Determining the parameters of massive protostellar clouds via radiative transfer modeling View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-01-18

AUTHORS

Ya. N. Pavlyuchenkov, D. S. Wiebe, A. M. Fateeva, T. S. Vasyunina

ABSTRACT

A one-dimensional method for reconstructing the structure of prestellar and protostellar clouds is presented. The method is based on radiative-transfer computations and a comparison of theoretical and observed intensity distributions at both millimeter and infrared wavelengths. The radiative transfer of dust emission is modeled for specified parameters of the density distribution, central star, and external background, and the theoretical distribution of the dust temperature inside the cloud is determined. The intensity distributions at millimeter and IR wavelengths are computed and quantitatively compared with observational data. The best-fit model parameters are determined using a genetic minimization algorithm, which makes it possible to reveal the ranges of parameter degeneracy as well. The method is illustrated by modeling the structure of two infrared dark clouds IRDC-320.27+029 (P2) and IRDC-321.73+005 (P2). The derived density and temperature distributions can be used to model the chemical structure and spectral maps in molecular lines. More... »

PAGES

1-12

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s1063772911010057

DOI

http://dx.doi.org/10.1134/s1063772911010057

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038285853


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0201", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Astronomical and Space Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia", 
          "id": "http://www.grid.ac/institutes/grid.465335.2", 
          "name": [
            "Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pavlyuchenkov", 
        "givenName": "Ya. N.", 
        "id": "sg:person.012666170751.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012666170751.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia", 
          "id": "http://www.grid.ac/institutes/grid.465335.2", 
          "name": [
            "Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wiebe", 
        "givenName": "D. S.", 
        "id": "sg:person.015110315535.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015110315535.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia", 
          "id": "http://www.grid.ac/institutes/grid.465335.2", 
          "name": [
            "Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fateeva", 
        "givenName": "A. M.", 
        "id": "sg:person.015273134113.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015273134113.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "International Max-Planck Research School, Heidelberg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.4372.2", 
          "name": [
            "Max-Planck Institute for Astronomy, D-69117, Heidelberg, Germany", 
            "International Max-Planck Research School, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Vasyunina", 
        "givenName": "T. S.", 
        "id": "sg:person.013020042171.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013020042171.90"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/35051509", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043337890", 
          "https://doi.org/10.1038/35051509"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011-01-18", 
    "datePublishedReg": "2011-01-18", 
    "description": "A one-dimensional method for reconstructing the structure of prestellar and protostellar clouds is presented. The method is based on radiative-transfer computations and a comparison of theoretical and observed intensity distributions at both millimeter and infrared wavelengths. The radiative transfer of dust emission is modeled for specified parameters of the density distribution, central star, and external background, and the theoretical distribution of the dust temperature inside the cloud is determined. The intensity distributions at millimeter and IR wavelengths are computed and quantitatively compared with observational data. The best-fit model parameters are determined using a genetic minimization algorithm, which makes it possible to reveal the ranges of parameter degeneracy as well. The method is illustrated by modeling the structure of two infrared dark clouds IRDC-320.27+029 (P2) and IRDC-321.73+005 (P2). The derived density and temperature distributions can be used to model the chemical structure and spectral maps in molecular lines.", 
    "genre": "article", 
    "id": "sg:pub.10.1134/s1063772911010057", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1136138", 
        "issn": [
          "0004-6299", 
          "1063-7729"
        ], 
        "name": "Astronomy Reports", 
        "publisher": "Pleiades Publishing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "55"
      }
    ], 
    "keywords": [
      "protostellar clouds", 
      "fit model parameters", 
      "one-dimensional method", 
      "intensity distribution", 
      "parameter degeneracies", 
      "theoretical distributions", 
      "radiative transfer computations", 
      "radiative transfer", 
      "dust temperature", 
      "minimization algorithm", 
      "observed intensity distribution", 
      "model parameters", 
      "genetic minimization algorithm", 
      "observational data", 
      "IR wavelengths", 
      "external background", 
      "radiative transfer modeling", 
      "density distribution", 
      "central star", 
      "specified parameters", 
      "temperature distribution", 
      "dust emission", 
      "transfer modeling", 
      "parameters", 
      "molecular lines", 
      "spectral maps", 
      "degeneracy", 
      "infrared wavelengths", 
      "distribution", 
      "computation", 
      "stars", 
      "algorithm", 
      "modeling", 
      "structure", 
      "wavelength", 
      "cloud", 
      "millimeters", 
      "density", 
      "maps", 
      "temperature", 
      "comparison", 
      "emission", 
      "range", 
      "lines", 
      "transfer", 
      "data", 
      "background", 
      "chemical structure", 
      "method"
    ], 
    "name": "Determining the parameters of massive protostellar clouds via radiative transfer modeling", 
    "pagination": "1-12", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1038285853"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1134/s1063772911010057"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1134/s1063772911010057", 
      "https://app.dimensions.ai/details/publication/pub.1038285853"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:27", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_551.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1134/s1063772911010057"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1134/s1063772911010057'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1134/s1063772911010057'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1134/s1063772911010057'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1134/s1063772911010057'


