The Tarantula Massive Binary Monitoring: I. Observational campaign and OB-type spectroscopic binaries
Bibliographic record
Abstract
Massive binaries (MBs) play a crucial role in the Universe. Knowing the distributions of their orbital parameters (OPs) is important for a wide range of topics, from stellar feedback to binary evolution channels, from the distribution of supernova types to gravitational wave progenitors, yet, no direct measurements exist outside the Milky Way. The Tarantula Massive Binary Monitoring was designed to help fill this gap by obtaining multi-epoch radial velocity monitoring of 102 MBs in the 30 Dor. In this paper, we analyse 32 VLT/FLAMES observations of 93 O- and 7 B-type binaries. We performed a Fourier analysis and obtained orbital solutions for 82 systems: 51 single- and 31 double-lined spectroscopic binaries. Overall, the OPs and binary fraction are remarkably similar across the 30 Dor region and compared to existing Galactic samples (GSs). This indicates that within these domains environmental effects are of second order in shaping the properties of MBs. A small difference is found in the distribution of orbital periods (OrbPs), which is slightly flatter (in log space) in 30 Dor than in the Galaxy, although this may be compatible within error estimates and differences in the fitting methodology. Also, OrbPs in 30 Dor can be as short as 1.1 d; somewhat shorter than seen in GSs. Equal mass binaries q>0.95 in 30 Dor are all found outside NGC 2070 the very young and massive cluster at 30 Dor's core. One outstanding exception however is the fact that earliest spectral types (O2-O7) tend to have shorter OrbPs than latter (O9.2-O9.7). Our results point to a relative universality of the incidence rate of MBs and their OPs in the metallicity range from solar ($Z_{\odot}$) to about $0.5Z_{\odot}$. This provides the first direct constraints on MB properties in massive star-forming galaxies at the Universes peak of star formation at redshifts z~1 to 2, which are estimated to have $Z~0.5Z_{\odot}$.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".