Mass-accretion, spectral, and photometric properties of T Tauri stars in Taurus based on TESS and LAMOST
Bibliographic record
Abstract
We present the analysis of 16 classical T Taur stars using LAMOST and TESS data, investigating spectral properties, photometric variations, and mass-accretion rates. All 16 stars exhibit emissions in H$α$ lines, from which the average mass-accretion rate of $1.76\times10^{-9}~M_{\odot}yr^{-1}$ is derived. Two of the stars, DL Tau and Haro 6-13, show mass-accretion bursts simultaneously in TESS, ASAS-SN, and/or ZTF survey. Based on these observations, we find that the mass-accretion rates of DL Tau and Haro 6-13 reach their maximums of $2.5 \times 10^{-8}~M_{\odot}yr^{-1}$ and $2 \times 10^{-10}~M_{\odot}yr^{-1}$ during the TESS observation, respectively. We detect thirteen flares among these stars. The flare frequency distribution shows that the CTTSs' flare activity is not only dominated by strong flares with high energy but much more active than those of solar-type and young low-mass stars. By comparing the variability classes reported in the literature, we find that the transition timescale between different classes of variability in CTTSs, such as from Stochastic (S) to Bursting (B) or from quasi-periodic symmetric (QPS) to quasi-periodic dipping (QPD), may range from 1.6 to 4 years. We observe no significant correlation between inclination and mass-accretion rates derived from the emission indicators. This suggests that inner disk properties may be more important than that of outer disk. Finally, we find a relatively significant positive correlation between the asymmetric metric "M" and the cold disk inclination compared to the literature. A weak negative correlation between the periodicity metric "Q" value and inclination has been also found.
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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 | 0.003 | 0.002 |
| 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) | 0.000 | 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".