Binarity at LOw Metallicity (BLOeM)
Bibliographic record
Abstract
Surveys in the Milky Way and Large Magellanic Cloud have revealed that the majority of massive stars will interact with companions during their lives. However, knowledge of the binary properties of massive stars at low metallicity, and therefore in conditions approaching those of the Early Universe, remain sparse. We present the Binarity at LOw Metallicity (BLOeM) campaign, an ESO large programme designed to obtain 25 epochs of spectroscopy for 929 massive stars in the Small Magellanic Cloud, allowing us to probe multiplicity in the lowest-metallicity conditions to date ( Z = 0.2 Z ⊙ ). BLOeM will provide (i) the binary fraction, (ii) the orbital configurations of systems with periods of P ≲ 3 yr, (iii) dormant black-hole binary candidates (OB+BH), and (iv) a legacy database of physical parameters of massive stars at low metallicity. Main sequence (OB-type) and evolved (OBAF-type) massive stars are observed with the LR02 setup of the GIRAFFE instrument of the Very Large Telescope (3960–4570 Å resolving power R = 6200; typical signal-to-noise ratio(S/N) ≈70–100). This paper utilises the first nine epochs obtained over a three-month time period. We describe the survey and data reduction, perform a spectral classification of the stacked spectra, and construct a Hertzsprung-Russell diagram of the sample via spectral-type and photometric calibrations. Our detailed classification reveals that the sample covers spectral types from O4 to F5, spanning the effective temperature and luminosity ranges 6.5 ≲ T eff /kK ≲ 45 and 3.7 < log L/L ⊙ < 6.1 and initial masses of 8 ≲ M ini ≲ 80 M ⊙ . The sample comprises 159 O-type stars, 331 early B-type (B0–3) dwarfs and giants (luminosity classes V–III), 303 early B-type supergiants (II–I), and 136 late-type BAF supergiants. At least 82 stars are OBe stars: 20 O-type and 62 B-type (13% and 11% of the respective samples). In addition, the sample includes 4 high-mass X-ray binaries, 3 stars resembling luminous blue variables, 2 bloated stripped-star candidates, 2 candidate magnetic stars, and 74 eclipsing binaries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".