MOPEX Online Manual
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Overlap Modules: S/N Estimator
Output Directory Keyword: SIGMA_DIR
Default Output Directory: <output_dir>/Sigma/
Depends on: Input files
Important Notes: This module is overridden if you have the have_uncertainties option turned on in your namelist.
PURPOSE
-
This module is used to estimate the uncertainty images when no independent
uncertainty measurement is available.
This module is usually not turned on since almost all Spitzer
data have uncertainty images from the Spitzer pipeline. If users
decide not to use the pipeline uncertainty images, this module needs to be switched on. Warning: if the wrong Gain or Read_Noise are specified, this module may produce uncertainties that are too large, and this will result in no outlier detection.
PARAMETER BLOCK
-
&SNESTIMATORIN
Gain = 66.8,
Read_Noise = 8.8,
Confusion_Sigma = 0,
&END
INPUT
Gain in e-/image unit: (float) This is the parameter used to translate the measured data counts to physical units of MJy/sr (or mJy/sq.arcsec). All Spitzer BCDs are in units of MJy/sr. This parameter has the same name as stored in the Spitzer BCD fits header, however, they have different units. MOPEX gain has the unit of e-/MJy/sr (or e-/mJy/sq.arcsec) and the fits header gain has the unit of e-/DN. Gain used by MOPEX can be computed from the gain parameter in the fits header by using formula:
Gain (mopex) = Exptime * Gain (header)/FLUXCONV
Here FLUXCONV is the flux conversion factor between e-/DN and surface brightness unit of MJy/sr (or mJy/sq.arcsec). FLUXCONV can be found in the Spitzer BCD fits image header.
Read_Noise in e-: (float) This parameter characterizes the noise in e- when the data are being read out from detector. It is also stored in the BCD fits image header.
Confusion_Sigma in e-: (float) The 1-sigma confusion noise limit. The source of this noise is from the spatially unresolved background galaxies. For IRAC data, this information can be found in http://ssc.spitzer.caltech.edu/documents/compendium/resolution/confusion.html. For MIPS data, the webpage is http://ssc.spitzer.caltech.edu/mips/documents/sens_hdf.pdf
OUTPUTS
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S/N Fits Files (*_sigma.fits): this module outputs the uncertainty image for each BCD image.
DISCUSSION
This module allows users to estimate the noise from the BCD data, if no uncertainty images are provided. Here you need to input gain, read out noise and confusion limit approriate for the dataset. We suggest that users look at the BCD image header to get gain and read out noise. This module should not be used unless you can not use the pipeline-provided uncertainty images.The module estimates the pixel uncertainty for each pixel in the image using the following model:
Here the parameters of the model Read_Noise σreadnoise, Gain g, and Confusion_Sigma σconfusion are specified in the namelist. The last term is the Poisson noise determined by the pixel value I.
The units of Read_Noise and Confusion_Sigma here are electrons. The module is designed to work with the images in DN units. If you are working with the images in units of surface brightness (MJy/sr; all Spitzer BCDs) then the gain should include the conversion factor from surface brightness units to electrons. The product of this step is the uncertainty images.
If sigma_weighted_coadd is set in the namelist, then the mosaic image is created by averaging the interpolated images by weighting with the interpolated images as well as the coverage maps. This is not recommended for Spitzer data. By default the mosaic image is created by averaging the interpolated images weighted only with the coverage maps.
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