TECHNICAL
REPORT
Title: : STScI NIRSpec Calibration Pipeline Doc #:
Processing Description
Date:
Rev:
Authors: T. Beck
Phone: 410338-5038
JWST-STScI-001859, SM-12
3 September 2009
-
Release Date: 24 November 2009
1.0
Abstract
NIRSpec observations will be carried out in three main observing modes: the multi-object
spectroscopy (MOS) mode using the micro-shutter arrays (MSAs; referred to as MOS or
MSA mode), the fixed slit (FS) observing mode for single object spectroscopy, and threedimensional imaging spectroscopic observations made using the integral field unit (IFU).
NIRSpec data reduction and calibration will be complex, particularly for the MOS
observing mode where spectra are simultaneously acquired on 100+ targets at a time.
This document wholly supports and is consistent with the existing ESA NIRSpec data
calibration documents, but provides a bit more detail on how the NIRSpec data reduction
pipeline for all science modes shall be carried out. At the end of this document, we have
included a section on ‘open issues’, which highlights some issues related to the NIRSpec
data reduction and calibration that require further investigation. We plan to update this
document several times during the implementation of the data calibration pipeline, as the
detailed algorithms are defined for specific reduction steps and analysis methods are
determined more conclusively.
2.0
Introduction
In this document, we outline the processing steps for the reduction of NIRSpec data in all
modes. Two key companion documents that are referenced throughout the text are:
NIRSpec Calibration Plan, by De Marchi et al. Document ID = ESA-JWST-PL-2959
NIRSpec Science Data Pipeline Inputs and User Processing Requirements by De Marchi
et al. Document ID = ESA-JWST-RQ-2961 (Referred to as “Data Processing
Requirements” document in all subsequent text).
When taken in combination, these existing documents provide a very detailed description
of why the NIRSpec data reduction methods have been defined and adopted. This present
document supports and expands upon the Calibration Plan and Science Requirements
Operated by the Association of Universities for Research in Astronomy, Inc., for the National
Aeronautics and Space Administration under Contract NAS5-03127
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
JWST-STScI-001859
SM-12
documents, but goes into more detail and emphasizes how the data might be reduced in
the calibration pipeline. We do not outline why the proposed reduction methods were
adopted, and we also do not discuss general data reduction philosophies that might be
applicable to all JWST instruments.
NIRSpec data reduction and calibration will be complicated. This document discusses
the data reduction for the initial processing from data ramps to count rate images (section
3), MSA mode observations (section 4), fixed slit (FS) mode (section 5) and IFU mode
observations (section 6). We also discuss potential format and information included in
reference files (although final formats are TBD and should be discussed), we propose
some initial structures for the pipeline output data products, and outline what may be
needed for post-pipeline processing and analysis tools – e.g. “Next Level” reduction and
analysis. All steps in the data reduction process are laid out in detail in the MOS data
reduction section. For the FS and IFU modes, many of the processing steps are very
similar (or effectively identical) to the MOS data reduction. As a result, the steps for the
FS and IFU modes that are different than for MOS reduction are presented in detail, and
steps that are identical refer to the MOS section for the description included there.
Also included in this document is a description of data reduction steps for processing
NIRSpec “images” – these are frames acquired with the grating mirror in place, used for
target acquisition and source placement verification (section 7). Reduction of these data
will consist of general imaging mode processing, and will likely be similar to the
reduction of NIRCam or TFI images. Also described in section 8 are proposed “Next
Level” processing steps for NIRCam data, for images that have been acquired
specifically for NIRSpec pre-imaging purposes.
In this document, we identify the outputs from the NIRSpec data reduction and
calibration process. As discussed at the JWST Calibration summit meeting in March
2009, we call these outputs “Browse Quality” at this point in the pipeline description.
This phrase was adopted to describe the processing outputs from the early incarnation of
the reduction pipeline. Of course, the data pipeline and calibration process should output
optimal Science Quality data whenever possible. But we do note that in the early stages
when problems and bugs in the pipeline processing are being worked out, the outputs are
all likely to be of “Browse Quality”, not science and publication quality by most
standards. As the pipeline processing improves, we hope that the Browse outputs will be
replaced by properly calibrated science grade outputs.
Unless otherwise stated, it is assumed throughout this document that we are describing
the reduction process for sets of NIRSpec spectra that were acquired at the same time,
within a single visit. As the details of specific data processing algorithms are determined,
and as studies are done to clarify issues that are listed as “TBD”, this document will be
updated to a new version (e.g., see Section 9 on ‘Open Issues’).
