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Record W2973082411 · doi:10.1088/1538-3873/ab3df4

unWISE Coadds: The Five-year Data Set

2019· article· en· W2973082411 on OpenAlexaff

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsPerimeter Institute
FundersNational Aeronautics and Space Administration
KeywordsSkyData setCalibrationInfraredSet (abstract data type)Artifact (error)Energy (signal processing)

Abstract

fetched live from OpenAlex

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 microns (W1) and 4.6 microns (W2). By incorporating both pre-hibernation WISE exposures from 2010-2011 and the first four years (2013-2017) of post-hibernation exposures from the NEOWISE-Reactivation mission, we are able to provide W1/W2 coadds that span a 15 times 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 microns, permitting detection of sources ~2 times (~0.7 mag) fainter than AllWISE at 5 sigma significance. We additionally present an updated set of "time-resolved" W1/W2 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 year 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 (DESI), and our full-sky coadded WISE/NEOWISE products will be key precursor data sets for upcoming wide-field infrared missions including SPHEREx and NEOCam.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0040.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.273
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2019
Admission routes1
Has abstractyes

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