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Record W4396496059 · doi:10.3847/1538-3881/ad3737

Modeling Circumstellar Gas Emission around a White Dwarf Using cloudy

2024· article· en· W4396496059 on OpenAlexfundno aff
Siyi Xu, Sherry Yeh, Laura K. Rogers, Amy Steele, Erik Dennihy, Alexandra E. Doyle, P. Dufour, B. Klein, Christopher J. Manser, Carl Melis, Tinggui Wang, Alycia J. Weinberger

Bibliographic record

VenueThe Astronomical Journal · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space AdministrationSpace Telescope Science InstituteNational Science Foundation
KeywordsWhite dwarfAstrophysicsWhite (mutation)AstronomyPhysicsCircumstellar diskAstrobiologyStarsChemistry

Abstract

fetched live from OpenAlex

Abstract The chemical composition of an extrasolar planet is fundamental to its formation, evolution, and habitability. In this study, we explore a new way to measure the chemical composition of the building blocks of extrasolar planets by measuring the gas composition of the disrupted planetesimals around white dwarf stars. As a first attempt, we used the photoionization code Cloudy to model the circumstellar gas emission around white dwarf Gaia J0611−6931 under some simplified assumptions. We found that most of the emission lines are saturated, and the line ratios approach the ratios of thermal emission; therefore, only lower limits to the number density can be derived. Silicon is the best-constrained element in the circumstellar gas, and we derived a lower limit of 1010.3 cm−3. In addition, we placed a lower limit on the total amount of gas to be 1.8 × 1019 g. Further study is needed to better constrain the parameters of the gas disk and connect it to other white dwarfs with circumstellar gas absorption.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.257
Teacher spread0.232 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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