Addressing Systematics in the Traceback Age of the β Pictoris Moving Group
Bibliographic record
Abstract
Abstract We characterize the impact of several sources of systematic errors on the computation of the traceback age of the β Pictoris moving group (βPMG). We find that uncorrected gravitational redshift and convective blueshift bias absolute radial velocity measurements by ∼0.6 km s−1, which leads to erroneously younger traceback ages by ∼2 Myr. Random errors on parallax, proper motion, and radial velocity measurements lead to an additional bias of ∼0.6 Myr on traceback ages. Contamination of astrometric and kinematic data by kinematic outliers and unresolved multiple systems in the full input sample of 76 members and candidates of βPMG also erroneously lowers traceback ages by ∼3 Myr. We apply our new numerical traceback analysis tool to a core sample of 25 carefully vetted members of βPMG using Gaia Data Release 3 data products and other kinematic surveys. Our method yields a corrected age of 20.4 ± 2.5 Myr, bridging the gap between kinematic ages (11–19 Myr) and other age-dating methods, such as isochrones and lithium depletion boundary (20–26 Myr). We explore several association size metrics that can track the spatial extent of βPMG over time, and we determine that minimizing the variance along the heliocentric curvilinear coordinate ξ ′ (i.e., toward the Galactic Center) offers the least random and systematic errors, due to the wider UVW space velocity dispersion of members of βPMG along the U-axis, which tends to maximize the spatial growth of the association along the ξ ′ -axis over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".