A.1 Overview
The driving philosophy behind the NEWSIPS Project is to provide a
homogeneous data product within a completely automated processing
environment. An algorithm to compute background fluxes in high dispersion,
BCKGRD, was designed with this philosophy. Although this algorithm
generally provides a good background flux estimate, the results are not
always optimal for particular regions of some images. A customized
interactive determination of the background fluxes based on individual
image characteristics can produce a more accurate estimate of the
background in certain cases when data pathologies are present.
The determination of smoothed background fluxes follows the geometric
resampling of pixels, so it is done with the high-dispersion resampled
image (SI). A representative high-dispersion SI given in Figure A1 shows
echelle orders running horizontally and the spatial (cross-dispersion)
direction running vertically. We will refer hereafter to the image sectors
at the top and bottom as the ``ends" of the image. The BCKGRD module
produces smoothed background flux spectra which, together with the gross
spectra, form the net spectra. The background model is created by computing
continuous Chebyshev polynomial functions from pixels that sample valid
background fluxes. For images with no continuum, the algorithm proceeds
straightforwardly by sampling the neighboring interorder fluxes for each
spectral order and fitting the result to a Chebyshev polynomial. The BCKGRD
algorithm models the backgrounds of images having continuum flux in two
one-dimensional passes, as described below.
For continuum source images, the background determination algorithm begins
by sampling fluxes along the spatial direction with an extraction slit 5
pixels tall. We refer to these cuts as ``swaths" and the steps taken for
this group of vertical swaths as ``Pass 1" operations. A total of 25
parallel swaths (26 for SWP) are taken in the spatial direction, each
located at a column position approximately 27 pixels larger than the
preceding swath. Figure A1 shows the positions of these Pass 1 swaths as
dotted vertical lines. The mean flux is computed along the extraction slit
from pixels containing valid local background fluxes; pixels are given a
weight of zero if they fall along the spectral orders or if they do not
contain valid fluxes (i.e., they have negative nu flags). The result
of this process is a background-flux array for each swath that is
discontinuous in spatial pixel number and contains noise. The array is
adjusted to account for interorder contamination using a Point Spread
Function (PSF) modeling technique described in Section A.2.2. The adjusted
array is fit to a trial Chebyshev function of degree 7. This fitting
degree is decreased if ringing is detected in the initial solution. The
resulting solution is a smoothed continuous fit to the raw background
fluxes. Each of the Pass 1 swaths is fit to a Chebyshev polynomial array
in the same fashion.
In the second pass (Pass 2) solutions from Pass 1 are used to form a second
and final set of 7th degree Chebyshev functions which model the background
fluxes at the positions of the echelle orders. This solution is determined
by sampling the solutions from the first pass at the locations where they
intersect the echelle-order locations. The echelle-order locations are
represented as solid lines in Figure A1. The computation of a Chebyshev
function from this sampling interpolates a continuous array of smoothed
background fluxes at each wavelength along these orders. The solution is
extrapolated beyond the target edges from the last calculated value.
The final background solutions are represented in two forms. First, a
background vector of 768 values is saved and written to the high-dispersion
merged extracted FITS file as a separate record for each order. Second, the
7 Chebyshev coefficients, together with a magnitude scale factor and
starting and ending positions are written as FITS keywords. The ``gross"
spectrum may be reconstructed simply by adding the background and the net
flux solutions.
Figure A1: Layout of the background extraction swaths on a
sample SWP high-dispersion image. Lines running in vertical
(spatial) direction are the Pass 1 extractions. Raw fluxes are
sampled along these lines that are within the target ring and
outside or between the echelle orders. The reconstructed
background solutions created in Pass 2 are placed in the positions
of the echelle orders (horizontal lines).
A.2.1 Data Screening
Prior to the execution of the background determination algorithm, the
fluxes and nu flags of each Pass 1 swath are evaluated to ensure that
enough valid pixels exist in a swath to produce a reliable fit to the data.