3.0
NIRSpec Initial Processing Steps (CALWebb)
The JWST HAWAII-2 RG near-infrared detectors will all use similar processing steps to
reduce the raw detector MULTIACCUM datacubes (ramps) into count-rate images. See
the NIRSpec Operations Concept document (OCD) for further description of the raw
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
-2-
JWST-STScI-001859
SM-12
NIRSpec data. In this section, we describe the details of the reduction steps that will be
adopted to execute this initial data processing. This portion of the JWST data reduction
pipeline has (temporarily) been dubbed the “CALWebb” processing. Figure 1 presents
the data reduction flow chart diagram for the CALWebb pipeline, and the following
sections describe each step. At the time of the JWST multi-instrument data calibration
summit in early April 2009, the order of the processing steps in this flow chart was
consistent for all instruments (including MIRI). As we learn more about the
characteristics of the flight detectors, the order of the processing steps may shift and the
algorithms may vary – but the key goals of each task should not change appreciably. It
should be noted that the CALWebb pipeline will operate on data from each of the two
NIRSpec SCAs separately, and each SCA should have their own calibration reference
files and supporting pipeline information.
Figure 1: The data reduction flow-chart for the initial processing steps, from raw datacubes to countrate images ("CALWebb").
The first sub-section in the description of the CALWebb processing includes a note on
the raw data file structures that will be inputs into the data reduction and calibration
pipeline processing steps. The CALWebb flow chart and discussion for NIRSpec
presents two data processing steps which we hope will be adopted for the final reduction
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
-3-
JWST-STScI-001859
SM-12
process, but these tasks are not as well defined as others and they may not be included in
the flow charts for all of the near-infrared instruments. These two tasks are the Interpixel Capacitance (IPC) deconvolution, and the Latent Image Correction.
3.1
NIRSpec Raw Data File Structures
The .fits file format is the known file structure for all JWST data, and all data in a single
exposure from a single SCA should be stored in a single file (Kriss 2004). The separate
frames or groups that comprise the final exposure will be stored as image planes in a
three-dimensional datacube. In this case, the file size would be NROWS x NCOLUMNS
x NGROUPS, where NROWS and NCOLUMNS designate the readout pixel array size or
subarray size (in full-frame readout for all H2RG detectors, NROWS = NCOLUMNS =
2048 pixels). However, the further details of the JWST data format have not been
finalized yet. For example, for exposures that have multiple integrations in order to avoid
saturation on a bright target, the file size would be NROWS x NCOLUMNS x
NGROUPS x NINTS. So, the raw .fits file that is input into the pipeline may have a 4
dimensional structure with multiple datacubes for each integration in the exposure. It is
yet to be determined what the final exposure data structure will look like, and whether
pixel data in the image planes may be reordered as the recorded science data is
transformed into raw exposure data. In the following processing steps, we note where
information on subarray processing will be important (e.g., in the CALWebb and FS
processing). We have deferred the discussion of pipeline reduction of multiple
integrations in a single exposure (NINTS) to a future version of this document, once the
final JWST file structures are adopted for this observing strategy. This is included in
Section 9 on “open issues”.
3.2
Inter-pixel Capacitance Deconvolution
Description: The NIRSpec detectors have capacitive coupling of the pixels. The most
obvious effect of this is the cross-like or “+-shaped” pattern of charge around the “hot”
pixels. Effectively, the observed data array (A’) is the convolution of the true image
array (A) with the IPC kernel, (k) (McCullough 2008). Deconvolution of the observed
data array by the measured IPC kernel will allow for the extraction of the true image,
unaffected by this characteristic of the detector. McCullough (2008) describes a method
to execute this deconvolution for the WFC3 H1RG detector, and proposes that the
optimum correction is achieved if the devonvolution is performed on data in the first step
of the pipeline reduction.
Input Data: the raw, unprocessed datacube with dimensions of 2048x2048 x ngroups,
where ngroups is the number of up-the-ramp groups in the acquired data. (or nrows x
ncolumns x ngroups, when subarrays are used for FS science).
Input Reference Files: The IPC kernel, k. k is a 3 by 3 matrix described by the values α
and β, which quantify the capacitive coupling of the center pixel’s charge to each of its
adjacent neighbors along columns (α) or rows (β) (McCullough 2008). The four corner
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
-4-
JWST-STScI-001859
SM-12
elements of the IPC kernel are typically zero (or very close to zero), and the sum of all
elements is 1:
Example IPC deconvolution kernel,
0
β
0
k = α 1− 2α − 2β α
0
β
0
For most HAWAII 2RG detectors, α = β, and work on multiple detectors has shown that
α is very close to 0.015 or thereabouts. The IPC kernels will need to be determined for
both of the NIRSpec flight detectors,
and we must verify the nature of the IPC kernel –
€
that α=β and that the corner kernel components do equal zero.
Output Data: The IPC corrected datacube with dimensions 2048x2048 x ngroups. The
cross or “+” shapes around hot pixels are corrected and all subsequent data will
accurately reflect the true detector characteristics (such as for gain calculations).
