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Record W4398177271 · doi:10.3847/1538-4357/ad354b

Spectroscopic Constraints on the Mid-infrared Attenuation Curve. I. Attenuation Model Using Polycyclic Aromatic Hydrocarbon Emission

2024· article· en· W4398177271 on OpenAlexaff
Thomas S. -Y. Lai, J. D. Smith, E. Peeters, H. W. W. Spoon, Shunsuke Baba, Masatoshi Imanishi, Takao Nakagawa

Bibliographic record

VenueThe Astrophysical Journal · 2024
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsAttenuationPolycyclic aromatic hydrocarbonHydrocarbonInfraredAromatic hydrocarbonAnalytical Chemistry (journal)Materials scienceChemistryEnvironmental chemistryPhysicsOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract We introduce a novel model to spectroscopically constrain the mid-infrared (MIR) extinction/attenuation curve from 3–17 μ m, using polycyclic aromatic hydrocarbon (PAH) emission drawn from an AKARI–Spitzer extragalactic cross-archival data set. Currently proposed MIR extinction curves vary significantly in their slopes toward the near-infrared, and the variation in the strengths and shapes of the 9.7 μ m and 18 μ m silicate absorption features make MIR spectral modeling and interpretation challenging, particularly for heavily obscured galaxies. By adopting the basic premise that PAH bands have relatively consistent intrinsic ratios within dusty starbursting galaxies, we can, for the first time, empirically determine the overall shape of the MIR attenuation curve by measuring the differential attenuation at specific PAH wavelengths. Our attenuation model shows PAH emission in most (ultra)luminous infrared galaxies is unambiguously subjected to attenuation, and we find strong evidence that PAH bands undergo differential attenuation as obscuration increases. Compared to preexisting results, the MIR attenuation curve derived from the model favors relatively gray continuum absorption from 3–8 μ m and silicate features with intermediate strength at 9.7 μ m but with stronger than typical 18 μ m opacity.

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 score0.752
Threshold uncertainty score0.567

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.283
Teacher spread0.260 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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