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Record W3090370529 · doi:10.1051/0004-6361/201936534

Physical properties of the ambient medium and of dense cores in the Perseus star-forming region derived from <i>Herschel</i> Gould Belt Survey observations

2020· article· en· W3090370529 on OpenAlexaff

Bibliographic record

VenueAstronomy and Astrophysics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsMcMaster UniversityQueen's University
FundersNational Astronomical Observatories, Chinese Academy of SciencesScience and Technology Facilities CouncilMax-Planck-Institut für AstronomieMinistério da Ciência, Tecnologia e Ensino SuperiorJet Propulsion LaboratoryCentre National d’Etudes SpatialesBundesministerium für Verkehr, Innovation und TechnologieCentre National de la Recherche ScientifiqueFundação para a Ciência e a TecnologiaKU LeuvenAgence Nationale de la RechercheNational Aeronautics and Space AdministrationImperial College LondonCalifornia Institute of TechnologyDeutsche ForschungsgemeinschaftCardiff University
KeywordsStarsBlack-body radiationPower lawStar formationMeasure (data warehouse)Spectral energy distributionInitial mass functionO-type starRight ascension

Abstract

fetched live from OpenAlex

The complex of star-forming regions in Perseus is one of the most studied due to its proximity (about 300 pc). In addition, its regions show variation in star-formation activity and age, with formation of low-mass and intermediate-mass stars. In this paper, we present analyses of images taken with the Herschel ESA satellite from 70 μ m to 500 μ m. From these images, we first constructed column density and dust temperature maps. We then identified compact cores in the maps at each wavelength, and characterised the cores using modified blackbody fits to their spectral energy distributions (SEDs): we identified 684 starless cores, of which 199 are bound and potential prestellar cores, and 132 protostars. We also matched the Herschel -identified young stars with Gaia sources to model distance variations across the Perseus cloud. We measure a linear gradient function with right ascension and declination for the entire cloud. This function is the first quantitative attempt to derive the gradient in distance across Perseus, from east to west, in an analytical form. We derived mass and temperature of cores from the SED fits. The core mass function can be modelled with a log-normal distribution that peaks at 0.82 M ⊙ suggesting a star formation efficiency of 0.30 for a peak in the system initial mass function of stars at 0.25 M ⊙ . The high-mass tail can be modelled with a power law of slope ~−2.32, which is close to the Salpeter’s value. We also identify the filamentary structure of Perseus and discuss the relation between filaments and star formation, confirming that stars form preferentially in filaments. We find that the majority of filaments with ongoing star formation are transcritical against their own internal gravity because their linear masses are below the critical limit of 16 M ⊙ pc −1 above which we expect filaments to collapse. We find a possible explanation for this result, showing that a filament with a linear mass as low as 8 M ⊙ pc −1 can already be unstable. We confirm a linear relationship between star formation efficiency and the slope of dust probability density function, and we find a similar relationship with the core formation efficiency. We derive a lifetime for the prestellar core phase of 1.69 ± 0.52 Myr for the whole Perseus complex but different regions have a wide range in prestellar core fractions, suggesting that star formation began only recently in some clumps. We also derive a free-fall time for prestellar cores of 0.16 Myr.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.045
GPT teacher head0.222
Teacher spread0.177 · 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

Citations47
Published2020
Admission routes1
Has abstractyes

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