Transmission spectroscopy of WASP-52 b with <i>JWST</i> NIRISS: water and helium atmospheric absorption, alongside prominent star-spot crossings
Bibliographic record
Abstract
ABSTRACT In the era of exoplanet studies with JWST, the transiting, hot gas giant WASP-52 b is an excellent target for atmospheric characterization through transit spectroscopy. WASP-52 b orbits an active K-type dwarf recognized for its surface heterogeneities, such as star-spots and faculae, which pose challenges to atmospheric characterization via transmission spectroscopy. Previous transit observations have detected active regions on WASP-52 through crossing events in transit light curves and via the spectral imprint of unocculted magnetic regions on transmission spectra. Here, we present the first JWST observations of WASP-52 b. Our Near Infrared Imager and Slitless Spectrometer/Single Object Slitless Spectroscopy (NIRISS/SOSS) transit observation, obtained through the GTO 1201 programme, detects two clear spot-crossing events that distort the 0.6–2.8 $\mu$m transit light curves of WASP-52 b. We find that these two occulted spots cover together about 2.4 per cent of the stellar surface and have temperatures about 400–500 K cooler than the stellar photosphere. Our NIRISS/SOSS transmission spectrum is best-fit by an atmosphere with H$_2$O (10.8$\sigma$), He (7.3$\sigma$, with evidence of an escaping tail at $\sim$2.9$\sigma$), hints of K (2.5$\sigma$), and unocculted star-spots and faculae (3.6$\sigma$). The retrieved H$_2$O abundance ($\log$ H$_2$O $\approx -4 \pm 1$) is consistent with a subsolar or solar atmospheric metallicity for two independent data reductions. Our results underscore the importance of simultaneously modelling planetary atmospheres and unocculted stellar heterogeneities when interpreting transmission spectra of planets orbiting active stars and demonstrate the necessity of considering stellar contamination models that account for both cold and hot active regions.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".