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Record W1996158105 · doi:10.1088/0067-0049/212/1/10

A SENSITIVE IDENTIFICATION OF WARM DEBRIS DISKS IN THE SOLAR NEIGHBORHOOD THROUGH PRECISE CALIBRATION OF SATURATED <i>WISE</i> PHOTOMETRY

2014· article· en· W1996158105 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsStarsPhotometry (optics)DebrisDebris diskPopulationCalibration

Abstract

fetched live from OpenAlex

We present a sensitive search for WISE W 3 (12 μm) and W 4 (22 μm) excesses from warm optically thin dust around Hipparcos main sequence stars within 75 pc from the Sun. We use contemporaneously measured photometry from WISE , remove sources of contamination, and derive and apply corrections to saturated fluxes to attain optimal sensitivity to >10 μm excesses. We use data from the WISE All-Sky Survey Catalog rather than the AllWISE release because we find that its saturated photometry is better behaved, allowing us to detect small excesses even around saturated stars in WISE . Our new discoveries increase by 45% the number of stars with warm dusty excesses and expand the number of known debris disks (with excess at any wavelength) within 75 pc by 29%. We identify 220 Hipparcos debris disk host stars, 108 of which are new detections at any wavelength. We present the first measurement of a 12 μm and/or 22 μm excess for 10 stars with previously known cold (50–100 K) disks. We also find five new stars with small but significant W 3 excesses, adding to the small population of known exozodi, and we detect evidence for a W 2 excess around HIP 96562 (F2V), indicative of tenuous hot (780 K) dust. As a result of our WISE study, the number of debris disks with known 10–30 μm excesses within 75 pc (379) has now surpassed the number of disks with known >30 μm excesses (289, with 171 in common), even if the latter have been found to have a higher occurrence rate in unbiased samples.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.248
Teacher spread0.238 · 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 designBench or experimental
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

Citations78
Published2014
Admission routes1
Has abstractyes

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