Stable and unstable accretion in the classical T Tauri stars IM Lup and RU Lup as observed by<i>MOST</i>
Bibliographic record
Abstract
Results of the time variability monitoring of the two classical T Tauri stars, RU Lup and IM Lup, are presented. Three photometric data sets were utilized: (1) simultaneous (same field) MOST satellite observations over four weeks in each of the years 2012 and 2013, (2) multicolour observations at the South African Astronomical Observatory in April–May of 2013, (3) archival V-filter All Sky Automated Survey (ASAS) data for nine seasons, 2001–2009. They were augmented by an analysis of high-resolution, public-domain VLT-UT2 Ultraviolet Visual Echelle Spectrograph spectra from the years 2000 to 2012. From the MOST observations, we infer that irregular light variations of RU Lup are caused by stochastic variability of hotspots induced by unstable accretion. In contrast, the MOST light curves of IM Lup are fairly regular and modulated with a period of about 7.19–7.58 d, which is in accord with ASAS observations showing a well-defined 7.247 ± 0.026 d periodicity. We propose that this is the rotational period of IM Lup and is due to the changing visibility of two antipodal hotspots created near the stellar magnetic poles during the stable process of accretion. Re-analysis of RU Lup high-resolution spectra with the broadening function approach reveals signs of a large polar coldspot, which is fairly stable over 13 years. As the star rotates, the spot-induced depression of intensity in the broadening function profiles changes cyclically with period 3.710 58 d, which was previously found by the spectral cross-correlation method.
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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 | 0.000 |
| 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".