IUE Ultraviolet Spectral Atlas of Standard Stars
 |
Computer Sciences Corporation |
A new MXLO data reduction tool has been developed to carry out
the IUE Standard Star Atlas Project
under the IRAF
(Image Reduction and Analysis Facility ) environment.
It facilitates reducing and analyzing data, generating the graphics
(gif and ps format) and wavelength_flux (w_f) table for MXLO spectra,
retrieving information from various related catologues, and so on.
The NEWSIPS
Low-Dispersion Merged Extracted Image (MXLO) were retrieved from the
IUE Final Archive,
supported by the Multi-Mission Archive at STScI
(MAST).
Features of The NEWSIPS MXLO Data
FITS Table
- The IUE NEWSIPS MXLO file is a 3-D FITS table, a binary table with fixed-length floating point vectors to contain the extracted fluxes and associated flags.
- Data Points
- The number of extracted points is always 640. Wavelength are linearly sampled to a uniform step size and measured in vacuum.
-
Aperture
- Double aperture low-dispersion spectra will contain two rows, with one row for each aperture.
-
Absolute calibration
- It convers to the absolute fluxes in the range of 1150-1980 Å for
short-wavelength spectra and 1850-3350 Å for long-wavelength spectra.
-
Data Quality Flags
- these flages indicate abnormal conditions
in the data which can range from fairly minor to quite serious situations.
The flags for the MXLO data should be examined carefully in order to
ascertain whether or not a particular data point is good or bad.
The data points with zero flag value indicate that the data have no know
problem condition. In general, flag values of -8 or more negative
are indicative of unreliable data. See Data Quality Flag Description
for a detailed discussion.
-
Small Aperture Spectra
- The absolute fluxes for small-aperture data
are significantly less reliable than those of large-aperture data.
Because centering errors in the small aperture can lead to large
variations in the overall observed flux level for individual spectra, it
is impossible to determine an absolute S/L ratio. Therefore, the average
of S/L over all wavelengths is normalized to unity. As a result,
the small-aperture fluxes are known in a relative sense but not in an
absolute one. See NEWSIPS Aperture Response Corrections for detials.
A New IRAF Tool for the MXLO Data Reduction
We assumed that the IUE MXLO data have been well-calibrated by the
IUE NEWSIPS pipline. And no additional calibration for MXLO data is made
by this project.
The run sequence to genaerate the spctra and flux tables is
- Convert Data Format
convert the IUE spectra from the MXLO format
to the
STSDAS Table formats using the
IUETOOL,
an IRAF subpackage provided by R. Shaw and H. Bushouse at STScI.
convert 3D FITS table to 2D STSDAS table, including the following table
columns.
* w : wavelength
* fL : flux of large aperture image
* fS : flux of small aperture image
* qL : data quality flag for large aperture image
* qS : data quality flag for small aperture image
* fLSc : averge combined flux of the large aperture and the
scaled small aperture
* qLSc : data quality flag for the averge combined image
- Make Spectra
wavelength ranges :
* LWR : 1850 - 3200 Å
* LWP : 1850 - 3200 Å
* SWP : 1150 - 1980 Å
extract ralated information from image header and
transfer it to graphics and tables.
combine images: utilized the STSDAS gcombine as a basic task
to make image combine/merge. See
Image Combine section for algorithms and parameter
specifications.
* Large Aperture Spectra
* Double (Large and Small)
Aperture Spectra
Image Combine: Algorithms and Parameters
The input images to be combined are specified by a list.
A list of input mask images, specified by the `masks' parameter,
can be used to mask bad pixels before rejection
operations. The mask images should have the same number and size
as the input images.
- For input images, set
- For each output image line,
- Parameter Specifications
Masks
The mask images, specified by the parameter, `masks', can be used
to mask bad pixels before thresholding and rejection operations.
The mask images should have the same number of and size as the
input images. A mask image is always treated as of a boolean type.
According to the IUE DQF, a zero value indicates a good pixel,
while all non-zero mask values indicate a bad pixel.
The procedure for making the masks includes the following steps:
- retrieve the "QUALITY" flag from MXLO.
- make the mask image for each spectra : assumed a zero "QUALITY"
value as good pixel.
- make the New Mask (optional).
Image Statistics and Image Scales
In order to combine images with rejection of pixels based on
deviations from some average or median they must be scaled
to a common level.
- LAp data only:
No image scaling is preformed for the LAp absolutely-
calibrated net spectra.
- SAp & LAp data:
The small-aperture fluxes are known in a relative
sense but not in an absolute one, and the absolute
fluxes for small-aperture data are significantly
less reliable than those of large-aperture data.
Therefore, the SAp data scaling stage must perform
prior to the image combining step.
The scaling stage includes the following steps:
- select wavelength region for estimating the average fL/fS
- make statistics for fL/fS within selected region
- scale fS and generate fSmed (the median scaled SAp Flux)
Weights
For each data point x(i), its own standard deviation sigma(i)
represents its measured precision. The most probable value
for the mean is the weighted average of the data points,
where each data point is weighted inversely by its own
variance sigma(i)**2 in sum.
Two types of weighting scheme are applied to process the MXLO data
- Uniform Weighting :
- Weights are constant for all pixels of each input
image.
- This uniform weighting scheme is adopted to combine a set
of LAp spectra or a set of combined LAp and SAp data. The
SAp data. The exposure time is seleted as the only
weighting factor.
- Pixel-wise Weighting
- Weight of a data point is the reciprocal of the
variance sigma(i)**2 at that pixel.
- An input error map, associated
with each input image must be specified. The sigma
is the scaled error taken from the input
error image.
- The "pixel-wise" weighting scheme is adopted to
combine the SAp and the LAp data.
Error Maps
The error map applied for our code is consist of two types of
uncertainty factor :
- "sigma" spectra : It is known that the MXLO "sigma"
spectra indicate the estimated error in the extracted
fluxes.
- median scaled factor of fL/fS over a select range, where
fL and fS are the flux for LAp and SAp, respectively.
The error maps would then be set as
- For LAp : error map = sqrt (sL)
- For SAp : error map = sqrt (sS * med)
where sL and sS is the "sigma" spectrum for LAp and SAp, respectively.
The "med" is the median scaled factor of fL/fS over the selected range.
And the "sqrt" here is the square root oprator.
Combining
After all the steps of masking pixels, scaling,
and applying a rejection algorithms, the remaining pixels are combined.
The pixels may be combined by computing a weighted average.
The weighted average, however,
is the only adopted option for our project, since the image number
for image combine is less than 5. In most cases, it is 2 or 3.
One of the following two weighting scheme must be specified for
making the weighted average combine.
- `weight'="exposure":
The uniform weights are specified in the same way as the scale factors.
The "exposure" option in the intensity scaling uses the value of the
image header keyword specified by the `expname' keyword. As implied
by the parameter name, this is typically the image exposure time
since DN levels are linear with the exposure time in SEC Vidicon.
- `weight'="pixelwise":
The weight is the reciprocal of the sigma**2, where the sigma is
the scaled error taken from the input error image.
jinger@stsci.edu
last updated: Jan. 2003