Any pixels containing flags with excessively negative ITF extrapolations or
cosmic ray hits are added to a ``pixels-to-avoid'' working template for
each Pass 1 swath. A few checks are then made to ensure that the
background-determining algorithm will run properly. For example, if too few
valid pixels can be found in a given Pass 1 swath to permit a reliable
background extraction, the solution is set to a zero-flux array. If this is
not repeated for the next swath, then the solution for the zeroed-out swath
is reset to the mean of the two adjacent swaths.
A.2.2.1 Basic Flow
For continuum source images, a series of 25 or 26 nearly equally spaced
extraction swaths (slit height of 5 pixels) are made in the spatial
direction of the high-dispersion SI, with a starting position at small
spatial pixel numbers (short wavelength end). The swath sample positions
were carefully selected to avoid contamination from any possible
low-dispersion (double) exposure. Except for the first and last few Pass 1
swaths, which form short chords along the left and right edges of the
camera image, each swath samples fluxes for nearly the entire range of
sample positions; that is to say that they include pixels at the spatial
ends of the camera which are not affected by contaminated
interorder-overlap flux. The ``interorder overlap" flux is described by a
PSF model described below. The accumulated effects of overlapping PSFs
increase as the orders become more closely spaced. The accumulation causes
the interorder overlap to become increasingly severe until the camera
sensitivity falls off at short wavelengths. It is this overlap which causes
local background extractions in IUESIPS to be systematically high for
short-wavelength orders and which necessitated a strategy for BCKGRD to
sample background fluxes in distant uncontaminated regions as well as local
contaminated ones.
The fluxes sampled from interorder pixels are modified if they are affected
by contamination from neighboring orders. A model PSF provides an estimate
of how much the fluxes should be offset before the Chebyshev fit is made
(see ``PSF Modeling'' section below). The PSF model itself consists of two
components, first, a monotonically decreasing function out to about four
pixels and, second, a ``halation ramp" which extends from four to about
seven pixels from the center of each order profile. Each of these
components is responsible for order overlap in a particular range of
echelle orders. We will refer to the image area where the monotonic portion
dominates as the ``Interorder-Overlap Region" (IOR). The halation component
is actually an extension of the IOR. However, BCKGRD treats it separately
because, unlike the IOR, its characterization is independent of the order
profiles.
The IOR and halation-dominated portions of the Pass 1 swath are indicated
in Figure A2. The initially sampled interorder fluxes in both the IOR and
the halation regions are revised downward during the course of the
calculations. The original and revised ``working" fluxes are shown in this
plot as squares and small crosses, respectively, and the flux revision for
one point is shown as a downward pointing arrow. Fluxes for pixels in the
halation-ramp region are affected by interorder contamination just like the
triangle-inscribed IOR region, but in these cases only from the flat PSFs
of the two neighboring orders. Because this overlap is constant, the
algorithm may estimate it for several orders along the swath and determine
a robust correction for halation. Note that the overwhelming majority of
orders are subject to overlap by either the monotonic (IOR) or ramp
components of the PSF.
In broad strokes, an initial estimate of the overlap for a given order is
performed by computing peaks of adjacent orders above the interorder flux
minimum and using the PSF to compute the fractional order flux to be
subtracted from the sampled interorder fluxes. Before discussing this
procedure in detail, it is necessary first to describe in more detail the
different domains of a Pass 1 swath where each of the PSF components
dominates.
A.2.2.2 Differentiation of Swath Regions
The continuous undulating line in Figure A2 is the Chebyshev solution for
an actual Pass 1 swath of an SWP image. The IOR area is denoted by a dashed
triangle with vertices at spatial pixel numbers nc, nf, and nd. The normal
execution of the background determination code requires that valid
background pixels are sampled at these three points as well as the adjacent
points nc-1, nf+1, and nd+1. In Figure A2 the rise from nd to nf is
determined largely by the width of the PSF. This slope may vary because of
circumstances for a particular exposure. For example, the slope of the
{nf~nd} leg of the IOR triangle is different for trailed images than for
point-source images. Because this slope is a property of the swath itself,
BCKGRD can use it to refine the overlap as estimated from the model PSF.
