Bibliographic record
Abstract
We present full-sky coadded maps created by uniformly combining the first five years of Wide-field Infrared Survey Explorer ( WISE ) and NEOWISE imaging at 3.4 μ m ( W 1) and 4.6 μ m ( W 2). By incorporating both pre-hibernation WISE exposures from 2010 to 2011 and the first four years (2013–2017) of post-hibernation exposures from the NEOWISE-Reactivation mission, we are able to provide W 1/ W 2 coadds that span a 15 × longer time baseline and are substantially deeper than the standard AllWISE data products. Our new five-year "full-depth" coadds are now the deepest ever all-sky maps at 3−5 μ m, permitting detection of sources ∼2 × (∼0.7 mag) fainter than AllWISE at 5 σ significance. We additionally present an updated set of "time-resolved" W 1/ W 2 coadds, which separately stack each of ∼10 sky passes at each inertial sky location, enabling motion and variability measurements for faint infrared sources over a long ∼7.5 yr time baseline. We highlight new processing improvements relative to our previous "unWISE" coadd releases, focusing on astrometric calibration and artifact flagging. The deep WISE stacks presented here are already being used to perform target selection for the Dark Energy Spectroscopic Instrument, and our full-sky coadded WISE /NEOWISE products will be key precursor data sets for upcoming wide-field infrared missions including SPHEREx and NEOCam.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".