Improved surface gravity and mass constraints for substellar objects from spectral line profile measurements at high resolution in the near-infrared
Bibliographic record
Abstract
Observed extra-solar systems within the Milky Way are comprised of stars and substellar objects (planets and brown dwarfs). Standard methods for measuring the mass of isolated brown dwarfs and directly-imaged giant planets are indirect, and rely heavily on largely uncalibrated theoretical models. Consequently, current mass estimates for many of these substellar objects are highly uncertain. With the arrival of new high resolution instruments such as SPIRou comes opportunity for new methods and improved constraints. We present an observational method to constrain the mass of substellar objects precisely, and demonstrate its feasibility on simulated SPIRou observations. We use a cross correlation technique to find the average shape of the absorption lines in an object’s spectrum, and determine its surface gravity to high precision through quantitative comparison to reference models. The average line profile width has the properties of being dependent on surface gravity and independent on the choice of reference model. Our results suggest that by using the average line profile, surface gravity can be constrained to better than 5%, and mass can subsequently be estimated to a precision of 10-15%. Performing our method on real high-resolution observations will provide the ultimate test.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".