The overlap between nc and nf depends primarily on source energy
distribution and not exposure parameters. The effect of overlap near pixel
nc appears to decrease in the figure, but this is an artifact of the
rapidly decreasing camera sensitivity at short wavelengths.
A.2.2.3 Swath-Dependent Data Files
Default template files have been created that specify the spatial pixel
numbers to be avoided in the Pass 1 background extraction for each camera
and for all source types (point, extended, and noncontinuum) sources. The
file also includes values for nc, nf, and nd of the IOR. Initially, the
list of spatial positions to be avoided includes only the starting and
ending values and the echelle order positions, but this is modified for a
particular image in the initial Data Screening step (Section A.2.1) from
the nu flags determined in the raw image screening (RAW_SCREEN) step.
Figure A2: Crosscut of background fluxes from a central
``Pass 1" swath through an SWP image. Stellar fluxes are off-scale
in this diagram. The triangular area describes the local raw
background fluxes in the Interorder-Overlap Region where order
crowding is severe; the halation region is shown to the right.
Small crosses denote the raw fluxes corrected for overlap by the
PSF model. The solid line is the Pass 1 solution, a Chebyshev,
degree-7 polynomial.
A.2.2.4 On-the-Fly Pathology Screening
The fluxes on and off the echelle orders are used in Pass 1 to determine
representative order heights and to estimate a PSF scaling factor unique to
every swath of the image processed. Checks are made at this point to clip
aberrant background flux values not flagged by RAW_SCREEN (e.g., from
negative flux extrapolations or undetected cosmic rays). Anomalous
interorder flux values in such cases are replaced with local means. The
same procedure is repeated if necessary to substitute highly anomalous
interorder fluxes with the means of the four neighboring interorder fluxes.
This procedure was adopted to cope with small pixel clusters in the
long-wavelength cameras that are prone to excessive negative
extrapolations.
A.2.2.5 PSF Modeling
The estimate of the contamination of IOR and halation-ramp region fluxes by
illumination from the spectral orders proceeds in two steps. Information
from the PSF model and from the echelle order fluxes is not used in the
first step, nor is a halation overlap region defined. The solution in this
``Step 1" of Pass 1 is determined entirely from a Chebyshev interpolation
from points in the end (non-IOR) regions of the swath. In the presence of
certain image pathologies (Section A.2.4), as well as the first and last
few Pass 1 swaths, this Step 1 is the only step; it becomes the final
solution for the Pass 1 phase.
Figure A3: A depiction of the influence of interorder overlap
in progressively raising the intensity of (unit height) orders
towards shorter wavelengths (left). The dashed lines represent
intensities the orders would have if there were no overlap.
For the great majority of Pass 1 swaths, i.e. those passing through the
middle of the camera image and not encountering poor statistical solutions,
BCKGRD continues with a Step 2. This step uses the solution from Step 1 as
a starting point to compute a PSF-compensated solution in which we attempt
to subtract from the measured interorder fluxes the contamination from
adjacent orders. As can be inferred from Figure A3, the correction for
this overlap can be determined by the simple relation:
Flux_corr = OrderHeight * 2*PSF_1/[1 - 2(PSF_1 - PSF_2)]
where Flux_corr and Order Height are given in FN flux units per pixel
and PSF1 and PSF2 are given as a fraction of the true local order height.
Step 2 concludes by subtracting Fluxcorr from the local measured
background at each interorder location and refitting a new Chebyshev
solution through the adjusted Pass 1 swath values. Note particularly that
in NEWSIPS an explicit effort is made to compensate for the contamination
of the it interorder fluxes (i.e., the background fluxes) in the manner
described above, there is no adjustment made to correct the on-order
fluxes (gross fluxes) for the effects of overlapping orders.
