|
Документ взят из кэша поисковой машины. Адрес
оригинального документа
: http://www.stsci.edu/documents/dhb/web/c18_niccalib.fm5.html
Дата изменения: Tue Nov 18 00:20:23 1997 Дата индексирования: Sat Dec 22 14:48:36 2007 Кодировка: Поисковые слова: п п п п п п п п п п |
Figure 16.5: Flowchart of calnicc Processing
16.5.1 Input Files
Calnicc expects two input files:
Object Detection and Classification
After both the direct and grism images have been read by the program, calnicc checks whether there is a background estimate in the header of the *_cal.fits images and subtracts it from the data, if present. 
above the background, where is the rms of the image after removing the spectra. Since no zero point for the wavelength scale is known for those spectra, the objects' location is simply noted in the log file, but no attempts are made to extract their spectra. Background Subtraction
After source identification, an estimate of the two-dimensional background level is derived and removed from each image.
The model background image for each grism is stored in an associated background FITS file. This image is scaled to the local flux within a region around each spectrum on the grism image, and the rescaled background is subtracted from the image itself. The scaling factor is calculated by taking the mean flux values of an ellipsoidal region surrounding each spectrum (but excluding the spectrum itself), and dividing it by the mean of the background image in the same region. Pixels belonging to overlapping spectra from two or more objects are excluded from the computation of the scaling factor. The uncertainty in the background estimate is given by the square root of the sum of the errors divided by the square root of the number of pixels.
Eq. 16.2
where x is the deflection in pixels relative to the position of the object in the direct image and
is the corresponding wavelength. The distortion of the spectra is parameterized as:
Eq. 16.3
where r is the distance of a pixel (x,y) from the object of coordinates (xo, yo) and
y is the deviation in pixels of the spectrum from a horizontal line. The alignment of the spectrum is taken into account by rotating the grism image around the object position (xo, yo) prior to the extraction. The distortions in the spectra are taken into account by introducing a corresponding distortion in the weights used for the extraction.
Eq. 16.4
where the sum over the flux gji of all pixels at wavelength
is performed with weights wji. The weights used to compute the spectra depend on the size of the objects. Two scenarios are handled by calnicc: point sources and extended objects (e.g., galaxies). For point sources, the weights are computed from simulated PSFs generated via the TinyTim software. The weights are optimal for point sources with flux-independent noise, namely, background dominated. For extended objects, the weight matrix is built from the direct image, under the assumption that the shape of the object is independent of wavelength. The size and orientation of the object is computed from the direct image using the moments of the image. The weight matrix is then created by summing up all the pixel values in a given row (fixed wavelength) of the grism image that fall within the ellipse defined by the size and orientation the object in the direct image.The error estimate
ji for each pixel is taken from the ERR array of the input grism image. The error estimate j for each wavelength is then the weighed quadratic sum over the errors of all pixels at constant wavelength.
The deblending algorithm is described in detail in the calnicc manual. The basic requirement for the algorithm to work is that, at each wavelength, different spatial portions of the spectrum to be deblended have different levels of contamination. The deblending algorithm relies on the assumption that the shape of the object is the same at all wavelengths. The deblending procedure produces also an error estimate which is reported in the output FITS table and indicated in the postscript file containing the spectrum.