Near-infrared Variability of Low-mass Stars in IC 1396A and Tr 37
Bibliographic record
Abstract
Abstract We have monitored nearly a square degree in IC 1396A/Tr 37 over 21 epochs extending over 2014–2016 for sources variable in the JHK bands. In our data, 65% ± 8% of previously identified cluster members show variations, compared with ≲0.3% of field stars. We identify 119 members of Tr 37 on the basis of variability, forming an unbiased sample down to the brown dwarf regime. The K-band luminosity function in Tr 37 is similar to that of IC 348 but shifted to somewhat brighter values, implying that the K- and M-type members of Tr 37 are younger than those in IC 348. We introduce methods to classify the causes of variability, based on behavior in the color–color and color–magnitude diagrams. Accretion hot spots cause larger variations at J than at K with substantial scatter in the diagrams; there are at least a dozen, with the most active resembling EXors. Eleven sources are probably dominated by intervention of dust clumps in their circumstellar disks, with color behavior indicating the presence of grains larger than for interstellar dust, presumably due to grain growth in their disks. Thirteen sources have larger variations at K than at J or H. For 11 of them, the temperature fitted to the variable component is very close to 2000 K, suggesting that the changes in output are caused by turbulence at the inner rim of the circumstellar disk exposing previously protected populations of grains.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".