BCKGRD uses the same trial spatial PSF model for all types of continuum
source types in a given camera. The algorithm also assumes that the PSF is
global over the image. The model was determined by replicating the
accumulation of flux overlap toward short wavelength orders from a large
number of actual images.
Because the PSF may actually change from image to image, the algorithm
attempts to accommodate such changes by using on-the-fly order information
to refine the PSF model - specifically the slope of the {nf~nd} leg of the
IOR triangle. This is accomplished by comparing the fractional flux
overlap with the model result for a reference order within the IOR, that
is by comparing the increase in overlap for this order to the overlap
found at the start of the IOR (pixel nd). If the measured and model slopes
agree within a tolerance factor (1.5 x), the program adopts the measured
slope and scales the model PSF accordingly. Otherwise, the model PSF is
used. Tests show that various Pass 1 swaths for a given image can either
pass or fail this tolerance test independently.
A.2.2.6 Post-Solution Pathology Tests
After a continuous Chebyshev solution is computed for a particular Pass 1
swath, a series of data pathology checks are performed on the solution (see
Section A.2.5). These tests are performed only on images of point and
extended sources containing continuum flux. If the solution fails any of
the pathology checks, an appropriate condition is relaxed (e.g. the PSF is
not used to calculate the true interorder background, or the degree of the
Chebyshev fit is decreased by one). The solution is then recalculated
iteratively. Since even adjacent Pass 1 swaths sometimes can pass different
pathology tests, their solutions can occasionally be different. This is a
subtle but often important source of error in the final background
solution. The results of these differing solutions are manifested as
``spike structure" in Figure A4, as described below.
Figure A4: Final background solution for SWP20931, order 95
(smooth solid line). The comb structure connected to the solution
reflects the solutions for the various Pass 1 swaths sampled at
the line position of this order.
A.2.3 Pass 2: Dispersion Direction Swaths
In the second operation, Pass 2, inferred background fluxes at the order
positions are sampled and assembled as arrays in the spectral direction.
The fluxes from the Pass 1 solutions are used to compute a continuous
Chebyshev solution for the background at each echelle order position. The
generation of a fit along the positions of the echelle orders proceeds with
the computation of a 7th-degree Chebyshev function interpolated for all
sample (wavelength) positions. In this step it is not necessary to screen
or exclude pixels or to test for data pathologies - only a single iteration
is required. The Pass 2 operation tends to dilute the effects of poor
solutions from a single Pass 1 swath. However, it also introduces a second
smoothing into the final background surface.
Figure A4 shows a final solution obtained for Order 95 of image SWP20931
on the B0 star HD93222. This order contains a strong Lyman-alpha
feature. The spike pattern indicates samplings from the 26 swaths in
Pass 1. A comparison of the residuals (comb pattern in the figure) shows
that the solution for the Lyman-alpha order is less certain than those
for long-wavelength orders where uncontaminated background pixels are
common.
A.2.4 Non-continuum images
The existence or absence of continuum flux in an IUE echellogram is
determined by the order registration module. Because interorder-overlap
flux can affect background determinations only for continuum images, in the
noncontinuum case BCKGRD does not go through a Pass 1 step. The background
estimates for these images are determined by sampling the interorder
background on the long-wavelength side (spatial) of a given order with a
one-pixel slit. A 7th degree Chebyshev solution is then computed for this
nearly continuous array of interorder pixels. Note that tests show that
some minor contamination from strongly saturated emission lines has been
detected in neighboring orders.
