Spectral energy distribution modelling of southern candidate massive protostars using the Bayesian inference method
Identifieur interne : 000508 ( Istex/Corpus ); précédent : 000507; suivant : 000509Spectral energy distribution modelling of southern candidate massive protostars using the Bayesian inference method
Auteurs : T. Hill ; C. Pinte ; V. Minier ; M. G. Burton ; M. R. CunninghamSource :
- Monthly Notices of the Royal Astronomical Society [ 0035-8711 ] ; 2009-01-11.
English descriptors
- KwdEn :
- Angular resolution, Bayesian, Bayesian inference, Bayesian inference method, Bayesian probability, Best value, Bolometric luminosity, Bottom panels, Bottom right, Cent bayesian probability, Cent contour, Cold component, Continuum, Contour level, Contour levels, Cumulative distribution, Cumulative distribution plot, Cumulative distribution plots, Cumulative distributions, Cumulative fraction, Cumulative plot, Cumulative plots, Decimal places, Different classes, Different types, Direct association, Distance ambiguity, Distance value, Distinct classes, Earliest stages, Entire sample, Equatorial coordinates, Evolutionary status, Free parameters, Global temperature, Grid, Ident tracerc, Individual sources, Infrared data, Infrared regime, Integration range, Ira, Iras association, Iras data, Iras images, Journal compilation, Large number, Likely combination, Little difference, Luminosity, Luminosity estimates, Luminosity lmin lmax, Main parameters, Marginal distinctions, Maser, Maser mass, Maser radio, Maser sample, Mass diagram, Mass mmin, Mass parameter, Mass values, Massive star formation, Massive stars, Median, Median value, Median values, Methanol, Methanol maser, Methanol maser emission, Methanol maser radio continuum source, Methanol maser radio continuum sources, Methanol maser sites, Methanol maser sources, Methanol masers, Midcourse space experiment, Millimetre, Millimetre continuum emission, Millimetre sources, Millimetres continuum sources, Minier, Mnras, Modelling, Multiple components, Null hypothesis, Observational data, Parameter, Parameter space, Peak position, Probability curves, Probability plot, Protostars, Protostellar envelope mass, Radiative transfer modelling, Radiative transfer models, Radio continuum association, Radio continuum emission, Radio continuum source, Radio continuum sources, Resultant luminosity, Resultant parameters, Same parent distribution, Same population, Section table, Seds, Simba, Simba source, Simba source list, Simba sources, Simba survey, Simple model, Small distinction, Source class, Source names, Source size, Spectral energy distribution, Spitzer data, Star formation, Star formation activity, Star formation activity sample, Statistical studies, Submillimetre, Suitable values, Synthetic seds, Systematic sampling, Tcold, Temperature distribution, Temperature tcoldmin tcoldmax, Total number, Tting, Tting procedure, Upper limit, Upper limits, Upper values, Uxes, Whilst, Whole sample, Wide range.
