Part II: NICMOS Data Handbook

5.8 Grism Data Reduction

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The NICMOS camera 3 grisms permit multi-object, slitless, low resolution spectroscopy. The reduction and analysis of NICMOS grism data benefit from decisions made by the user and from careful, interactive examination, and are therefore discussed here rather than in the chapter on pipeline calibration.

Software to extract spectra from NICMOS grism images has been developed at Space Telescope European Coordinating Facility (ST-ECF). Two packages are available: NICMOSlook, which is interactive, and aXe, which is non-interactive. The interactive program, NICMOSlook, provides a number of tools called from an IDL GUI widget. This program is recommended for most extraction because of its versatility and interactive features. The automatic version, aXe, is recommended if an extraction of a large number of spectra from different images is desired. Both software packages, together with user documentation, can be obtained from the ST-ECF Web site. Here we offer only a brief description of grism analysis methodology, and refer the user to the documentation provided with the software for details. The ST-ECF also maintains grism calibration reference files which are included with the software distributions.

The extraction software assumes that grism data are pipeline processed like regular images with the exception that they are not flatfielded by calnica. The flatfield correction for a given pixel depends both on the pixel location (x,y) and on the wavelength of the light which is dispersed onto that pixel. The latter is not known until the location of the dispersed source has been specified, and therefor the flatfielding cannot be done in advance. It is omitted during 2 dimensional data processing through calnica. Later, after 1-dimensional spectra have been extracted from the grism images, the extraction software applies a wavelength dependent correction for QE variations (see Section 5.8.2).

The wavelength calibration for the extraction of spectra requires a direct image corresponding to each grism image. If individual exposures for the grism images are co-added before extraction, a similar co-addition should also be performed for the direct images in order to maintain the relative position of objects in the direct image with the corresponding spectrum in the grism image. If grism and direct images are processed separately by calnicb, this relative registration is, in general, not maintained.

In general, combining dithered grism images before extracting spectra is probably not a good idea whenever it can be avoided. As noted above, every pixel on the array has a different spectral response. Combining dithered grism images before extraction will combined data from different pixels, making it difficult or impossible to reliably flux calibrate the resulting spectrum. In general, we recommend to feed individual images direct from calnica to the extraction software.

5.8.1 Extraction Software

Detailed software manuals and descriptions of the extraction algorithms can be found at http://www.stecf.org/instruments/NICMOSgrism/. Only a brief summary is given below.

Input Files

The extraction software requires two types of input images, one for object finding, and one which contains the spectra to be extracted. Typically, the former is a direct image of the target field obtained with one of the NICMOS continuum filters, preferably at a wavelength within the range covered by the grism. However, the grism image itself can also be used for object finding, e.g. on the zeroth order spectra. The image which contains the spectra is assumed to be not flatfielded, which is the default in calnica.

In most cases, the input files are the output of calnica (*_cal.fits). The software packages also accept plain FITS images without NICMOS specific extensions, but some functionalities which depend on the error planes (*_cal.fits[err]) or data quality flags (*_cal.fits[dq]) will not be available.

Output Files

The basic output of the extraction are the spectra, which can be written as ASCII or FITS tables, and associated metadata.

5.8.2 Processing

Object Detection and Classification

The extraction software programs include automatic finding of objects on the direct images. NICMOSlook uses DAOFIND for that purpose, while aXe uses Sextractor to find and classify objects. In NICMOSlook, objects can also be marked interactively with the cursor.

It may sometimes be useful to use the grism images to search for particular types of spectra "by eye". In this case, the spectral images can be flatfielded using ordinary, on-orbit grism flatfields, displayed, and examined visually for e.g. emission line objects, or very red spectra. Ordinarily, these on-orbit flatfields are not used as part of NICMOS grism data processing: instead, the flatfielding is done on the extracted spectra, as is described below. Once the interesting objects have been identified, their spectra should be extracted from non-flatfielded grism images.

Location of Spectra

The positions of the direct objects can be used to compute the location and orientation of the spectra. The software packages know the approximate position of spectra relative to the position of the object on the direct image. However, the orientation of spectra varies enough from observation to observation so that a "tracing" of spectra is necessary for accurate spectrum extraction. See the NICMOSlook manual for details.

Background Subtraction

After source identification, an estimate of the two-dimensional background level is derived and removed from each image.

