IRAF help page for package noao.twodspec.multispec, program newimage

• image
• output
• lower = -10
• upper = -10
• lines = *
• ex_model = no
• clean = yes
• nreplace = 1000.
• sigma_cut = 4.
• niterate = 1
• model = smooth
• fit_type = 2
• naverage = 20
• interpolator = spline3
• verbose = no

newimage image output

image

output

lower = -10

upper = -10

lines = *

ex_model = no

clean = yes

sigma_cut = 4.

niterate = 1

model = smooth

fit_type = 2

naverage = 20

interpolator = spline3

verbose = no

A new multi-spectra image is created using the description of the multi-spectra image in the MULTISPEC database associated with image. The user selects the image lines from the original image to be in the new image. The options allow the creation of model images or images in which the bad or deviant pixels are replaced by model profile values.

If ex_model = yes or clean = yes model spectra are fit to the spectra in the image. There are two models: a five parameter Gaussian profile called gauss5 and profiles obtained by averaging naverage image lines surrounding the image line being modeled called smooth. The model is selected with the parameter model.

When ex_model = yes an image containing model spectra is produced.

When clean = yes pixels with large residuals from the model are detected and removed from the model fit. The selected model is fit to the pixels which are not in the bad pixel list (not yet implemented) and which have not been removed from the model fit. The sigma of the fit is computed. Deviant pixels are detected by comparing them to the model to determine if they differ by more than sigma_cut times the sigma. The model fit is iterated, removing deviant pixels at each iteration, until no more pixels are found deviant or nreplace pixels have been found. The pixels removed or in the bad pixel list are then replaced with model values. (To clean and extract the spectra with this algorithm see msextract.)

There are some technical differences in the model fitting and cleaning algorithms for the two models. In model smooth the fit for the profile scale factors is done independently for each spectrum and automatically corrected when a bad pixel is detected. This fitting process is fast and rigorous. The parameter nreplace in this model refers to the maximum number of pixels replaced per spectrum.

In model gauss5, however, the profile scale factors are fit to the entire image line (hence its ability to fit blended spectra). There are two fitting algorithms; a rigorous simultaneous fit and an approximate method. The simultaneous fit is selected when fit_type = 1. This step is relatively slow. The alternative method of fit_type = 2 sets the scale factor for each spectrum by taking the median scale, where scale = data / model profile, for the three pixels nearest the center of the profile. The median minimizes the chance of a large error due to a single bad pixel. This scale may be greatly in error in the case of extreme blending but is also quite fast; the extraction time is reduced by at least 40%. The steps of profile fitting and deviant pixel detection are alternated and the maximum number of iterations through these two steps is set by niterate. The default of 1 means that the model fitting is not repeated after detecting deviant pixels.

The option verbose can be used to print the image lines being extracted and any pixels replaced by the cleaning process.

To create a cleaned version of the image using model smooth for cleaning:

cl> newimage image newimage

To create an model image using model gauss5:

cl> newimage image newimage ex_model=yes model="gauss5"

msextract,