Bibliographic record
Abstract
The absolute number and the density profiles of different types of stars in the solar neighborhood are a fundamental anchor for studies of the initial mass function, stellar evolution, and galactic structure. Using data from the Gaia DR1 Tycho-Gaia Astrometric Solution, we reconstruct Gaia's selection function and we determine Gaia's volume completeness, the local number density, and the vertical profiles of different spectral types along the main sequence from early A stars to late K stars as well as along the giant branch. We clearly detect the expected flattening of the stellar density profile near the mid-plane for all stellar types: All vertical profiles are well represented by sech^2 profiles, with scale heights ranging from ~50 pc for A stars to ~150 pc for G and K dwarfs and giants. We determine the luminosity function along the main sequence for M_V < 7 (M >~ $0.72 M_\odot$) and along the giant branch for M_J >~ -2.5. Converting this to a mass function, we find that the high-mass (M > $1\,M_\odot$) present-day mass function along the main sequence is d n / d M = 0.016 $(M/M_\odot)^{-4.7}$ stars/pc^3/$M_\odot$. Extrapolating below M = $0.72\,M_\odot$, we find a total mid-plane stellar density of 0.040+/-0.002 $M_\odot$/pc^3. Giants contribute 0.00039+/-0.00001 stars/pc^3 or about 0.00046+/-0.00005 $M_\odot$/pc^3. The star-formation rate surface density is \Sigma(t) = 7+/-1 exp(-t/[7+/-1 Gyr]) $M_\odot$/pc^2/Gyr. Overall, we find that Gaia DR1's selection biases are manageable and allow a detailed new inventory of the solar neighborhood to be made that agrees with and extends previous studies. This bodes well for mapping the Milky Way with the full Gaia data set.
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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 | 0.002 | 0.002 |
| 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 | 0.003 |
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".