The grism image is not flat-fielded and the QE variations across the NICMOS detectors are strong, implying that a significant structure is present in an image of blank sky. Several options to subtract this background are provided. They include interpolation over the region of the spectra, or subtracting scaled versions of background images. The extraction software determines the regions of interpolation excluding positions occupied by other spectra in the image. The most accurate background subtraction can be achieved if a background image is specially prepared "by hand" from a dithered dataset.

Extraction of Spectra

   

Flux and Error Bars

Once objects on the images have been detected, their spectra can be extracted. The flux is then given by:

where the sum over the flux gji of all pixels at wavelength is performed with weights wji.

Several options for the weights can be used to achieve optimum S/N. Constant weights lead to an optimum extraction for high S/N spectra, while for background limited objects, weights can be derived from the profile of the spectrum to be extracted. The profile can either be determined directly from the spectrum, or predicted from the direct image for very low S/N spectra under the assumption that the shape of the object is independent of wavelength. First, the size and orientation of the object is computed from the direct image.

Since NICMOS grism images are undersampled, spectra of point sources and sources up to the size of a few pixels are best extracted using constant weights even for low S/N spectra.

The error estimate eji for each pixel is propagated from the ERR array of the input grism image. The error estimate ej for each wavelength is then the weighted quadratic sum over the errors of all pixels at constant wavelength.

   

Wavelength Calibration

The dispersion relation and the deviation of the spectra have been determined from wavelength calibration observations, and are parametrized as:

where x is the deflection in pixels relative to the position of the object in the direct image and is the corresponding wavelength. The coefficients are contained in the reference file grismspec.dat for NICMOSlook or in the configuration file (.conf) for aXe. The dispersion relation is given by:

where r is the distance of a pixel (x, y) from the object of coordinates (x_o, y_o) 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 (x_o, y_o) 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.

   

Flatfielding of Spectra

After the spectra are extracted, the fluxes are corrected for pixel-to-pixel variations in the quantum efficiency of the detector (i.e., flatfielded). The QE variations depend both on the wavelength and on the position of the object on the detector. Because of this wavelength dependence, the flatfielding cannot be performed before the spectra are extracted and wavelength calibrated. The corrected flux fc() is computed as follows

where q(x,y,) are interpolated flatfields. For wavelengths where narrow band flatfields are available, they are used. For other wavelengths, the correction factors are derived through interpolation from a set of monochromatic flatfield images (see Storrs et al. 1999, NICMOS ISR-99-002). The list of flats used for the QE correction is shown in Table 5.10. The most recent flatfield reference files can be obtained at:

http://www.stsci.edu/hst/nicmos/calibration/reffiles/flatfile_list.html.

Table 5.10: Default Flatfields For Spectra

Flatfield File	(µ)	FWHM (µ)	Filter
i191346kn_flt.fits	1.07990	0.0096000	F108N
i191346mn_flt.fits	1.12830	0.0110000	F113N
i191346pn_flt.fits	1.64600	0.0170000	F164N
i191346qn_flt.fits	1.65820	0.0164000	F166N
i191346sn_flt.fits	1.87380	0.0192000	F187N
i191346tn_flt.fits	1.90030	0.0174000	F190N
i1913470n_flt.fits	1.96390	0.0186000	F196N
i1913471n_flt.fits	1.99740	0.0206000	F200N
i1913472n_flt.fits	2.12130	0.0206000	F212N
i1913473n_flt.fits	2.14870	0.0200000	F215N
i1913475n_flt.fits	2.39770	0.1975000	F240M

 
   

Flux calibration and Correction for Pixel Response Function

Once the spectra are extracted, the count rates in ADU/second are converted to physical units using calibration data form photometric standards P330E and G191B2B.

Undersampling of NICMOS grism images in combination with significant variation of the QE across any given pixel imposes a wave-like pattern onto extracted spectra of point sources and small objects. Since spectra are not exactly aligned with the rows of the images, the exact sub-pixel position and orientation of the spectra determines the phase and period of those waves. A simple model can be used to correct this effect for point sources. Details are discussed in an article by W. Freuding in the May 1999 issue of the ST-ECF Newsletter.

   

Deblending of Overlapping Spectra

Since the NICMOS grisms are slitless, overlaps among different spectra are likely to happen. The strategy of observing the same target at different telescope roll angles helps remove overlap in many instances.

aXe reports an estimate of the contamination by nearby objects. NICMOSlook includes an algorithm designed to remove or minimize contamination of one spectrum from another. The deblending algorithm is described in detail in the NICMOSlook software 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.