- Teeft :
- Angular resolution, Bayesian, Bayesian inference, Bayesian inference method, Bayesian probability, Best value, Bolometric luminosity, Bottom panels, Bottom right, Cent bayesian probability, Cent contour, Cold component, Continuum, Contour level, Contour levels, Cumulative distribution, Cumulative distribution plot, Cumulative distribution plots, Cumulative distributions, Cumulative fraction, Cumulative plot, Cumulative plots, Decimal places, Different classes, Different types, Direct association, Distance ambiguity, Distance value, Distinct classes, Earliest stages, Entire sample, Equatorial coordinates, Evolutionary status, Free parameters, Global temperature, Grid, Ident tracerc, Individual sources, Infrared data, Infrared regime, Integration range, Ira, Iras association, Iras data, Iras images, Journal compilation, Large number, Likely combination, Little difference, Luminosity, Luminosity estimates, Luminosity lmin lmax, Main parameters, Marginal distinctions, Maser, Maser mass, Maser radio, Maser sample, Mass diagram, Mass mmin, Mass parameter, Mass values, Massive star formation, Massive stars, Median, Median value, Median values, Methanol, Methanol maser, Methanol maser emission, Methanol maser radio continuum source, Methanol maser radio continuum sources, Methanol maser sites, Methanol maser sources, Methanol masers, Midcourse space experiment, Millimetre, Millimetre continuum emission, Millimetre sources, Millimetres continuum sources, Minier, Mnras, Modelling, Multiple components, Null hypothesis, Observational data, Parameter, Parameter space, Peak position, Probability curves, Probability plot, Protostars, Protostellar envelope mass, Radiative transfer modelling, Radiative transfer models, Radio continuum association, Radio continuum emission, Radio continuum source, Radio continuum sources, Resultant luminosity, Resultant parameters, Same parent distribution, Same population, Section table, Seds, Simba, Simba source, Simba source list, Simba sources, Simba survey, Simple model, Small distinction, Source class, Source names, Source size, Spectral energy distribution, Spitzer data, Star formation, Star formation activity, Star formation activity sample, Statistical studies, Submillimetre, Suitable values, Synthetic seds, Systematic sampling, Tcold, Temperature distribution, Temperature tcoldmin tcoldmax, Total number, Tting, Tting procedure, Upper limit, Upper limits, Upper values, Uxes, Whilst, Whole sample, Wide range.
Abstract
Concatenating data from the millimetre regime to the infrared, we have performed spectral energy distribution (SED) modelling for 227 of the 405 millimetre continuum sources of Hill et al. which are thought to contain young massive stars in the earliest stages of their formation. Three main parameters are extracted from the fits: temperature, mass and luminosity. The method employed was the Bayesian inference, which allows a statistically probable range of suitable values for each parameter to be drawn for each individual protostellar candidate. This is the first application of this method to massive star formation. The cumulative distribution plots of the SED modelled parameters in this work indicate that collectively, the sources without methanol maser and/or radio continuum associations (MM‐only cores) display similar characteristics to those of high‐mass star formation regions. Attributing significance to the marginal distinctions between the MM‐only cores and the high‐mass star formation sample, we draw hypotheses regarding the nature of the MM‐only cores, including the possibility that the population itself comprises different types of source, and discuss their role in the formation scenarios of massive star formation. In addition, we discuss the usefulness and limitations of SED modelling and its application to the field. From this work, it is clear that within the valid parameter ranges, SEDs utilising current far‐infrared data cannot be used to determine the evolution of massive protostars or massive young stellar objects.
Url:
DOI: 10.1111/j.1365-2966.2008.14103.x
Links to Exploration step
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<term>Bayesian inference</term>
<term>Bayesian inference method</term>
<term>Bayesian probability</term>
<term>Best value</term>
<term>Bolometric luminosity</term>
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<term>Bottom right</term>
<term>Cent bayesian probability</term>
<term>Cent contour</term>
<term>Cold component</term>
<term>Continuum</term>
<term>Contour level</term>
<term>Contour levels</term>
<term>Cumulative distribution</term>
<term>Cumulative distribution plot</term>
<term>Cumulative distribution plots</term>
<term>Cumulative distributions</term>
<term>Cumulative fraction</term>
<term>Cumulative plot</term>
<term>Cumulative plots</term>
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<term>Different types</term>
