AGNfitter

A BAYESIAN MCMC APPROACH to FITTING SPECTRAL ENERGY DISTRIBUTIONS of AGNs

Gabriela Calistro Rivera, Elisabeta Lusso, Joseph F. Hennawi, David W. Hogg

    Research output: Contribution to journalArticle

    Abstract

    We present AGNfitter, a publicly available open-source algorithm implementing a fully Bayesian Markov Chain Monte Carlo method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGNs) from the sub-millimeter to the UV, allowing one to robustly disentangle the physical processes responsible for their emission. AGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both the nuclear and host galaxy emission simultaneously. The model consists of four physical emission components: an accretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star-forming regions. AGNfitter determines the posterior distributions of numerous parameters that govern the physics of AGNs with a fully Bayesian treatment of errors and parameter degeneracies, allowing one to infer integrated luminosities, dust attenuation parameters, stellar masses, and star-formation rates. We tested AGNfitter's performance on real data by fitting the SEDs of a sample of 714 X-ray selected AGNs from the XMM-COSMOS survey, spectroscopically classified as Type1 (unobscured) and Type2 (obscured) AGNs by their optical-UV emission lines. We find that two independent model parameters, namely the reddening of the accretion disk and the column density of the dusty torus, are good proxies for AGN obscuration, allowing us to develop a strategy for classifying AGNs as Type1 or Type2, based solely on an SED-fitting analysis. Our classification scheme is in excellent agreement with the spectroscopic classification, giving a completeness fraction of and , and an efficiency of and , for Type1 and Type2 AGNs, respectively.

    Original languageEnglish (US)
    Article number98
    JournalAstrophysical Journal
    Volume833
    Issue number1
    DOIs
    StatePublished - Dec 10 2016

    Fingerprint

    spectral energy distribution
    active galactic nuclei
    dust
    energy
    accretion
    Markov chain
    accretion disks
    physics
    distribution
    parameter
    Markov chains
    occultation
    completeness
    XMM-Newton telescope
    star formation rate
    classifying
    stellar mass
    Monte Carlo method
    attenuation
    luminosity

    Keywords

    • galaxies: active
    • galaxies: nuclei
    • galaxies: statistics
    • methods: statistical
    • quasars: general

    ASJC Scopus subject areas

    • Astronomy and Astrophysics
    • Space and Planetary Science

    Cite this

    AGNfitter : A BAYESIAN MCMC APPROACH to FITTING SPECTRAL ENERGY DISTRIBUTIONS of AGNs. / Calistro Rivera, Gabriela; Lusso, Elisabeta; Hennawi, Joseph F.; Hogg, David W.

    In: Astrophysical Journal, Vol. 833, No. 1, 98, 10.12.2016.

    Research output: Contribution to journalArticle

    Calistro Rivera, Gabriela ; Lusso, Elisabeta ; Hennawi, Joseph F. ; Hogg, David W. / AGNfitter : A BAYESIAN MCMC APPROACH to FITTING SPECTRAL ENERGY DISTRIBUTIONS of AGNs. In: Astrophysical Journal. 2016 ; Vol. 833, No. 1.
    @article{87411b4b6c5144bb9316ebbe8400da51,
    title = "AGNfitter: A BAYESIAN MCMC APPROACH to FITTING SPECTRAL ENERGY DISTRIBUTIONS of AGNs",
    abstract = "We present AGNfitter, a publicly available open-source algorithm implementing a fully Bayesian Markov Chain Monte Carlo method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGNs) from the sub-millimeter to the UV, allowing one to robustly disentangle the physical processes responsible for their emission. AGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both the nuclear and host galaxy emission simultaneously. The model consists of four physical emission components: an accretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star-forming regions. AGNfitter determines the posterior distributions of numerous parameters that govern the physics of AGNs with a fully Bayesian treatment of errors and parameter degeneracies, allowing one to infer integrated luminosities, dust attenuation parameters, stellar masses, and star-formation rates. We tested AGNfitter's performance on real data by fitting the SEDs of a sample of 714 X-ray selected AGNs from the XMM-COSMOS survey, spectroscopically classified as Type1 (unobscured) and Type2 (obscured) AGNs by their optical-UV emission lines. We find that two independent model parameters, namely the reddening of the accretion disk and the column density of the dusty torus, are good proxies for AGN obscuration, allowing us to develop a strategy for classifying AGNs as Type1 or Type2, based solely on an SED-fitting analysis. Our classification scheme is in excellent agreement with the spectroscopic classification, giving a completeness fraction of and , and an efficiency of and , for Type1 and Type2 AGNs, respectively.",
    keywords = "galaxies: active, galaxies: nuclei, galaxies: statistics, methods: statistical, quasars: general",
    author = "{Calistro Rivera}, Gabriela and Elisabeta Lusso and Hennawi, {Joseph F.} and Hogg, {David W.}",
    year = "2016",
    month = "12",
    day = "10",
    doi = "10.3847/1538-4357/833/1/98",
    language = "English (US)",
    volume = "833",
    journal = "Astrophysical Journal",
    issn = "0004-637X",
    publisher = "IOP Publishing Ltd.",
    number = "1",