NOTE: many aspects of the IPC deconvolution step need to be finalized before the
decision to implement this processing step should be made. These include: 1) Verifying
that the deconvolution of the IPC kernel improves the noise statistics and accuracy of the
data reduction, 2) verification of the IPC kernel shape, and whether it can be well
described by α and β parameters or if the full 3 x 3 matrix should be used for the
deconvolution, 3) How stable in time the IPC kernel is, 4) if uncertainties in the IPC
kernel are ever significant in the deconvolution process and 5) if deconvolution of the
IPC kernel should be the first processing step in the detector reduction, or if it would be
better placed elsewhere in the reduction flow.
3.3
Flag Bad Pixels
Description: This task will create the uncertainty and data quality (UNC and DQ) image
cubes and flag values from the static bad pixel mask file to the DQ image. This uses the
bad pixel mask reference file, which contains an image array for known bad (hot or cold)
pixels. The flag value may vary depending on the type of bad pixel (consistency in flag
values between JWST instruments is desired). There will be one bad pixel mask file for
each of the two NIRSpec detectors. Besides the truly “defective” bad pixels included in
the reference images, other bad pixels may be flagged. For example, pixels that are
saturated or have high flux levels and might show latency in the next accumulated
detector image should also be identified. Reference pixels can also be flagged as bad.
Input Data: The IPC corrected datacube of 2048x2048x ngroups dimensions (or nrows x
ncolumns x ngroups, when subarrays are used for FS science).
Input Reference Files: The bad pixel mask image for each NIRSpec detector with
dimensions 2048 x 2048, and a saturation reference image for each detector with
dimensions 2040 x 2040.
Output Data: The datacube of 2048x2048 x ngroups, with x 3 data extensions. The file
consists of the bad pixel corrected data image cube in the first extension, the Uncertainty
image cube (UNC) and the Data Quality datacube (DQ) (or nrows x ncolumns x ngroups,
when subarrays are used for FS science).
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
-5-
JWST-STScI-001859
SM-12
Output Reference File: When applicable, a second output of this procedure is a
2040x2040 image (or nrows x ncolumns x ngroups, when subarrays are used for FS
science), which flags the pixels that had high flux or were saturated (and perhaps records
the count value?). This image output would keep the record of latency for the next
detector image (see section 3.6). For this, the generic term of output Latency Map is
adopted.
3.4
Reference Pixel Correction
Description: A 4 pixel wide border of reference pixels surrounds the 2040x2040 light
sensitive pixels in the NIRSpec detectors. These pixels are used to correct slow bias
drift. The optimal method for correcting the bias offset sampled by the reference pixels is
TBD, most detector groups seem to do this correction in a different manner. The proper
method to be adopted for correcting reference pixels will be the topic of further study.
Input Data: The bad pixel masked datacube image of 2048x2048x ngroups dimension,
with corresponding UNC and DQ extensions. (or nrows x ncolumns x ngroups, when
subarrays are used for FS science).
Input Reference Files: None
Output Data: Reference pixel corrected datacube image of dimension 2040x2040x
ngroups, with corresponding and updated UNC and DQ extensions. (or nrows x
ncolumns x ngroups, when subarrays are used for FS science).
3.5
Linearity Correction
Description: Correct the up-the-ramp image datacube for the effects of detector nonlinear flux response.
Input Data: The reference pixel corrected datacube image of 2040x2040x ngroups
dimension, with corresponding UNC and DQ extensions. (or nrows x ncolumns x
ngroups, when subarrays are used for FS science).
Input Reference Information: This task requires coefficients for the linearity correction
equation for each of the two NIRSpec detectors (assumed to be a ~3rd order polynomial –
but flexibility should exist in case a different order polynomial is adopted). The input
might be a single set of reference coefficients for each of the two NIRSpec detectors. The
reference file should have a header parameter that specifies the function that should be
used to interpret the coefficients. At this time, only a polynomial transformation needs to
be implemented, but flexibility should exist in the format of the reference file in case the
means for correcting the linearity evolves.
Though it is presently thought unnecessary, it is TBD if multiple coefficients may be
needed - such as one set of coefficients for each of the 2040x2040 detector pixels. If
linearity coefficients are required for every pixel, then the input reference file structure
might be two input image array datacubes of dimension 2040x2040x~4 – where the first
two dimensions represent each pixel coordinate on the detector, and the third dimension
is the polynomial coefficients for the linearity correction.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
-6-
JWST-STScI-001859
SM-12
Output Data: linearized flux datacube image of dimension 2040x2040x ngroups, with
corresponding and updated UNC and DQ extensions (or nrows x ncolumns x ngroups,
when subarrays are used for FS science). The pipeline must apply the linearity correction
to both the measured pixel values and the corresponding uncertainties, recording the
result in an extension in the output file.
3.6
Dark Subtraction
Description: Correct the science image datacube for dark current by subtracting a
corresponding dark image cube.
Input Data: The linearized science image datacube of dimension 2040x2040 x ngroups,
with corresponding UNC and DQ image cubes (or nrows x ncolumns x ngroups, when
subarrays are used for FS science).