<term>Direct association</term>
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<term>Grid</term>
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<term>Temperature distribution</term>
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<term>Total number</term>
<term>Tting</term>
<term>Tting procedure</term>
<term>Upper limit</term>
<term>Upper limits</term>
<term>Upper values</term>
<term>Uxes</term>
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<term>Bayesian</term>
<term>Bayesian inference</term>
<term>Bayesian inference method</term>
<term>Bayesian probability</term>
<term>Best value</term>
<term>Bolometric luminosity</term>
<term>Bottom panels</term>
<term>Bottom right</term>
<term>Cent bayesian probability</term>
<term>Cent contour</term>
<term>Cold component</term>
<term>Continuum</term>
<term>Contour level</term>
<term>Contour levels</term>
<term>Cumulative distribution</term>
<term>Cumulative distribution plot</term>
<term>Cumulative distribution plots</term>
<term>Cumulative distributions</term>
<term>Cumulative fraction</term>
<term>Cumulative plot</term>
<term>Cumulative plots</term>
<term>Decimal places</term>
<term>Different classes</term>
<term>Different types</term>
<term>Direct association</term>
<term>Distance ambiguity</term>
<term>Distance value</term>
<term>Distinct classes</term>
<term>Earliest stages</term>
<term>Entire sample</term>
<term>Equatorial coordinates</term>
<term>Evolutionary status</term>
<term>Free parameters</term>
<term>Global temperature</term>
<term>Grid</term>
<term>Ident tracerc</term>
<term>Individual sources</term>
<term>Infrared data</term>
<term>Infrared regime</term>
<term>Integration range</term>
<term>Ira</term>
<term>Iras association</term>
<term>Iras data</term>
<term>Iras images</term>
<term>Journal compilation</term>
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<term>Likely combination</term>
<term>Little difference</term>
<term>Luminosity</term>
<term>Luminosity estimates</term>
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<term>Main parameters</term>
<term>Marginal distinctions</term>
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<term>Maser mass</term>
<term>Maser radio</term>
<term>Maser sample</term>
<term>Mass diagram</term>
<term>Mass mmin</term>
<term>Mass parameter</term>
<term>Mass values</term>
<term>Massive star formation</term>
<term>Massive stars</term>
<term>Median</term>
<term>Median value</term>
<term>Median values</term>
<term>Methanol</term>
<term>Methanol maser</term>
<term>Methanol maser emission</term>
<term>Methanol maser radio continuum source</term>
<term>Methanol maser radio continuum sources</term>
<term>Methanol maser sites</term>
<term>Methanol maser sources</term>
<term>Methanol masers</term>
<term>Midcourse space experiment</term>
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<term>Millimetre continuum emission</term>
<term>Millimetre sources</term>
<term>Millimetres continuum sources</term>
<term>Minier</term>
<term>Mnras</term>
<term>Modelling</term>
<term>Multiple components</term>
<term>Null hypothesis</term>
<term>Observational data</term>
<term>Parameter</term>
<term>Parameter space</term>
<term>Peak position</term>
<term>Probability curves</term>
<term>Probability plot</term>
<term>Protostars</term>
<term>Protostellar envelope mass</term>
<term>Radiative transfer modelling</term>
<term>Radiative transfer models</term>
<term>Radio continuum association</term>
<term>Radio continuum emission</term>
<term>Radio continuum source</term>
<term>Radio continuum sources</term>
<term>Resultant luminosity</term>
<term>Resultant parameters</term>
<term>Same parent distribution</term>
<term>Same population</term>
<term>Section table</term>
<term>Seds</term>
<term>Simba</term>
<term>Simba source</term>
<term>Simba source list</term>
<term>Simba sources</term>
<term>Simba survey</term>
<term>Simple model</term>
<term>Small distinction</term>
<term>Source class</term>
<term>Source names</term>
<term>Source size</term>
<term>Spectral energy distribution</term>
<term>Spitzer data</term>
<term>Star formation</term>
<term>Star formation activity</term>
<term>Star formation activity sample</term>
<term>Statistical studies</term>
<term>Submillimetre</term>
<term>Suitable values</term>
<term>Synthetic seds</term>
<term>Systematic sampling</term>
<term>Tcold</term>
<term>Temperature distribution</term>
<term>Temperature tcoldmin tcoldmax</term>
<term>Total number</term>
<term>Tting</term>
<term>Tting procedure</term>
<term>Upper limit</term>
<term>Upper limits</term>
<term>Upper values</term>
<term>Uxes</term>
<term>Whilst</term>
<term>Whole sample</term>
<term>Wide range</term>