A.2.5 Data Pathology Assessments
Occasionally circumstances in the interorder fluxes lead to solutions that
are slightly unstable, producing wiggles in an interpolated region that go
beyond the flux range of sampled pixels at the two spatial ends of the
camera. Such occurrences may be caused by abnormal conditions affecting the
image (e.g. target-ring glow, cosmic ray hits, LWR flare, flux down-turns
at camera edge). A series of eight ``pathology tests" has been added to
BCKGRD to protect against blind solutions at the end of Pass 1 that do not
agree with simple and often correct interpolations. These checks generally
rely on a comparison of fluxes at two or more pixels along the swath or on
a ratio of smoothed flux ranges. The rms statistic computed from local raw
background fluxes is a convenient unit of measure for flux ranges because
it does not rely upon source brightness, exposure time, or an arbitrary
flux level. In most cases a failure of a solution in a pathology test
causes either the PSF information not to be used in Pass 1, the degree of
the polynomial fit to the interorder data to be reduced, or both. Lowering
the fitting degree has the effect of removing extra wiggles in the
solution; however, the degree of the Chebyshev fit is never reduced below
3. In those cases where superfluous wiggles in the IOR region persist
stubbornly after a few trials, a simple linear interpolation is adopted
between ``good'' regions. This may occur for spatial positions toward the
short wavelength (spatial) end of the IOR for certain swaths having
reliable background samplings at the target edge. These tests are used only
for continuum source images in Pass 1. Therefore, the final output
background vectors from Pass 2 are still guaranteed to be pure continuous
Chebyshev functions.
The data pathology tests keep track of the number of iterations through the
swath-fitting routines, as well as a history of previous failure modes.
Certain pathologies have been found to oscillate sometimes between two
types of failures. If such patterns are detected, a fall-back exit option
is adopted. Table A.1 summarizes the pathology checks and the strategies
for circumventing them in subsequent iterations of the swath-fitting
routine.
BCKGRD establishes a hierarchy of severity for the various pathologies it
identifies. Tests 1-4 and 8 are the most critical, and the responses to
failure of them are therefore more severe. Each test looks for a different
and well known pathology. For example, Test 4 and Test 6 search for subtly
different degrees of a similar pathology, so their response strategies are
different. Test 2 searches for a solution minimum within the IOR (exceeding
a tolerance) compared over the rest of the swath whereas Test 8 searches
for a flux minimum in the solution anywhere within the swath. Test 2
permits a milder fix while Test 8 looks for a more severe IOR minimum. If
Test 8 detects this condition, a linear interpolation between nc and 50
pixels beyond nd is used, with points in the halation ramp taken from the
measured interorder flux values, without consideration of the PSF. If
Test 2 repeatedly fails, the IOR-minimum pathology is guaranteed to be
addressed by linear interpolation in Test 8.
Experience has shown that Test 7 is one of the most commonly occurring
rejections of an initial solution. This test rejects having a local maximum
at the high-sample number end of a swath. These rejections are often caused
by an abnormal global shift of the spectral image format combined with
target ring glow, but they can result also from any systematic rise in flux
toward the short wavelength (spatial) end of the camera as well. If the
pathology cannot be circumvented by decreasing the degree of the Chebyshev
fit or by resorting to ignoring the PSF information in the swath-fitting
routine, the solution is admitted. Test 7 is similar to Test 3 but checks
for two maxima in the the middle of the short-wavelength (spatial) end of
the image. In Test 3 one of the maxima searched for must be within the IOR,
but this is not necessarily true for Test 7. Test 3 also checks for flare
discharges which can occur in the long-wavelength corner of LWR images and
computes two possible solutions: the first is a pure Chebyshev solution
with the flare points deweighted and the second is a quadratic
extrapolation of the solution without the flared region included. The
solution with lower fluxes in the upturned flare region is then adopted, as
this solution causes ringing less frequently.
A rejection by Test 5a, which searches for a maximum in the IOR, causes the
degree of the fit to be decreased on the first iteration and halves the
trial PSF slope factor. It interpolates linearly over this region if
additional iterations are necessary. Test 5b searches for an excessive
minimum for spatial positions at the short wavelength (spatial) end of the
image and the beginning of the IOR. A minimum in the IOR is usually caused
either by a bright target ring at the beginning of the swath, or an
overestimate of the PSF. Such conditions cause a false overcorrection for
background contamination within the IOR and hence a background solution
which is too low. Both tests are addressed by decreasing the Chebyshev
degree.
