Des régimes d'authenticité

Morisset, Lucie K.

doi:10.2307/j.ctv18ph3qx

Cited by 42 publications

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“…With about 5 or more free parameters, each theoretical spectrum is a single stellar burst defined by metallicity and age, superimposed at a given mass fraction on a spectral component with exponentially decreasing star formation rate characterized by the age of the oldest stars, e-folding time of star formation, and both spectral components could be dust-attenuated. Similarly, 10 5 − 10 6 theoretical spectra are considered in parameter estimation on stellar spectra (e.g., Robitaille et al 2007, who considered a model defined by 14 parameters), and on H II regions (e.g., Morisset 2009, who studied a model defined by 15 parameters). One approach to prepare and manage a non-analytical model is to store all of the precalculated spectra, fixed at both the parameter choice and the parameter resolution.…”

Section: Introductionmentioning

confidence: 99%

GENERATING ON-THE-FLY LARGE SAMPLES OF THEORETICAL SPECTRA THROUGH ANN-DIMENSIONAL GRID

Yip

2009

The Astronomical Journal

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Many analyses and parameter estimations undertaken in astronomy require a large set ( 10 5 ) of non-analytical, theoretical spectra, each of these defined by multiple parameters. We describe the construction of an N -dimensional grid which is suitable for generating such spectra. The theoretical spectra are designed to correspond to a targeted parameter grid but otherwise to random positions in the parameter space, and they are interpolated on-the-fly through a pre-calculated grid of spectra. The initial grid is designed to be relatively low in parameter resolution and small in occupied hard disk space and therefore can be updated efficiently when a new model is desired. In a pilot study of stellar population synthesis of galaxies, the mean square errors on the estimated parameters are found to decrease with the targeted grid resolution. This scheme of generating a large model grid is general for other areas of studies, particularly if they are based on multi-dimensional parameter space and are focused on contrasting model differences.

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Section: Introductionmentioning

confidence: 99%