Preliminary Target Selection for the DESI Bright Galaxy Survey (BGS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Dark Energy Spectroscopic Instrument (DESI) will execute a nearly magnitude-limited survey of low redshift galaxies (0.05 ≤ z ≤ 0.4, median z ≈ 0.2). Clustering analyses of this Bright Galaxy Survey (BGS) will yield the most precise measurements to date of baryon acoustic oscillations and redshift-space distortions at low redshift. DESI BGS will comprise two target classes: (i) BRIGHT ( r < 19.5 mag), and (ii) FAINT (19.5 < r < 20 mag). Here we present a summary of the star-galaxy separation, and different photometric and geometrical masks, used in BGS to reduce the number of spurious targets. The selection results in a total density of ∼800 objects deg −2 for the BRIGHT and ∼600 objects deg −2 for the FAINT selections. A full characterization of the BGS selection can be found in Ruiz-Macias et al.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it