| flamingosinfo (September2002) | gemini.flamingos | flamingosinfo (September2002) |
The NIRI package contains tasks for processing near-IR imaging and long-slit spectroscopy data. The specifics of the individual tasks can be found in their help files. This document describes the common features of the tasks and gives a description of the NIRI data format.
Only the NIRI imaging tasks have been released at this time. The spectroscopy tasks will be released at a later date.
The tasks are designed to provide a fairly complete and flexible reduction for the purpose of assessing data quality at the time of observation. Real-time reductions may not be optimal for a particular science application. The NIRI package scripts can be optimized for a particular application using the hidden parameters to achieve the best possible results.
The tasks produce logfiles of the performed processing steps. The name of the logfile may be set in each individual task, or at the package level by setting niri.logfile.
The tasks add header keywords to the output images. These header keywords contain information about the performed processing steps and the values of the critical parameters that were used.
It is recommended to use imtype="fits". This is set automatically when loading the GEMINI package.
All data must be run through FPREPARE before further processing. FPREPARE first converts the simple FITS images from Flamingos to multiextension FITS (MEF) and adds certain essential header keywords to the primary header unit (PHU). If requested, it also computes the variance and data quality planes. If no input badpixel mask is given, only saturated and non-linear pixels are flagged. FPREPARE can also be run a 2nd time on a dataset to create the VAR/DQ planes and add a badpixel mask and DQ information at a later time. Additionally, if the number of non-destructive reads is greater than 1, the data are scaled to have the correct flux-level and exposure time.
Currently, FPREPARE is the only task unique to Flamingos. Basically, FPREPARE makes Flamingos images look like NIRI images, so the remainder of the reduction steps mirror those of NIRI.
NIFLAT is used for deriving normalized flat fields as well as bad pixel masks for imaging data. The input images can be calibration unit (GCAL) flat fields, twilight flatfields, or sky flatfields, and darks. The output images are the normalized flat field and a bad pixel mask (in the data quality plane, if requested). If flagged, objects are identified and removed using NISKY prior to normalization.
The niflat.statsec, niflat.thresh_dlo, and niflat.thresh_dup parameters should be changed from the NIRI defaults to take into account differences between the NIRI and FLAMINGOS detectors. Use:
niflat.statsec="[200:1848,200:1848]" niflat.thresh_dlo=-30. niflat.thresh_dup=450.
NIFASTSKY is used for constructing sky images by median or min/max filtering. No attempt is made to specifically flag objects in the sky images. The task is mostly used for rapid reductions at the telescope, although it may suffice for standard reductions depending on the complexity of the field.
The nifastsky.statsec parameter should be changed to "[200:1848,200:1848]" from the default NIRI value.
NISKY is used for constructing sky images. Objects in the input images are flagged using information about the FWHM of the PSF and the signal in the objects. In general, it is recommended to use NISKY rather than NIFASTSKY for science quality reductions. Because NISKY does a preliminary reduction of each image and identifies objects to mask, it is considerably slower than NIFASTSKY.
The values of nisky.statsec, nisky.fwhmpsf, and nisky.rebin should be changed to:
nisky.statsec="[200:1848,200:1848]" nisky.fwhmpsf=8. nisky.rebin=10.
Depending on the seeing one may need to iterate further on the nisky.fwhmpsf parameter, although the above value should work fairly well in general.
NIREDUCE is used to sky and/or dark subtract and flat field divide the science data. The sky image can be scaled before subtraction if desired. The dark current does not appear to
The nifastsky.statsec parameter should be changed to "[200:1848,200:1848]" from the default NIRI value.
For typical reductions the user will need appropriate flat fields and short dark images, on-target science images and sky images. Flat field images taken with the calibration unit are usually taken both with the IR lamp on and off to allow separation of the instrumental thermal signature from the sensitivity response. Short darks are used to identify bad pixels.
If the target is not extended and the field is uncrowded, the on-target images may be used as the sky images. This assumes that sufficiently large dither steps were used during the observations.
1. Use FPREPARE to update the raw data images to MEF and create the variance and data quality planes, if desired. The other tasks will not run if the data has not been FPREPAREd. You may wish to run FPREPARE first on just the calibration images, use NIFLAT to derive the badpixel mask, and then run FPREPARE on your science frames using the badpixel mask as input to making the DQ plane.
2. Use NIFLAT to derive normalized flat fields and a bad pixel mask.
3. Use NISKY (or alternatively NIFASTSKY) to derive sky images. The near-IR sky level and structure varies on timescales of a few minutes, and care should be taken to combine only sky images that are close enough in time to each other and to the relevant science images that such variations are non-significant. This may take some experimentation.
4. Use NIREDUCE to subtract the sky images and apply the flat fields. Data from each filter should be processed separately, as NIREDUCE takes only one sky image at a time as its input. Better sky subtraction can often be achieved by scaling the sky image to the same mean level as the science frame (assuming the science frame is sky-dominated). There is a flag to set scaling in NIREDUCE.
The processed images may be co-added with the task GEMTOOLS.IMCOADD. Photometry may be derived with any suitable photometry package.
The NIRI spectroscopy tasks have been used to reduce Flamingos-I spectroscopic data, but this use may require that the user experiments with the parameter setting of the NIRI tasks. The FLAMINGOS package contains a database file flamingos$fsappwave.dat to use with NSAPPWAVE.