    }

    TY - JOUR

    T1 - AGNfitter

    T2 - A BAYESIAN MCMC APPROACH to FITTING SPECTRAL ENERGY DISTRIBUTIONS of AGNs

    AU - Calistro Rivera, Gabriela

    AU - Lusso, Elisabeta

    AU - Hennawi, Joseph F.

    AU - Hogg, David W.

    PY - 2016/12/10

    Y1 - 2016/12/10

    N2 - We present AGNfitter, a publicly available open-source algorithm implementing a fully Bayesian Markov Chain Monte Carlo method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGNs) from the sub-millimeter to the UV, allowing one to robustly disentangle the physical processes responsible for their emission. AGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both the nuclear and host galaxy emission simultaneously. The model consists of four physical emission components: an accretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star-forming regions. AGNfitter determines the posterior distributions of numerous parameters that govern the physics of AGNs with a fully Bayesian treatment of errors and parameter degeneracies, allowing one to infer integrated luminosities, dust attenuation parameters, stellar masses, and star-formation rates. We tested AGNfitter's performance on real data by fitting the SEDs of a sample of 714 X-ray selected AGNs from the XMM-COSMOS survey, spectroscopically classified as Type1 (unobscured) and Type2 (obscured) AGNs by their optical-UV emission lines. We find that two independent model parameters, namely the reddening of the accretion disk and the column density of the dusty torus, are good proxies for AGN obscuration, allowing us to develop a strategy for classifying AGNs as Type1 or Type2, based solely on an SED-fitting analysis. Our classification scheme is in excellent agreement with the spectroscopic classification, giving a completeness fraction of and , and an efficiency of and , for Type1 and Type2 AGNs, respectively.

    AB - We present AGNfitter, a publicly available open-source algorithm implementing a fully Bayesian Markov Chain Monte Carlo method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGNs) from the sub-millimeter to the UV, allowing one to robustly disentangle the physical processes responsible for their emission. AGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both the nuclear and host galaxy emission simultaneously. The model consists of four physical emission components: an accretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star-forming regions. AGNfitter determines the posterior distributions of numerous parameters that govern the physics of AGNs with a fully Bayesian treatment of errors and parameter degeneracies, allowing one to infer integrated luminosities, dust attenuation parameters, stellar masses, and star-formation rates. We tested AGNfitter's performance on real data by fitting the SEDs of a sample of 714 X-ray selected AGNs from the XMM-COSMOS survey, spectroscopically classified as Type1 (unobscured) and Type2 (obscured) AGNs by their optical-UV emission lines. We find that two independent model parameters, namely the reddening of the accretion disk and the column density of the dusty torus, are good proxies for AGN obscuration, allowing us to develop a strategy for classifying AGNs as Type1 or Type2, based solely on an SED-fitting analysis. Our classification scheme is in excellent agreement with the spectroscopic classification, giving a completeness fraction of and , and an efficiency of and , for Type1 and Type2 AGNs, respectively.

    KW - galaxies: active

    KW - galaxies: nuclei

    KW - galaxies: statistics

    KW - methods: statistical

    KW - quasars: general

    UR - http://www.scopus.com/inward/record.url?scp=85006835152&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85006835152&partnerID=8YFLogxK

    U2 - 10.3847/1538-4357/833/1/98

    DO - 10.3847/1538-4357/833/1/98

    M3 - Article

    VL - 833

    JO - Astrophysical Journal

    JF - Astrophysical Journal

    SN - 0004-637X

    IS - 1

    M1 - 98

    ER -