Table A.1: Recourses to Various Pathological Trial Solutions
TEST NUMBER CONDITION/RECOURSES
1 Solution monotonic between nd & nf:
Short Swaths If poly. degree > 3, decrease degree;
otherwise linearly interpolate.
2 Min. in IOR between nc & nd;
IOR Min. decrease degree,
on third attempt, give up.
3 Max. at high sample no.:
Max. near swath end decrease degree.
[LWR Flare] [Lower weights for points in flare]
4 Max. in IOR is max. for swath:
IOR Max. decrease degree
5a Max. at low sample no.:
Max. at or near swath start decrease degree & reduce
slope of PSF model
5b Min. at low sample no.:
Min. at or near swath start 1st time: go to Test 6;
thereafter: decrease degree or
interpolate linear solution
6 Max. within IOR:
IOR max. decrease degree or
interpolate linearly across IOR
7 Max. at high sample no.:
Max. at end decrease degree
8 Absolute min. is in IOR:
IOR min. interpolate linearly in IOR
Absolute!
A.3 Failure Modes
The background swath fitting process is considered to fail a particular
Pass 1 swath if any of the following conditions apply.
* Fewer than 20 valid background pixels exist in the swath.
* The first two valid background pixels in the swath are not continuous.
* There are no data gaps in the swath (continuum image only).
* The starting pixel for the IOR is less than spatial pixel number 3 in
the swath (continuum sources only).
* The first and last pixel with usable background fluxes are on the same
side of the IOR (continuum sources only).
When a solution for a Pass 1 swath fails any of these tests, the background
solution is set to zero for the entire swath. If this occurs for an
isolated swath, an interpolated solution based on the two neighboring
Pass 1 swaths is substituted for the original solution. If two consecutive
Pass 1 swaths, or a total of five, have null values based on these
conditions, the background arrays for the entire image are set to zero, and
BCKGRD has ``given up" on the image. This occurs for an exceedingly small
number of images (e.g., about 13 out of 12,898 images in the GSFC
high-dispersion archives), usually as a consequence of a major portion of
the image being missing. This condition is documented in the processing
history log.
A.4 Problem Areas
Tests have shown that high-dispersion spectra from each of the three IUE
cameras have characteristics that impose unique challenges for automated
background extraction algorithms. The great diversity of image types in the
archives prohibits implementing any strategy that makes assumptions about
the behavior of source spectra in order to fix background problems,
particularly in an automated processing environment.
The SWP camera has a unique confluence of conditions, a circular target
ring and a blaze function shifted towards short wavelengths along the
echelle-dispersion direction, which together make it impossible to sample
local background at the short wavelength corner of the image. For spectra
of early-type objects, these conditions almost guarantee that for the
shortest-wavelength swaths in Pass 1 no pixels will be found at the short
end (spatial) of the swath to use for interpolation in the IOR. In these
cases the solutions will turn upwards in this region. This problem is
addressed at the end of the first pass of BCKGRD by grafting a solution
from a neighboring swath in place of the turned-up solution. Experience
shows that this strategy is sometimes only partially successful for SWP
images. As a result the final background solution in the short-wavelength
corner is sometimes too high. This behavior can influence the background
solutions for the blue wing of Lyman alpha and lines of neighboring
echelle orders that fall in the same blaze region to be as much as a few
percent too high relative to the local continuum. This is probably the most
significant systematic error produced by BCKGRD. However, we emphasize that
this problem is inherent in the placement of these spectral features close
to the target ring. Therefore, even customized extractions will not be able
to model an accurate local background solution in this region.
