Characterizing the Near-infrared Spectra of Flares from TRAPPIST-1 during JWST Transit Spectroscopy Observations
Bibliographic record
Abstract
Abstract We present the first analysis of JWST near-infrared spectroscopy of stellar flares from TRAPPIST-1 during transits of rocky exoplanets. Four flares were observed from 0.6–2.8 μ m with the Near Infrared Imager and Slitless Spectrograph and 0.6–3.5 μ m with the Near Infrared Spectrograph during transits of TRAPPIST-1b, f, and g. We discover P α and Br β line emission and characterize flare continuum at wavelengths from 1–3.5 μ m for the first time. Observed lines include H α , P α –P ϵ , Br β , He i λ 0.7062 μ m, two Ca ii infrared triplet (IRT) lines, and the He i IRT. We observe a reversed Paschen decrement from P α –P γ alongside changes in the light-curve shapes of these lines. The continuum of all four flares is well described by blackbody emission with an effective temperature below 5300 K, lower than the temperatures typically observed at optical wavelengths. The 0.6–1 μ m spectra were convolved with the Transiting Exoplanet Survey Satellite (TESS) response, enabling us to measure the flare rate of TRAPPIST-1 in the TESS bandpass. We find flares of 10 30 erg, large enough to impact transit spectra occur at a rate of 3.6 − 1.3 + 2.1 flare day −1 , ∼10× higher than previous predictions from K2. We measure the amount of flare contamination at 2 μ m for the TRAPPIST-1b and f transits to be 500 ± 450 and 2100 ± 400 ppm, respectively. We find up to 80% of flare contamination can be removed, with mitigation most effective from 1.0–2.4 μ m. These results suggest transits affected by flares may still be useful for atmospheric characterization efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".