Input Reference Files: A dark image cube that has been processed through all previous
steps (IPC corrected, reference pixel subtracted, linearized). The current plan is for
acquisition of multiple NIRSpec detector dark images during long, ~10,600s parallel
observations using the NIRSpec NRSRAPID readmode (ngroups = 1000; e.g., see the
NIRSpec OCD). Thus the input dark image cube will likely be of dimension
2040x2040x1000. The region of the 3D dark datacube image used to correct the science
data can be extracted from this large dark cube. If the science was acquired in the NRS
or NRSSLOW read modes, which involve averaging or dropping frames in the readout
pattern, then the input dark datacube may need to be processed to result in the same data
structure and signal/noise characteristics.
Output Data: dark-subtracted science datacube image of dimension 2040x2040x
ngroups, with corresponding and updated UNC and DQ extensions (or nrows x ncolumns
x ngroups, when subarrays are used for FS science). Each dark rate in the dark image
cube has an associated uncertainty that will be propagated by the pipeline during
processing.
3.7
Image Latency Correction
Description: If the SCA was exposed to bright illumination in preceding science frames,
provide a first-order correction to the present science image datacube for latency effects.
To do this, an accurate knowledge of the NIRSpec detector pixel charge trap structure
and the exponential decay characteristics of the latency are needed. Additionally, the
existence of this task implies that there is a method in place to track images and access
the saturation and high flux maps for the data image acquired immediately prior to this
science data. The implementation of this correction could be tricky and complicated
(e.g., see Regan et al. 2009), and the details are TBD.
Input Data: The dark subtracted science datacube of dimension 2040x2040x ngroups,
with corresponding UNC and DQ extensions (or nrows x ncolumns x ngroups, when
subarrays are used for FS science).
Input Reference Files: A latency map of the saturated and high flux pixels flagged from
the previously acquired image or images is needed (i.e., output image from the bad pixel
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
-7-
JWST-STScI-001859
SM-12
correction step and/or linearity correction, sections 3.2 and 3.4). Additional information
on the exponential decay of the latency and a 2040x2040 image of the pixel charge trap
characteristics will also be needed for each detector for this correction to be applied.
(Details are TBD).
Output Data: A latency corrected science datacube image of dimension 2040x2040x
ngroups, with corresponding and updated UNC and DQ extensions (or nrows x ncolumns
x ngroups, when subarrays are used for FS science).
NOTE: A number of issues need to be figured out before this latency correction method
can be implemented. These include: 1) How easy or feasible is it going to be to track
latency from one image to the next? 2) Will implementation of this require a whole new
system of updatable calibration reference files for every exposure that is taken? 3) How
accurate is it to flag previously saturated pixels and attempt a latency correction without
knowing details of the charge trap maps for a detector? (e.g., derivation of charge trap
maps is not something that is being tested for the NIRSpec flight SCAs during DS testing
at Goddard). Many questions need to be ironed out before this can be adopted. In the
early operations of JWST, mitigation of the effects of saturation will likely be best done
with dithered observations.
3.8
Cosmic Ray Cleaning and Collapse to Rate Image
Description: Identify and remove hits by cosmic rays in the linearized, up-the-ramp
datacubes, and collapse the cube into a cleaned, 2-dimensional count rate image. Cosmic
rays can be flagged and removed as spurious outliers in the datacube by analyzing the
slopes of each of the pixel ramps. The slope of the data ramps before and after the cosmic
ray hit should be the same. The best estimate for the true ramp slope is found by
analyzing the data to determine the slope and y intercept in the intervals before and after
the cosmic ray hit and taking a weighted mean of the two fits. Fixen et al. (2000) and
Regan (2007) showed the benefits of using optimum weighting analysis based on the
signal-to-noise to determine the best slopes of the data ramps. The count rate images for
each detector are constructed using the best slope fit for each pixel, with the appropriate
factors for the detector gain and the group time used to scale the image to units of
electrons/second.
Input Data: The dark subtracted and (where applicable) latent corrected science datacube
image of 2040x2040x ngroups dimensions, with corresponding UNC and DQ extensions
(or nrows x ncolumns x ngroups, when subarrays are used for FS science).
Input Reference Information: Header keyword information on detector gain and group
time.
Output Data: The 2040x2040 2-dimensional count rate image in units of
electrons/second, with corresponding and updated UNC and DQ extensions (or nrows x
ncolumns, when subarrays are used for FS science).
