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Record W4220972280 · doi:10.1051/0004-6361/202143022

Radial velocity variability and the evolution of hot subdwarf stars

2022· article· en· W4220972280 on OpenAlexfundno aff
S. Geier, M. Dorsch, Ingrid Pelisoli, Nicole Reindl, U. Heber, A. Irrgang

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryBrookhaven National LaboratoryNational Astronomical Observatories, Chinese Academy of SciencesOffice of ScienceMax-Planck-Institut für AstronomieMax-Planck-GesellschaftChinese Academy of SciencesDeutsche ForschungsgemeinschaftYork UniversityFermilabNational Science FoundationCase Western Reserve UniversityTexas Tech UniversityCarnegie Mellon UniversityUniversity of PittsburghUniversity of ArizonaLos Alamos National LaboratoryCollege of Engineering, Michigan State UniversityPrinceton UniversityAlfred P. Sloan FoundationUniversity of WashingtonEuropean Space AgencyJohns Hopkins UniversityNational Development and Reform CommissionDeutscher Akademischer AustauschdienstHarvard UniversityOhio State UniversityNew Mexico State UniversityUniversity of PortsmouthYale UniversityVanderbilt UniversityDrexel UniversityNational Aeronautics and Space AdministrationScience and Technology Facilities CouncilUniversität BaselU.S. Naval ObservatoryU.S. Department of Energy
KeywordsSubdwarfAstrophysicsPhysicsStarsRadial velocitySkyPopulationHorizontal branchBinary numberAstronomyMetallicityWhite dwarfDemography

Abstract

fetched live from OpenAlex

Hot subdwarf stars represent a late and peculiar stage in the evolution of low-mass stars, since they are likely formed by close binary interactions. In this work, we perform a radial velocity (RV) variability study of a sample of 646 hot subdwarfs with multi-epoch radial velocities based on spectra from Sloan Digital Sky Survey (SDSS) and Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST). The atmospheric parameters and RVs were taken from the literature. For stars with archival spectra but without literature values, we determined the parameters by fitting model atmospheres. In addition, we redetermined the atmospheric parameters and RVs for all the He-enriched sdO/Bs. This broad sample allowed us to study RV-variability as a function of the location in the Teff − log g- and Teff − log n(He)/n(H) diagrams in a statistically significant way. We used the fraction of RV-variable stars and the distribution of the maximum RV variations ΔRVmax as diagnostics. Both indicators turned out to be quite inhomogeneous across the studied parameter ranges. A striking feature is the completely dissimilar behaviour of He-poor and He-rich hot subdwarfs. While the former have a high fraction of close binaries, almost no significant RV variations could be detected for the latter. This has led us to the conclusion that there is likely no evolutionary connection between these subtypes. On the other hand, intermediate He-rich- and extreme He-rich sdOB/Os are more likely to be related. Furthermore, we conclude that the vast majority of this population is formed via one or several binary merger channels. Hot subdwarfs with temperatures cooler than ∼24 000 K tend to show fewer and smaller RV-variations. These objects might constitute a new subpopulation of binaries with longer periods and late-type or compact companions. The RV-variability properties of the extreme horizontal branch (EHB) and corresponding post-EHB populations of the He-poor hot subdwarfs match and confirm the predicted evolutionary connection between them. Stars found below the canonical EHB at somewhat higher surface gravities show large RV variations and a high RV variability fraction. These properties are consistent with most of them being low-mass EHB stars or progenitors of low-mass helium white dwarfs in close binaries.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.185
Teacher spread0.180 · 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 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

Citations33
Published2022
Admission routes1
Has abstractyes

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