Both long-wavelength cameras have the problem of a number of specific
pixels being habitually converted from low DNs to highly negative FNs. In
general, this is not a problem because BCKGRD clips highly aberrant fluxes
before its Chebyshev fitting. However, the problem of clipping only
aberrant fluxes is complicated for the LWP camera because of the turndown
of the local background at the long-wavelength (spatial) end of the target.
This complexity has been treated by disallowing clipping of anomalous
fluxes in this region of the LWP image. Another complication is that
although a 7th degree is usually sufficient to fit the flux turndown near
the target ring, the solution may not adequately follow the turndown if the
background fluxes are noisy or have caused the Chebyshev degree to be
reduced to less than 6. Generally, there are no ``notable" spectral lines
in these orders, but a customized extraction can take care of this problem
should the need arise.
An additional characteristic of the LWP camera fluxes is the abrupt
increase in noise in the short (spatial) end of the image. It is not clear
that this causes systematic background deviations, but it may cause a
degraded accuracy in the background result.
The LWR camera exhibits a few patches of enhanced sensitivity that are not
completely calibrated out in the photometric correction step. The most
dramatic of these is the ``flare" at the long spatial and short-wavelength
(dispersion) corner of late-epoch images. Inspection of many images shows
an upturn in the local background in this area even during the IUE
Commissioning Period. Therefore, background fluxes extracted in this area
either by NEWSIPS or by customized extractions should be used with caution.
NEWSIPS Manual Addendum
II. Revised LWR Ripple Correction
The LWR ripple correction described in section 11.2.1 of the NEWSIPS image
processing manual was modified shortly after the Goddard images had been
processed. The new correction derived by Cassatella [1] slightly improves
the ripple correction shortward of 2200 Angstroms. The new correction was
implemented at Vilspa and, in November, 1997, all the Goddard LWR images
were reprocessed with the new correction. As a result, all archived LWR high
dispersion NEWSIPS data requested after November 14th, 1997 use the ripple
correction described below.
The new LWR ripple correction uses the same expression for the alpha
parameter as defined in Section 11.2.1 of the NEWSIPS manual. The blaze
wavelength for order m however is now defined as:
lambda_cm = (K / m) + Deltalambda(m,T)
where
K = A + Bm + Cm2 + Dm3 + Em4
and
A = 0.281749635D+06
B = -0.223565585D+04
C = 0.365319482D+02
D = -0.262477775D+00
E = 0.701464055D-03
The Deltalambda term is a time-dependent correction to the blaze
wavelength defined by the equation
Deltalambda(m,T) = a(m) + b(m)date + c(m)date^2
where date is the observation date as a fractional year (e.g., 1987.1), and
the coefficients are defined at the reference orders shown below.
Order a b c
115 35736.699D0 -35.970790D0 0.0090515542D0
111 62433.199D0 -62.859885D0 0.0158223297D0
107 53287.133D0 -53.638642D0 0.0134980459D0
103 42742.7090 -43.014583D0 0.0108219635D0
99 28040.843D0 -28.206111D0 0.0070930274D0
95 10463.439D0 -10.501169D0 0.0026347077D0
91 6223.1919D0 -6.2320255D0 0.0015601476D0
87 4478.2512D0 -4.4662128D0 0.0011134034D0
83 112.32014D0 -0.0566704D0 0.0D0
79 156.16074D0 -0.787367D0 0.0D0
In practice, the Delta-lambda term is calculated at the reference
orders for a given observation date and then linearly interpolated to the
desired order. Orders longward of order 115 use the correction for order
115, and similarly orders shortward of order 79 use the correction for
order 79.
The study by Cassatella [1] determined that the changes to the LWR ripple
correction did not affect any other calibrations (e.g., the absolute
calibration). Therefore, no other calibrations were modified.
To verify images have been processed using the correct ripple correction, a
line was added to the HISTORY portion of the primary FITS header. Images
processed with the original ripple correction will contain the comment: "LWR
RIPPLE CORRECTION VERSION 1.0 APPLIED". Reprocessed images will have the
comment: "LWR RIPPLE CORRECTION VERSION 2.0 APPLIED".