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
-8-
JWST-STScI-001859
SM-12
4.0
NIRSpec MOS Data Reduction (MSA Mode)
As outlined in the NIRSpec Calibration Plan and Data Processing Requirements
documents, the data reduction for MSA mode observations is more complicated then IFU
or FS data reduction because the pipeline will need to rely on an instrument model to
correct for throughput including part of the flat field correction. The pixel-to-pixel flat
(P-flat) fielding is decoupled from the instrument model and the low frequency flat (Lflat) correction. At the present time, it is assumed that P-Flat correction is wavelength
insensitive, or only mildly dependent on wavelength. The MOS flat field data correction
is thus broken into multiple steps, the P-flat is applied to the full detector image and the
L-flat and throughput correction will be best done once the data have been extracted into
individual 2-D spectra for each open MSA shutter. Note, as described in the Data
Processing Requirements document, the character of the P-flat correction may be
wavelength dependent. The optimal placement of the P-flat correction step in the data
reduction pipeline is awaiting further information on the wavelength dependent character
of the P-flats from the NIRSpec flight detectors. If the P-flat is wavelength dependent,
then this correction will likely need to be incorporated in the throughput calibration in
some manner (this is TBD). NIRSpec MSA data will always be acquired in full-frame
readout, no subarrays will ever be used.
4.1
Data Combine
Description: Combine data images of targets that were acquired at the same nominal
position within the same MSA shutter. This would probably include combining data that
was acquired within the same visit only, though perhaps taken after multiple guide star
acquisitions (under the assumption that the NIRSpec target acquisition will need to be
repeated every ~10000 seconds because of the need to re-point the high gain antenna).
For data combination at this point in the reduction process, the number of input images
may be linked to specific and fixed dithering strategies that are defined by the GOs in the
APT.
Input Data: Collapsed count rate images of dimension 2040x2040, with corresponding
UNC and DQ extensions. Several images will be taken as inputs, depending on the
number to be combined.
Input Reference Files: Header keywords that verify the offset pattern may be needed.
Output Data: A single, combined output count rate image of dimension 2040x2040, with
corresponding and updated UNC and DQ extensions.
NOTE: It will be important to have the option to combine spectra prior to the flat fielding
steps. However, not all data will be acquired in a manner which allows for data
combination at this early stage in the pipeline processing. It is still TBD if data
combining at this point should be somehow merged into an automated pipeline, or if this
should only be an option available to users who want to re-process their data in this
manner.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
-9-
JWST-STScI-001859
SM-12
4.2
Background Subtract
Description: Subtract the background flux from a MOS science target spectrum using a
background spectrum that was taken nearby in time and through the exact same MSA slit.
Subtracting background in this early stage of the reduction is a ‘ground-based’ observing
bias to remove high sky flux background prior to the flat fielding step. While we do not
have the worry of high and variable sky flux, background subtraction may be appropriate
here for bright NIRSpec targets that are photon noise dominated. The optimal
implementation of background subtraction for the faintest targets may depend on detector
characteristics such as correlated noise on spatial scales that span several MSA slits. It is
possible that the signal-to-noise on very faint sources can be improved if background
subtraction is done at a later point in the reduction, using background spectra that were
acquired nearby but not in the same MSA slit as the science target. Further work is TBD
for optimal MOS background subtraction strategies.
Input Data: A count rate file of the science target image (or list of target image files) that
has dimension 2040x2040 with UNC and DQ extensions, with its corresponding
background file (or list of background files). For background subtraction at this point in
the data reduction process, the number of input images and the number and ordering of
the corresponding background images will likely be linked to specific and fixed dithering
strategies defined by the GO in the APT. (Note that an image file that has a target in one
shutter may have background for a different shutter. Hence, a given .fits file may appear
at different positions in both the target image list and the background image list,
depending on the ordering of the dither pattern).
Input Reference Information: Header keyword information on dither pattern sequence
and possibly keyword information propagated from the APT on whether shutters contain
a target or are background.
Output Data: Background-subtracted count rate image of dimension 2040x2040, with
corresponding and updated UNC and DQ extensions. After this processing step, the
MSA slits which have positive target spectra will be processed further by the pipeline.
The slits that were for background should not be processed, because the data are likely
affected by the one-to-one image subtraction procedure.
NOTE: It will be important to have the option to subtract background from spectra prior
to the flat fielding steps. However, not all data will be acquired in a manner which
allows for background subtraction at this early stage in the pipeline processing. It is still
TBD if background subtraction at this point should be somehow merged into an
automated pipeline, or if this should only be an option available to users who want to reprocess their data in this manner.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 10 -
JWST-STScI-001859
SM-12
Figure 2: The flow-chart diagrams for MSA Mode Data Reduction
4.3
P-Flat Correction
Description: Correct the NIRSpec full-frame detector data for pixel-to-pixel flat field
variations. It is presently assumed that the pixel-to-pixel flat field variations of the
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 11 -
JWST-STScI-001859
SM-12
NIRSpec flight detectors will generally be wavelength insensitive, or only a weak
function of wavelength. As a result of this assumption, the P-flat correction should be
applied directly to the full-frame images to remove sensitivity variations of the detector
over small scales.
Input Data: The NIRSpec count rate science images of size 2040x2040 pixels, with
corresponding UNC and DQ extensions.
Input Reference Files: The NIRSpec P-Flat images of size 2040x2040 pixels, with
corresponding UNC and DQ extensions.