 

This table displays all metadata directly associated to this object as RDF triples.

136 TRIPLES      22 PREDICATES      75 URIs      66 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1134/s1063772911010057 schema:about anzsrc-for:02
2 anzsrc-for:0201
3 schema:author N69861ac7141642ee878cfcf2c1ad7cef
4 schema:citation sg:pub.10.1038/35051509
5 schema:datePublished 2011-01-18
6 schema:datePublishedReg 2011-01-18
7 schema:description A one-dimensional method for reconstructing the structure of prestellar and protostellar clouds is presented. The method is based on radiative-transfer computations and a comparison of theoretical and observed intensity distributions at both millimeter and infrared wavelengths. The radiative transfer of dust emission is modeled for specified parameters of the density distribution, central star, and external background, and the theoretical distribution of the dust temperature inside the cloud is determined. The intensity distributions at millimeter and IR wavelengths are computed and quantitatively compared with observational data. The best-fit model parameters are determined using a genetic minimization algorithm, which makes it possible to reveal the ranges of parameter degeneracy as well. The method is illustrated by modeling the structure of two infrared dark clouds IRDC-320.27+029 (P2) and IRDC-321.73+005 (P2). The derived density and temperature distributions can be used to model the chemical structure and spectral maps in molecular lines.
8 schema:genre article
9 schema:inLanguage en
10 schema:isAccessibleForFree true
11 schema:isPartOf N0d0bbbc7ae2b4b88bec66dbc6d1dffab
12 N80a5d741aca4419e8b574778d4a80fa0
13 sg:journal.1136138
14 schema:keywords IR wavelengths
15 algorithm
16 background
17 central star
18 chemical structure
19 cloud
20 comparison
21 computation
22 data
23 degeneracy
24 density
25 density distribution
26 distribution
27 dust emission
28 dust temperature
29 emission
30 external background
31 fit model parameters
32 genetic minimization algorithm
33 infrared wavelengths
34 intensity distribution
35 lines
36 maps
37 method
38 millimeters
39 minimization algorithm
40 model parameters
41 modeling
42 molecular lines
43 observational data
44 observed intensity distribution
45 one-dimensional method
46 parameter degeneracies
47 parameters
48 protostellar clouds
49 radiative transfer
50 radiative transfer computations
51 radiative transfer modeling
52 range
53 specified parameters
54 spectral maps
55 stars
56 structure
57 temperature
58 temperature distribution
59 theoretical distributions
60 transfer
61 transfer modeling
62 wavelength
63 schema:name Determining the parameters of massive protostellar clouds via radiative transfer modeling
64 schema:pagination 1-12
65 schema:productId Nbc7095d3c6d84655b2a620cdf0ed69b8
66 Ndb2f9b40dfb744919bb8c53d2928066f
67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038285853
68 https://doi.org/10.1134/s1063772911010057
69 schema:sdDatePublished 2022-05-20T07:27
70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
71 schema:sdPublisher Ne50bac22f2b542d79d9db8ab8f7f0f70
72 schema:url https://doi.org/10.1134/s1063772911010057
