late-M dwarf. Atmospheric characterization of planets around late-type stars will likely continue to suffer from degeneracy with stellar activity unless active regions' parameters can be constrained using additional information. For the early-K star, stellar contamination mostly manifests itself through a slope at optical wavelengths similar to Rayleigh scattering. In all cases, contamination induces offsets in measured planet radii. At high resolution, we show that we can determine the origin of H2O and CO detection signals and lift the degeneracy observed at low resolution, provided sufficient planet RV variation during transit and adequate correction for the RME and CLV when required. High-resolution spectroscopy may therefore help resolve issues arising from stellar contamination for favorable systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".