Output Data: P-flat corrected NIRSpec science images of size 2040x2040 pixels, with
corresponding UNC and DQ extensions.
4.4
2-D Slit Extraction
Description: Extract full frame images into 2-D data sub-image (windows), with one
MSA slit spectrum per extracted 2-D data sub-image (See Figure 3).
While it is still TBD how and when the extraction of individual MSA slit spectra into 2-D
sub-windows will be handled in the NIRSpec pipeline, we emphasize that extraction of
the MSA spectra into smaller slit spectra is very important. This will allow for easier
processing on each individual spectrum, and the manner of processing the 2-D extracted
data can be identical to long-slit data processing for many of the subsequent steps. The
extracted window will encompass all data from the main centered slit, but it may also
contain small regions of spectra from slits near to the target slit.
If the data already had background subtraction executed in the previous steps, then only
the slits with target data should be extracted into smaller 2-D image sub-windows. If
background subtraction has not yet been done, both the target and background slit data
should be extracted. Each MSA shutter will have associated x & y pixel coordinates
which map out the spectral extraction box. For each grating+filter combination, every
MSA shutter will map to its own unique 2-D image extraction sub-window.
Input Data: P-Flat corrected data image of 2040x2040 size, with corresponding UNC and
DQ extensions.
Input Reference Files and Reference Information: The reference file and reference
information will need to define the extraction box location pixel coordinates. To define
the extraction box pixel coordinates, a reference file for each MSA quadrant might be
used (as described below). A set of polynomial equations might be used to generate the x
and y pixel positions of the extraction box. If the polynomial approach is adopted, it
would be assumed that the reference information would include coefficients for the
polynomial calculation, and the x and y pixel extraction box locations would be
calculated by the pipeline for every MSA shutter to be extracted. (Tracy’s note: For the
record, I don’t like the polynomial approach, because it means that there is the
possibility that x and y pixel locations might be extracted differently on different
computer platforms – e.g., reducing data on one machine w/ a flat field generated on a
different machine might not work if pixel values are calculated differently because of
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 12 -
JWST-STScI-001859
SM-12
computer or platform settings. This also implies a whole lot of redundant calculations
for multiple observations through an identical MSA configuration).
The exact format of the pixel extraction reference information is TBD, but below we
describe a file format structure that could be used.
An example of the reference file structure for MSA shutter spectrum extraction might be:
file with four image extensions that are image array datacubes of real numbers, with
dimensions of 365x171x4. Each of the datacube arrays map out the spectral extraction
boxes for the MSA quadrants. The 365x171 dimensions of these reference datacubes
correspond to the dimensions of the MSA quadrants. The third dimension of the
datacube is the pixel values for the extraction box size. Because there are for MSA
quadrants in NIRSpec, a separate MSA extraction reference file of this nature would be
needed for each MSA quadrant.
Four pixel values are needed to define the pixel coordinate image extraction locations.
For example, Figure 3 presents the extraction box location for an MSA slit spectra, with
xα, yα as the pixel coordinates of the lower left extraction location, and xβ, yβ as the
pixel coordinates of the upper right of the box. The two pixel locations, xα, yα and xβ
yβ define the full extraction sub-image location, and make up the four planes of the z
dimension of the data image:
Reference Image, x dim = n, ydim = m and z dimension plane 1:
xα n,m
m,1→171
n,1→365
Reference Image, x dim = n, ydim = m and z dimension plane 2:
€€€
€
€
€
yα n,m
m,1→171
n,1→365
Reference Image, x dim = n, ydim = m and z dimension plane 3:
x β n,m
m,1→171
n,1→365
Reference Image, x dim = n, ydim = m and z dimension plane 4:
yβ n,m
m,1→171
n,1→365
One reference pixel extraction datacube will be needed for each of the four MSA
quadrants, and these are included in the reference file as the four datacube extensions.
Additionally, reference datacube files are needed for each of the spectral configurations,
for a total of 9 reference files, 36 datacube extensions (one extension for each of the four
MSA quadrants for the prism, G140M+F070LP, G140H+F070LP, G140M+F100LP,
G140H+F100LP, G235M, G235H, G395M and G395H spectral modes). The reference
file must be able to distinguish between pixel locations on the two NIRSpec detectors
(spectra in the R=2700 mode will extend over both detectors).
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 13 -
JWST-STScI-001859
SM-12
Figure 3: A Figure presenting the pixel coordinates necessary to define the boundaries of the 2-D
sub-image extraction boxes - xα, yα, xβ and yβ.
This task will ultimately need access to either header keyword information or calibrated
sky coordinates propagated from the APT that defines which shutters contain a target and
which are background.
Output Data: Multiple 2-dimensional spectral slices that are x_spec x y_spatial in size,
where the x dimension is the spectral dispersion dimension (x_spec) and y is the pixel
length in the cross dispersion direction that the MSA spectra extends over (y_spatial).
