Binary White Dwarfs in the Galactic Halo
Bibliographic record
Abstract
We use the stellar population synthesis code SeBa (Portegies Zwart & Verbunt (1996), Toonen, Nelemans & Portegies Zwart (2012)) to study the halo white dwarf population. Here we assume a Kroupa initial mass function and compare 4 models, varying two parameters: the star formation (SF) history of the halo (either continuous SF during 2.5 Gyr, which started 13.2 Gyr ago, or a SF burst during 360 Myr, which started 12.9 Gyr ago – see the left panel of the figure) and the binary fraction of the halo (either 100% single stars, or 100% binaries). White dwarf cooling models (Althaus et al. (2009) and Renedo et al. (2010)) allow us to plot the halo white dwarf luminosity function for these 4 models, as is done in the right panel of the figure. Combined with an assumption about the density distribution of halo stars, we will study which of these white dwarfs Gaia can see, and what that can tell us about the initial parameter distributions in the halo. In the near future, we plan to use the Munich-Groningen semi-analytical galaxy formation model (Starkenburg et al. (2013)), to obtain key ingredients for the population synthesis modeling, such as a realistic star formation history (see the left panel of the figure).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".