Lucky Imaging survey for southern M dwarf binaries
Bibliographic record
Abstract
<i>Context. <i/>While M dwarfs are the most abundant stars in the Milky Way, there is still large uncertainty about their basic physical properties (mass, luminosity, radius, etc.) as well as their formation environment. Precise knowledge of multiplicity characteristics and how they change in this transitional mass region, between Sun-like stars on the one side and very low mass stars and brown dwarfs on the other, provide constraints on low mass star and brown dwarf formation.<i>Aims. <i/>In the largest M dwarf binary survey to date, we search for companions to active, and thus preferentially young, M dwarfs in the solar neighbourhood. We study their binary/multiple properties, such as the multiplicity frequency and distributions of mass-ratio and separation, and identify short period visual binaries, for which orbital parameters and hence dynamical mass estimates can be derived in the near future. <i>Methods. <i/>The observations are carried out in the SDSS <i>i<i/>' and <i>z<i/>' band using the Lucky Imaging camera AstraLux Sur at the ESO 3.5 m New Technology Telescope. Lucky Imaging is a very efficient way of observing a large sample of stars at an angular resolution close to the diffraction limit. <i>Results. <i/>In the first part of the survey, we observed 124 M dwarfs of integrated spectral types M 0–M 6 and identified 34 new and 17 previously known companions to 44 stars. We derived relative astrometry and component photometry for these binary and multiple systems. More than half of the binaries have separations smaller than 1” and would have been missed in a simply seeing-limited survey. Correcting our sample for selection effects yields a multiplicity fraction of 32<i>±<i/>6% for 108 M dwarfs within 52 pc and with angular separations of 0.1”–6.0”, corresponding to projected separations of 3–180 AU at median distance 30 pc. Compared to early-type M dwarfs (<i>M<i/> 0.3 ), later-type (and hence lower mass) M dwarf binaries appear to have closer separations, and more similar masses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".