Because of distortion and spectral curvature, the y_spatial dimension of each 2-D
extraction window will be larger than the ~4 pixel length of the undispersed images of
the MSA slits. The x_spec dimension depends upon the grating used and the wavelength
region being sampled. For further discussion, these extracted 2D spectral images are
referred to as sub-images.
The 2-dimensional spectral sub-images may be organized into a single data file using
multiple extensions. The number of science extensions will depend on whether the target
data only is extracted (# of science extensions = # of MSA targets), or whether the target
and background slits are both extracted (# of science extensions = # of open MSA slits).
Each extracted science spectral data sub-image should also have its associated extracted
UNC and DQ extensions.
4.5
Complete Throughput and L-Flat Correction
Description: Correct the spectra for throughput and low frequency flat field variation,
including all effects from the transmission of the optics and a default chromatic slit-loss
correction. If the P-flat does turn out to be a slowly varying function of wavelength, then
this P-flat wave dependence should also be merged into this correction.
Input Data: The extracted 2-d sub-images of dimension x_spec x y_spatial, with
corresponding UNC and DQ planes.
Input Reference Files: The L-flat and throughput reference data cube, which will likely
be an image datacube that includes all correction components for throughput and the lowfrequency flat. This reference image will be constructed from the instrument model
initially, but will be verified by on-ground and in-orbit spectral flats acquired with the
NIRSpec CAA flat field lamp and may possibly be replaced by these empirical flats, as
the catalog of observed MSA slit-flat fields is built up over time. This image datacube
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 14 -
JWST-STScI-001859
SM-12
will consist of 2-d throughput and flat correction model spectra for each slit in each of the
four MSA quadrants. The default chromatic slit-loss correction will be based on a pointsource centered in each slit, and may be factored into the throughput values for the
reference datacube. More work is TBD to determine the precise nature and format of the
reference datacube, and supporting input reference values.
Output Data: The L-flat corrected, extracted 2-d sub-images of dimension x_spec x
y_spatial, with corresponding UNC and DQ planes.
4.6
Initial Wavelength and Spatial Calibration
Description: Use the instrument model to provide the initial wavelength and spatial
calibration for each of the spectral sub-images. This task, in practice, will likely only
consist of the addition of calibration keys that designate the initial spatial and spectral
calibration reference values. As a result, this initial calibration will be flexible and could
be done prior to (or in conjunction with) the L-flat correction step.
Input Data: The L-flat corrected, extracted 2-d sub-images of dimension x_spec x
y_spatial, with corresponding UNC and DQ planes.
Input Reference Files: Reference model equations which link the open slit in the MSA
quadrant to the extracted data from the detector. Based on the spectral configuration, the
NIRSpec instrument model will provide the approximate spectral and spatial calibration,
including distortion and spectral curvature. The structure of the input model files is TBD.
Output Data: The L-flat corrected, extracted 2-d sub-images of dimension x_spec x
y_spatial, with spatial and spectral calibration added and corresponding UNC and DQ
planes.
4.7
Final Wavelength and Spatial Rectification
Description: Determine and apply the final wavelength and spatial rectification to the
spectral sub-images. Empirical on-sky spectra and NIRSpec lamp wave calibration
images will be acquired to verify the accuracy of the initial wavelength and spatial
rectification processes, and can be used to generate correction factors to the initial
calibration. The final rectification process will consist of merging the spectra onto a
regular pixel grid, and removing the spatial and spectral curvature from the data (See
Data Processing Requirements document for further discussion).
As described in the Data Processing Requirements document, during this step in the
reduction process it may be desirable (or necessary) to combine spectral sub-images that
were acquired in a fixed offset pattern. To do this, offset spectral data would be
interpolated onto a finer grid and merged in combination with the wavelength and spatial
rectification process, in a type of drizzle combination/rectification. This will require
further development and may be hard-coded to be applied only if a fixed or associated
offset pattern was used to acquire the data.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 15 -
JWST-STScI-001859
SM-12
Input Data: The L-flat corrected, extracted 2-d sub-images of dimension x_spec x
y_spatial, with spatial and spectral calibration keywords added and corresponding UNC
and DQ extensions.
Input Reference Files: Rectification coefficients and transformation information derived
from the instrument model and verified or corrected using empirical on-sky and lamp
wave calibration data. The structure of these reference inputs is TBD, but it should
contain: (a) physical parameters for each grating, (b) coefficients of a two-dimensional
polynomial describing MSA-to-detector distortion for all gratings, (c) coordinates
describing the detector geometry, and (d) empirical corrections, if necessary .
Output Data: The spatially and spectrally rectified 2-d sub-images, with spatial and
spectral calibration keywords added and corresponding UNC and DQ extensions. The
final dimensions of the 2-D sub-images after the rectification process will be something
other (larger) than the input x_spec x y_spatial size (TBD).
