Документ взят из кэша поисковой машины. Адрес
оригинального документа
: http://www.adass.org/adass/proceedings/adass00/P1-36/
Дата изменения: Tue May 29 19:51:06 2001 Дата индексирования: Tue Oct 2 04:49:13 2012 Кодировка: Поисковые слова: m 45 |
In a previous paper (Kurtz & Mink 2000), we described a way to model the night sky by decomposing a set of observed night sky spectra into orthogonal eigenvectors using singular value decomposition. A subset of these eigenvectors is then fit to the object+sky spectra and the fit removed, leaving residual object spectra. This is made easier by the fact that for redshift work, any continuum signal is removed anyway, as only the positions and shapes of absorption and emission lines are needed.
This paper describes the software package we developed to carry out the work. Because software written as part of the RVSAO package (Kurtz & Mink 1998) already did much of the work of dealing with spectra, we adapted that and produced a new IRAF package, SVDFIT. The code was written in SPP to take advantage of many existing useful subroutines, though we experimented with various numbers of iterations using CL scripts and simpler SPP tasks.
svdvec is a Fortran program, called by IRAF as a foreign task, which decomposes an array of spectra, such as that created by svdprep , into eigenvectors using Singular Value Decomposition. An array of eigenvector spectra with the same dispersion is returned.
svdres is an SPP task which fits a set of eigenvectors to a spectrum, stacked spectra, or a list of spectra, returning the residuals as spectra in the same format as input. The processing features of svdprep are included in svdres , so it can be run as a single task on unrebinned spectra.
svdres2 is a modification of svdres to fit two sets of eigenvectors to a spectrum, removing and optional continuum in between, returning the final residuals, which may be sky-subtracted spectra. This avoids a lot of I/O and more easily parameterizes the entire two-pass process.
Sky spectra from long exposures on moonless nights were rebinned using the svdprep task to make an array of spectra with identical dispersion. Before rebinning each spectrum, the brightest night sky lines (OI at 5577, 6300, and 6363 Angstroms and Na at 5890 Angstroms) were replaced by interpolated continuum, and the entire continuum signal was removed. We assume that any spectral features on these bright lines would have been overwhelmed by the Poisson noise in a long-exposure sky spectrum.
Eigenvectors for these spectra were then computed using svdvec . Any singular valued decomposition program could be used. Figure 1 shows the first two eigenvectors.
Major sky spectrum features are then removed by fitting the first two eigenvectors--3 or 4 might be OK, too--and removing the fit from the array of spectra using svdres .
Using svdprep , the continuum is removed from the residual spectra. It turns out that many of the eigenvectors in the first pass account for night to night sky continuum variation on a scale smaller by the sky spectrum signal. Removing this spectrum by spectrum leaves only sky and object spectrum features.
We again compute eigenvectors for the array of residual spectra using svdvec . Fitting the first 7-10 of these eigenvectors using svdres removes the rest of the sky features from our object+sky spectrum. Figure 2 shows the first seven eigenvectors.
Kurtz, Michael J. & Mink, Douglas J. 1998, PASP, 110, 934
Kurtz, Michael J. & Mink, Douglas J. 2000, ApJ, 533, L183