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<front><div type="abstract" xml:lang="en">Concatenating data from the millimetre regime to the infrared, we have performed spectral energy distribution (SED) modelling for 227 of the 405 millimetre continuum sources of Hill et al. which are thought to contain young massive stars in the earliest stages of their formation. Three main parameters are extracted from the fits: temperature, mass and luminosity. The method employed was the Bayesian inference, which allows a statistically probable range of suitable values for each parameter to be drawn for each individual protostellar candidate. This is the first application of this method to massive star formation. The cumulative distribution plots of the SED modelled parameters in this work indicate that collectively, the sources without methanol maser and/or radio continuum associations (MM‐only cores) display similar characteristics to those of high‐mass star formation regions. Attributing significance to the marginal distinctions between the MM‐only cores and the high‐mass star formation sample, we draw hypotheses regarding the nature of the MM‐only cores, including the possibility that the population itself comprises different types of source, and discuss their role in the formation scenarios of massive star formation. In addition, we discuss the usefulness and limitations of SED modelling and its application to the field. From this work, it is clear that within the valid parameter ranges, SEDs utilising current far‐infrared data cannot be used to determine the evolution of massive protostars or massive young stellar objects.</div>
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<p>Concatenating data from the millimetre regime to the infrared, we have performed spectral energy distribution (SED) modelling for 227 of the 405 millimetre continuum sources of Hill et al. which are thought to contain young massive stars in the earliest stages of their formation. Three main parameters are extracted from the fits: temperature, mass and luminosity. The method employed was the Bayesian inference, which allows a statistically probable range of suitable values for each parameter to be drawn for each individual protostellar candidate. This is the first application of this method to massive star formation.</p>
<p>The cumulative distribution plots of the SED modelled parameters in this work indicate that collectively, the sources without methanol maser and/or radio continuum associations (MM‐only cores) display similar characteristics to those of high‐mass star formation regions. Attributing significance to the marginal distinctions between the MM‐only cores and the high‐mass star formation sample, we draw hypotheses regarding the nature of the MM‐only cores, including the possibility that the population itself comprises different types of source, and discuss their role in the formation scenarios of massive star formation. In addition, we discuss the usefulness and limitations of SED modelling and its application to the field. From this work, it is clear that within the valid parameter ranges, SEDs utilising current far‐infrared data cannot be used to determine the evolution of massive protostars or massive young stellar objects.</p>
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<affiliation xml:id="a4" countryCode="FR"><unparsedAffiliation>CEA, DSM, IRFU, Service d'Astrophysique, 91191 Gif‐sur‐Yvette, France</unparsedAffiliation>
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<affiliation xml:id="a5" countryCode="FR"><unparsedAffiliation>Laboratoire AIM, CEA/DSM ‐ CNRS ‐ Université Paris Diderot, IRFU/Service d'Astrophysique, CEA‐Saclay, 91191 Gif‐sur‐Yvette, France</unparsedAffiliation>
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<keywordGroup xml:lang="en"><keyword xml:id="k1">masers</keyword>
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<keyword xml:id="k4">stars: fundamental parameters</keyword>
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regions</keyword>
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<abstractGroup><abstract type="main" xml:lang="en"><title type="main">ABSTRACT</title>
<p>Concatenating data from the millimetre regime to the infrared, we have performed spectral energy distribution (SED) modelling for 227 of the 405 millimetre continuum sources of Hill et al. which are thought to contain young massive stars in the earliest stages of their formation. Three main parameters are extracted from the fits: temperature, mass and luminosity. The method employed was the Bayesian inference, which allows a statistically probable range of suitable values for each parameter to be drawn for each individual protostellar candidate. This is the first application of this method to massive star formation.</p>
