Aurigaia: mock Gaia DR2 stellar catalogues from the auriga cosmological simulations
Bibliographic record
Abstract
We present and analyse mock stellar catalogues that match the selection criteria and observables (including uncertainties) of the Gaia satellite data release 2 (DR2). The source are six cosmological high-resolution magneto-hydrodynamic ΛCDM zoom simulations of the formation of Milky Way analogues from the auriga project. Mock data are provided for stars with V < 16 mag and V < 20 mag at |b| > 20 deg. The mock catalogues are made using two different methods: the public snapdragons code, and a method based on that of Lowing et al. (2015) that preserves the phase-space distribution of the model stars. These publicly available catalogues contain five-parameter astrometry, radial velocities, multiband photometry, stellar parameters, dust extinction values, and uncertainties in all these quantities. In addition, we provide the gravitational potential and information on the origin of each star. By way of demonstration, we apply the mock catalogues to analyses of the young stellar disc and the stellar halo. We show that (i) the young outer stellar disc exhibits a flared distribution that is detectable in the height and vertical velocity distribution of |$\rm A$|- and |$\rm B$|-dwarf stars up to radii of ∼15 kpc, and (ii) the spin of the stellar halo out to 100 kpc can be accurately measured with Gaia DR2 RR Lyrae stars. These catalogues are well suited for comparisons with observations and should help to (i) develop and test analysis methods for the Gaia DR2 data, (ii) gauge the limitations and biases of the data, and (iii) interpret the data in the light of theoretical predictions from realistic ab initio simulations of galaxy formation in the ΛCDM cosmological model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".