Timescale of mass accretion in pre-main-sequence stars
Bibliographic record
Abstract
We present the initial result of a large spectroscopic survey aimed at measuring the timescale of mass accretion in young, pre-main-sequence stars in the spectral type range K0–M5. Using multi-object spectroscopy with VIMOS at the VLT we identified the fraction of accreting stars in a number of young stellar clusters and associations of the ages of between 1–30 Myr. The fraction of accreting stars decreases from ~60% at 1.5–2 Myr to ~2% at 10 Myr. No accreting stars are found after 10 Myr at a sensitivity limit of 10<sup>-11<sup/> . We compared the fraction of stars showing ongoing accretion () to the fraction of stars with near-to-mid infrared excess (). In most cases we find <i><<i/> , i.e. , mass accretion appears to cease (or drop below detectable level) earlier than the dust is dissipated in the inner disk. At 5 Myr, 95% of the stellar population has stopped accreting material at a rate of , while ~20% of the stars show near-infrared excess emission. Assuming an exponential decay, we measure a mass accretion timescale () of 2.3 Myr, compared to a near-to-mid infrared excess timescale () of 3 Myr. Planet formation and/or migration, in the inner disk might be a viable mechanism to halt further accretion onto the central star on such a short timescale.
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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 | 0.000 | 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) | 0.001 | 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".