<p>The cumulative distribution plots of the SED modelled parameters in this work indicate that collectively, the sources without methanol maser and/or radio continuum associations (MM‐only cores) display similar characteristics to those of high‐mass star formation regions. Attributing significance to the marginal distinctions between the MM‐only cores and the high‐mass star formation sample, we draw hypotheses regarding the nature of the MM‐only cores, including the possibility that the population itself comprises different types of source, and discuss their role in the formation scenarios of massive star formation. In addition, we discuss the usefulness and limitations of SED modelling and its application to the field. From this work, it is clear that within the valid parameter ranges, SEDs utilising current far‐infrared data cannot be used to determine the evolution of massive protostars or massive young stellar objects.</p>
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<mods version="3.6"><titleInfo lang="en"><title>Spectral energy distribution modelling of southern candidate massive protostars using the Bayesian inference method</title>
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<titleInfo type="abbreviated" lang="en"><title>SED modelling of candidate massive protostars</title>
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<titleInfo type="alternative" contentType="CDATA" lang="en"><title>Spectral energy distribution modelling of southern candidate massive protostars using the Bayesian inference method</title>
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<name type="personal"><namePart type="given">T.</namePart>
<namePart type="family">Hill</namePart>
<affiliation>School of Physics, University of Exeter, Stocker Rd, EX4 4QL Exeter</affiliation>
<affiliation>Leiden Observatory, Leiden University, PO BOX 9513, 2300 RA Leiden, the Netherlands</affiliation>
<affiliation>School of Physics, University of New South Wales, Sydney 2052, NSW, Australia</affiliation>
<affiliation>E-mail: thill@astro.ex.ac.uk</affiliation>
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<name type="personal"><namePart type="given">C.</namePart>
<namePart type="family">Pinte</namePart>
<affiliation>School of Physics, University of Exeter, Stocker Rd, EX4 4QL Exeter</affiliation>
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<name type="personal"><namePart type="given">V.</namePart>
<namePart type="family">Minier</namePart>
<affiliation>CEA, DSM, IRFU, Service d'Astrophysique, 91191 Gif‐sur‐Yvette, France</affiliation>
<affiliation>Laboratoire AIM, CEA/DSM ‐ CNRS ‐ Université Paris Diderot, IRFU/Service d'Astrophysique, CEA‐Saclay, 91191 Gif‐sur‐Yvette, France</affiliation>
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<name type="personal"><namePart type="given">M. G.</namePart>
<namePart type="family">Burton</namePart>
<affiliation>School of Physics, University of New South Wales, Sydney 2052, NSW, Australia</affiliation>
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<name type="personal"><namePart type="given">M. R.</namePart>
<namePart type="family">Cunningham</namePart>
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<edition>Accepted 2008 October 16. Received 2008 October 15; in original form 2008 September 11</edition>
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<abstract lang="en">Concatenating data from the millimetre regime to the infrared, we have performed spectral energy distribution (SED) modelling for 227 of the 405 millimetre continuum sources of Hill et al. which are thought to contain young massive stars in the earliest stages of their formation. Three main parameters are extracted from the fits: temperature, mass and luminosity. The method employed was the Bayesian inference, which allows a statistically probable range of suitable values for each parameter to be drawn for each individual protostellar candidate. This is the first application of this method to massive star formation. The cumulative distribution plots of the SED modelled parameters in this work indicate that collectively, the sources without methanol maser and/or radio continuum associations (MM‐only cores) display similar characteristics to those of high‐mass star formation regions. Attributing significance to the marginal distinctions between the MM‐only cores and the high‐mass star formation sample, we draw hypotheses regarding the nature of the MM‐only cores, including the possibility that the population itself comprises different types of source, and discuss their role in the formation scenarios of massive star formation. In addition, we discuss the usefulness and limitations of SED modelling and its application to the field. From this work, it is clear that within the valid parameter ranges, SEDs utilising current far‐infrared data cannot be used to determine the evolution of massive protostars or massive young stellar objects.</abstract>
<subject lang="en"><genre>keywords</genre>
<topic>masers</topic>
<topic>stars: early‐type</topic>
<topic>stars: formation</topic>
<topic>stars: fundamental parameters</topic>
<topic>H ii regions</topic>
<topic>submillimetre</topic>
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