Precise Radial Velocities of Cool Low-mass Stars with iSHELL
Bibliographic record
Abstract
Abstract The coolest dwarf stars are intrinsically faint at visible wavelengths and exhibit rotationally modulated stellar activity from spots and plages. It is advantageous to observe these stars at near-infrared (NIR) wavelengths (1–2.5 μ m) where they emit the bulk of their bolometric luminosity and are most quiescent. In this work, we describe our methodology and results in obtaining precise radial velocity (RV) measurements of low-mass stars using K -band spectra taken with the R ∼ 80,000 iSHELL spectrograph and the NASA Infrared Telescope Facility using a methane isotopologue gas cell in the calibration unit. Our novel analysis pipeline extracts RVs by minimizing the rms of the residuals between the observed spectrum and a forward model. The model accounts for the gas cell, tellurics, blaze function, multiple sources of quasi-sinusoidal fringing, and line spread function of the spectrograph. The stellar template is derived iteratively using the target observations themselves through averaging barycenter-shifted residuals. We have demonstrated 5 m s −1 precision over one-year timescales for the M4 dwarf Barnard’s Star and K dwarf 61 Cygni A, and 3 m s −1 over a month for the M2 dwarf GJ 15 A. This work demonstrates the potential for iSHELL to determine dynamical masses for candidate exoplanets discovered with the NASA Transiting Exoplanet Survey Satellite mission, and to search for exoplanets orbiting moderately active and/or young K & M dwarfs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".