73 sgo:license sg:explorer/license/
74 sgo:sdDataset articles
75 rdf:type schema:ScholarlyArticle
76 N0d0bbbc7ae2b4b88bec66dbc6d1dffab schema:issueNumber 1
77 rdf:type schema:PublicationIssue
78 N15be4af1e8764205934fdac8a148da9e rdf:first sg:person.015110315535.16
79 rdf:rest Ne6a864bb8f50410ab19d587465bae953
80 N381c1657b8364bd2851621b8ababf8e9 rdf:first sg:person.013020042171.90
81 rdf:rest rdf:nil
82 N69861ac7141642ee878cfcf2c1ad7cef rdf:first sg:person.012666170751.33
83 rdf:rest N15be4af1e8764205934fdac8a148da9e
84 N80a5d741aca4419e8b574778d4a80fa0 schema:volumeNumber 55
85 rdf:type schema:PublicationVolume
86 Nbc7095d3c6d84655b2a620cdf0ed69b8 schema:name dimensions_id
87 schema:value pub.1038285853
88 rdf:type schema:PropertyValue
89 Ndb2f9b40dfb744919bb8c53d2928066f schema:name doi
90 schema:value 10.1134/s1063772911010057
91 rdf:type schema:PropertyValue
92 Ne50bac22f2b542d79d9db8ab8f7f0f70 schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 Ne6a864bb8f50410ab19d587465bae953 rdf:first sg:person.015273134113.16
95 rdf:rest N381c1657b8364bd2851621b8ababf8e9
96 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
97 schema:name Physical Sciences
98 rdf:type schema:DefinedTerm
99 anzsrc-for:0201 schema:inDefinedTermSet anzsrc-for:
100 schema:name Astronomical and Space Sciences
101 rdf:type schema:DefinedTerm
102 sg:journal.1136138 schema:issn 0004-6299
103 1063-7729
104 schema:name Astronomy Reports
105 schema:publisher Pleiades Publishing
106 rdf:type schema:Periodical
107 sg:person.012666170751.33 schema:affiliation grid-institutes:grid.465335.2
108 schema:familyName Pavlyuchenkov
109 schema:givenName Ya. N.
110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012666170751.33
111 rdf:type schema:Person
112 sg:person.013020042171.90 schema:affiliation grid-institutes:grid.4372.2
113 schema:familyName Vasyunina
114 schema:givenName T. S.
115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013020042171.90
116 rdf:type schema:Person
117 sg:person.015110315535.16 schema:affiliation grid-institutes:grid.465335.2
118 schema:familyName Wiebe
119 schema:givenName D. S.
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015110315535.16
121 rdf:type schema:Person
122 sg:person.015273134113.16 schema:affiliation grid-institutes:grid.465335.2
123 schema:familyName Fateeva
124 schema:givenName A. M.
125 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015273134113.16
126 rdf:type schema:Person
127 sg:pub.10.1038/35051509 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043337890
128 https://doi.org/10.1038/35051509
129 rdf:type schema:CreativeWork
130 grid-institutes:grid.4372.2 schema:alternateName International Max-Planck Research School, Heidelberg, Germany
131 schema:name International Max-Planck Research School, Heidelberg, Germany
132 Max-Planck Institute for Astronomy, D-69117, Heidelberg, Germany
133 rdf:type schema:Organization
134 grid-institutes:grid.465335.2 schema:alternateName Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia
135 schema:name Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia
136 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...