Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation

24 pages, 14 figures.-- arXiv:0504483 astro-ph pre-print supplied.-- Final full-text version of the paper available at: http://dx.doi.org/10.1051/0004-6361:20053283.

Detalhes bibliográficos
Autores: Cerviño, Miguel, Luridiana, Valentina
Formato: artículo
Fecha de publicación:2006
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/6776
Acesso em linha:http://hdl.handle.net/10261/6776
Access Level:acceso abierto
Palavra-chave:Galaxies: star clusters
Galaxies: stellar content
Hertzsprung-Russell (HR) and C-M diagrams
Methods: data analysis
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spelling Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulationCerviño, MiguelLuridiana, ValentinaLuridiana, ValentinaGalaxies: star clustersGalaxies: stellar contentHertzsprung-Russell (HR) and C-M diagramsMethods: data analysis24 pages, 14 figures.-- arXiv:0504483 astro-ph pre-print supplied.-- Final full-text version of the paper available at: http://dx.doi.org/10.1051/0004-6361:20053283.SIMBAD Objects associated to the paper available at: http://simbad.u-strasbg.fr/simbo.pl?bibcode=2006A%26A...451..475C.[Context] Synthesis models predict the integrated properties of stellar populations. Several problems exist in this field, mostly related to the fact that integrated properties are distributed. To date, this aspect has been either ignored (as in standard synthesis models, which are inherently deterministic) or interpreted phenomenologically (as in Monte Carlo simulations, which describe distributed properties rather than explain them).[Aims] This paper presents a method of population synthesis that accounts for the distributed nature of stellar properties.[Methods] We approach population synthesis as a problem in probability theory, in which stellar luminosities are random variables extracted from the stellar luminosity distribution function (sLDF).[Results] With standard distribution theory, we derive the population LDF (pLDF) for clusters of any size from the sLDF, obtaining the scale relations that link the sLDF to the pLDF. We recover the predictions of standard synthesis models, which are shown to compute the mean of the luminosity function. We provide diagnostic diagrams and a simplified recipe for testing the statistical richness of observed clusters, thereby assessing whether standard synthesis models can be safely used or a statistical treatment is mandatory. We also recover the predictions of Monte Carlo simulations, with the additional bonus of being able to interpret them in mathematical and physical terms. We give examples of problems that can be addressed through our probabilistic formalism: calibrating the SBF method, determining the luminosity function of globular clusters, comparing different isochrone sets, tracing the sLDF by means of resolved data, including fuzzy stellar properties in population synthesis, among others. Additionally, the algorithmic nature of our method makes it suitable for developing analysis tools for the Virtual Observatory.[Conclusions] Though still under development, ours is a powerful approach to population synthesis. In an era of resolved observations and pipelined analyses of large surveys, this paper is offered as a signpost in the field of stellar populations.This work was supported by the Spanish Programa Nacional de Astronomía y Astrofísica through the project AYA2004-02703. MC is supported by a Ramón y Cajal fellowship. VL is supported by a CSIC-I3P fellowship.Peer reviewedEDP Sciences200820082006info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_65011377660 bytesapplication/pdfhttp://hdl.handle.net/10261/6776http://dx.doi.org/10.1051/0004-6361:20053283reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglésinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/67762026-05-22T06:33:51Z
dc.title.none.fl_str_mv Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation
title Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation
spellingShingle Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation
Cerviño, Miguel
Galaxies: star clusters
Galaxies: stellar content
Hertzsprung-Russell (HR) and C-M diagrams
Methods: data analysis
title_short Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation
title_full Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation
title_fullStr Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation
title_full_unstemmed Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation
title_sort Confidence limits of evolutionary synthesis models. IV. Moving forward to a probabilistic formulation
dc.creator.none.fl_str_mv Cerviño, Miguel
Luridiana, Valentina
Luridiana, Valentina
author Cerviño, Miguel
author_facet Cerviño, Miguel
Luridiana, Valentina
author_role author
author2 Luridiana, Valentina
author2_role author
dc.subject.none.fl_str_mv Galaxies: star clusters
Galaxies: stellar content
Hertzsprung-Russell (HR) and C-M diagrams
Methods: data analysis
topic Galaxies: star clusters
Galaxies: stellar content
Hertzsprung-Russell (HR) and C-M diagrams
Methods: data analysis
description 24 pages, 14 figures.-- arXiv:0504483 astro-ph pre-print supplied.-- Final full-text version of the paper available at: http://dx.doi.org/10.1051/0004-6361:20053283.
publishDate 2006
dc.date.none.fl_str_mv 2006
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url http://hdl.handle.net/10261/6776
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dc.publisher.none.fl_str_mv EDP Sciences
publisher.none.fl_str_mv EDP Sciences
dc.source.none.fl_str_mv http://dx.doi.org/10.1051/0004-6361:20053283
reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
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