Apache Point Observatory follow-up of ACcelerating Candidate ExopLanet host Stars (APO ACCELS): Ages for 166 Accelerating Stars in the Northern Hemisphere
Bibliographic record
Abstract
Directly imaged substellar companions with well-constrained ages and masses serve as vital empirical benchmarks for planet formation and evolution models. Potential benchmark companions can be identified from astrometric accelerations of their host stars. We use Gaia DR3 and Hipparcos astrometry to identify 166 northern hemisphere stars with astrometric accelerations consistent with a substellar companion between 0.5'' and 1''. For this accelerating sample we identify young stars using APO/ARCES spectra and TESS light curves. From spectroscopic screening of the sample, we measure ages for 24 stars with detectable amounts of lithium, place lower age limits on 135 stars with lithium non-detections, and measure ages from R'HK for 34 stars. 129 stars have TESS light curves from which we measure ages for 20 stars with rotation rates < 15 days, and we identify 3 eclipsing binaries. We present median ages and confidence intervals of age posteriors for the entire sample and discuss how the overall age distribution of our sample compares to a uniform star formation rate in the solar neighborhood. We identify 47 stars with median ages < 2 Gyr, 31 stars with median ages < 1 Gyr, and 14 stars with median ages < 0.5 Gyr, making them high-priority targets for direct imaging follow-up.
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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 | 0.001 |
| 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".