Detecting Exoplanets Closer to Stars with Moderate Spectral Resolution Integral-field Spectroscopy
Bibliographic record
Abstract
Abstract While radial velocity surveys have demonstrated that the population of gas giants peaks around 3 au, the most recent high-contrast imaging surveys have only been sensitive to planets beyond ∼10 au. Sensitivity at small angular separations from stars is currently limited by the variability of the point-spread function. We demonstrate how moderate-resolution integral-field spectrographs can detect planets at smaller separations (≲ 0.3“) by detecting the distinct spectral signature of planets compared to the host star. Using OSIRIS (R ≈ 4000) at the W.M. Keck Observatory, we present the results of a planet search via this methodology around 20 young targets in the Ophiuchus and Taurus star-forming regions. We show that OSIRIS can outperform high-contrast coronagraphic instruments equipped with extreme adaptive optics and non-redundant masking in the 0.05“–0.3“ regime. As a proof of concept, we present the 34σ detection of a high-contrast M dwarf companion at ≈0.1“ with flux ratio of ≈ 0.92% around the field F2 star HD 148352. We developed an open-source Python package, breads, for the analysis of moderate-resolution integral-field spectroscopy data in which the planet and the host star signal are jointly modeled. The diffracted starlight continuum is forward-modeled using a spline model, which removes the need for prior high-pass filtering or continuum normalization. The code allows for analytic marginalization of linear hyperparameters, which simplifies the posterior sampling of other parameters (e.g., radial velocity, effective temperature). This technique could prove very powerful when applied to integral-field spectrographs such as NIRSpec on the JWST and other upcoming first-light instruments on the future Extremely Large Telescopes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".