Note that because the LWR images had already been processed, the LWR ripple
correction was applied (at Goddard) using an IDL routine called MXCOR2. The
program basically read the net flux vector from the archived MXHI files,
re-applied the ripple and absolute flux calibrations, wrote the new
ripple-corrected and absolutely-calibrated net flux vectors back into the
MXHI file, and updated the comment in the HISTORY portion of the FITS
header. At Vilspa, the new correction was implemented in the NEWSIPS
processing software. Tests showed the two methods produced identical
results.
References
1 Cassatella, A. 'Ripple Correction and Absolute Calibration for LWR High
Resolution Spectra Processed with NEWSIPS', September 1997, internal
report.
III. High Dispersion Heliocentric Velocity Correction
Wavelengths for all high dispersion images except nulls and onboard
calibration lamp exposures (i.e., object classes 98 and 99) are routinely
reduced to a heliocentric frame of reference. The algorithm used in NEWSIPS
is basically the same as that used in IUESIPS and is based on programs
originally written by Howard Cohen and Arthur Young from Indiana University
and described by Harvel(1980) [1]. Some minor differences however do exist.
The velocity components of the earth and IUE in a righthanded rectangular
equatorial coordinate system (+x is toward the vernal equinox, +z is toward
the north celestial pole) are computed using the routines described in
Harvel (1980). The computed net radial velocity of the IUE spacecraft
toward the object is given by the expression:
V = V_xCos(alpha)Cos(delta) + V_ySin(alpha)Cos(delta) + V_zSin(delta)
where
V_x = V_x(Earth) + V_x(IUE)
V_y = V_y(Earth) + V_y(IUE)
V_z = V_z(Earth) + V_z(IUE)
and
alpha = right ascension of object,
delta = declination of object.
The corrected wavelengths are then defined as:
lambda_corrected = (1 + V/c)*lambda_uncorrected
where c is the speed of light.
The calculation is such that a net approach of the IUE spacecraft toward
the target requires a positive net radial velocity correction to the
heliocentric reference frame, following the standard convention. The
individual IUE and earth velocity components and the net radial velocity
correction used are documented in the image processing history portion of
the primary FITS header. Note that typically the correction for the earths
motion is roughly 30 km/sec while the correction for the spacecraft motion
is between plus or minus 3.1 km/sec.
One difference between the IUESIPS and NEWSIPS velocity corrections is in
the calculated observation times. The IUESIPS correction was based on an
estimate of the midpoint of observation which was calculated by subtracting
half of the estimated exposure time from the observation end time. This
estimate was known to introduce some errors though because the exposure
times stored in the IUESIPS label were occasionally incorrect. The NEWSIPS
velocity correction however is based on the observation start time! (This
may have been an inadvertent error since NEWSIPS also calculates the time
of the observation midpoint.) This can lead to a less accurate velocity
correction depending on the orientation of the spacecraft orbit with
respect to the direction of the target and the length of the exposure.
Presumably the error would be small, amounting to a NEWSIPS error of less
than 1-2 km/sec.
A second difference is that IUESIPS used a single set of spacecraft orbital
elements (obtained on November 22, 1979), while NEWSIPS uses the latest set
of orbital elements available before the observation was made. Because the
spacecraft's orbit gradually changed with time, updated orbital elements
were calculated every few weeks. This difference could cause an IUESIPS
error of up to 3 km/sec depending on the orientation of the satellite with
respect to the target and the observation date Taylor(1993) [2].
References
1 Harvel, C.A.,'IUE Data Reduction XVI. Orbital Velocity Corrections',
June, 1980, NASA IUE Newsletter, No. 10, pp. 32-36
2 Taylor, L.L.,'A Brief Discourse Concerning Velocity Corrections for
IUE Data', March, 1993, NASA IUE Newsletter, No. 50, pp.2-8