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Record W4416227203 · doi:10.3847/1538-3881/ae112b

The Influence of Tight Binaries on Protoplanetary Disk Masses

2025· article· en· W4416227203 on OpenAlexaff
Kevin Flaherty, Peter Knowlton, Tasan Smith-Gandy, A. Meredith Hughes, Marina Kounkel, Eric L. N. Jensen, James Muzerolle Page, Kevin R. Covey

Bibliographic record

VenueThe Astronomical Journal · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Victoria
FundersJet Propulsion LaboratoryNational Astronomical Observatory of JapanNational Institutes of Natural SciencesEuropean Space AgencyCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNational Radio Astronomy ObservatoryUniversity of California, Los AngelesKorea Astronomy and Space Science InstituteNational Science Foundation
KeywordsCircumbinary planetBinary numberPlanetCircumstellar diskBinary starProtoplanetary diskStarsPlanetary system

Abstract

fetched live from OpenAlex

Abstract Binary systems are a common site of planet formation, despite the destructive effects of the binary on the disk. While surveys of planet-forming material have found diminished disk masses around binaries with medium separation (∼10–100 au), less is known about tight (<10 au) binaries, where a significant circumbinary disk may escape the disruptive dynamical effects of the binary. We survey over 100 spectroscopic binaries in the Orion A region with the Atacama Large Millimeter/submillimeter Array (ALMA), detecting significant continuum emission among 21 of them with disk masses ranging from 1 to 100 M ⊕ . We find evidence of systematically lower disk masses among the binary sample when compared to single-star surveys, which may reflect a diminished planet-forming potential around tight binaries. The infrared excess fraction among the binary sample is comparable to that of single stars, although the tight binaries without significant ALMA emission display tentative evidence of weaker 3–5 μ m excesses. The depletion of cold dust is difficult to explain by clearing alone, and the role of additional mechanisms needs to be explored. It may be the result of the formation pathway for these objects, systematic differences in intrinsic properties (e.g., opacity) or a bias in how the sample was constructed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.228
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2025
Admission routes1
Has abstractyes

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