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    Robust photometric redshift determinations of gamma-ray burst afterglows at z greater than or similar to 2

    Access Status
    Fulltext not available
    Authors
    Curran, Peter
    Wijers, R.
    Heemskerk, M.
    Starling, R.
    Wiersema, K.
    van der Horst, A.
    Date
    2008
    Type
    Journal Article
    
    Metadata
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    Citation
    Curran, P. and Wijers, R. and Heemskerk, M. and Starling, R. and Wiersema, K. and van der Horst, A. 2008. Robust photometric redshift determinations of gamma-ray burst afterglows at z greater than or similar to 2. Astronomy and Astrophysics. 490: pp. 1047-1053.
    Source Title
    Astronomy and Astrophysics
    Additional URLs
    http://www.aanda.org/articles/aa/pdf/2008/42/aa10545-08.pdf
    ISSN
    0004-6361
    URI
    http://hdl.handle.net/20.500.11937/10487
    Collection
    • Curtin Research Publications
    Abstract

    Context. Theory suggests that about 10% of Swift-detected gamma-ray bursts (GRBs) will originate at redshifts, z, greater than 5 yeta number of high redshift candidates may be left unconfirmed due to the lack of measured redshifts.Aims. Here we introduce our code, GRBz, a method of simultaneous multi-parameter fitting of GRB afterglow optical and nearinfrared, spectral energy distributions. It allows for early determinations of the photometric redshift, spectral index and host extinctionto be made.Methods. We assume that GRB afterglow spectra are well represented by a power-law decay and model the effects of absorption dueto the Lyman forest and host extinction. We use a genetic algorithm-based routine to simultaneously fit the parameters of interest, anda Monte Carlo error analysis.Results. We use GRBs of previously determined spectroscopic redshifts to prove our method, while also introducing new near infrareddata of GRB 990510 which further constrains the value of the host extinction.Conclusions. Our method is effective in estimating the photometric redshift of GRBs, relatively unbiased by assumptions of theafterglow spectral index or the host galaxy extinction. Monte Carlo error analysis is required as the method of error estimate based onthe optimum population of the genetic algorithm underestimates errors significantly.

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