SEARCH FOR HIGH PROPER MOTION OBJECTS IN THE CFHTLS DEEP FIELDS
Bibliographic record
Abstract
We present the results of a search for high proper motion white dwarfs in the deep survey of the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS). The CFHTLS Deep Survey covers 4 deg 2 in five filters ( u *, g ', r ', i ', and z '). For the first and the fourth fields, we use data for a 5 year baseline from 2004 to 2009. For the second and the third fields, we have a 4 year baseline from 2004 to 2008. Proper motion selection is used to distinguish cool high-velocity white dwarfs from distant objects with similar blue colors such as compact faint galaxies and quasars. We discovered 44 white dwarf candidates brighter than g ' = 24 on the basis of their spectral energy distribution and reduced proper motions. We found one white dwarf candidate with effective temperature less than 4000 K. From its estimated tangential velocity of 31 km s −1 and a distance of 124 pc, it appears to be located in the thin or thick disk of the Galaxy. We also find five candidates having T eff between 4000 K and 5000 K. One candidate in D2 with effective temperature of 5000 K and tangential velocity of 190 km s −1 indicates that it could be in the thick disk or in the halo. The other four candidates are likely located in the thin disk because of their estimated distances and tangential velocity.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".