Note: There are some science applications that will not wish to interpolate the MSA
spectra onto a rectified spatial and spectral grid. For this reason, the following
reduction steps should be constructed to work with data that have not been rectified onto
a regular pixel grid (with the possible exception of the data combine and background
subtraction steps, which may not work well on un-rectified data).
4.8
Data Combine
Description: Combine 2-D MSA spectral sub-images that have been rectified onto a
regular pixel grid. The input spectra for combining need not have been acquired through
the same slit. The initial accuracy and ease of pipeline implementation for this data
combination step may require that the spectra were obtained in a fixed offset pattern so
that merging of the spectra can be done in an automated fashion (Tumlinson 2009a).
Ultimately, it is desirable to merge all target spectra together in the pipeline, regardless of
the offset pattern. This will likely require a sophisticated means to track the target
through any MSA slit position in a user-defined offset sequence. The data combination
described here can be executed on either a target or background spectral sub-image.
Input Data: Multiple 2-D spectral sub-images that have been rectified onto a regular
spatial and spectral pixel grid, with corresponding UNC and DQ extensions.
Input Reference Info.: Header keyword information on the spatial and spectral
calibration and offset pattern will be needed for automated data combination.
Output Data: A single, merged and combined 2-D spectral sub-image with
corresponding UNC and DQ extensions.
4.9
Background Subtract
Description: Subtract the background flux off of 2-D sub-image spectra acquired through
different MSA slits, if background flux has not yet been subtracted (i.e., in step 4.2). The
background slit spectra used for this subtraction should be very nearby to the target slit
spectra because the optical distortion through the NIRSpec field causes slits in different
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 16 -
JWST-STScI-001859
SM-12
regions of the MSA quadrants to sample different areas on the sky. For ease of initial
implementation, this background subtraction will likely be linked to specific offset
sequence patterns selected by the user. As in the previous step for data combination,
user-selected offset sequences will likely require a sophisticated means to track targets
and identify corresponding background spectra. This is desirable, but will not be a goal
of the initial pipeline implementation.
The input spectra can be rectified onto a regular spectral and spatial pixel grid, or
possibly un-rectified if the spectra were acquired through adjacent slits (?? TBD??).
Depending on the science philosophy, the target and background spectra may have been
combined in the previous step. Subtraction of background using un-rectified spectra
acquired in a slit adjacent to the science target could be important for removing the
effects of correlated detector noise from the data.
Input Data: Multiple rectified (or unrectified) 2-D spectral sub-images with
corresponding UNC and DQ extensions. If the target spectra were rectified onto a
regular pixel grid, then the background spectra must be rectified also.
Input Reference Info.: Header keyword information on offset pattern and/or background
spectra location will be needed for automated data background subtraction.
Output Data: Background subtracted 2-D spectral sub-images with corresponding UNC
and DQ extensions.
4.10 Aperture Flux Correction
Description: Apply a default flux correction to the spectra for the effects of un-centered
targets within the slits. Nearly all targets observed through the MSA will have slit loss
effects caused by improper centering, because of the nature of using the fixed MSA grid
when observing multiple targets. As described in the Data Processing Requirements
document, the spectra must be corrected for these effects of slit-loss. This default
correction assumes a point source PSF flux distribution.
Input Data: The 2-D spectral sub-image (may be rectified or un-rectified) with
corresponding UNC and DQ extensions.
Input Reference Info.: Every target observed through the MSA must have associated
reference information on the centering position of the target observed through the slit.
This information is captured by the APT in the proposal planning process, and will be
useful information to have propagated within supporting visit meta-data and information
propagated to the DMS regarding the target/background/MSA configurations and
positions. The centering info. for the aperture flux correction will likely need to come
from meta-data information, associated with the APT MSA reference and slit definition
that was captured during the MSA planning process. The magnitude of the aperture flux
correction as a function of wavelength will depend upon the target centering. (Ideally,
we’d like for the aperture flux correction to also capture information on the source PSF
shape and positioning based on the NIRCam pre-imaging. But this is likely a higher level
pipeline goal).
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 17 -
JWST-STScI-001859
SM-12
Output Data: The 2-D spectral sub-image (may be rectified or un-rectified) that has been
corrected for aperture flux losses, with corresponding UNC and DQ extensions.
4.11 Absolute Flux Calibration
Description: Apply the calibration which translates the count rate image units of e-/sec
to flux calibrated data units. This will be a direct multiplicative factor applied uniformly
to each pixel in the spectral images. The final units of the NIRSpec pipeline output data
are TBD.
Input Data: The 2-D spectral sub-image (may be rectified or un-rectified) that has been
corrected for aperture flux losses, with corresponding UNC and DQ extensions.
Input Reference Info.: Input flux calibration information, likely in the form of a
photometric calibration header keyword determined from prior calibration observations.
Output Data: The flux calibrated 2-D spectral sub-image (may be rectified or unrectified), with corresponding UNC and DQ extensions. This is a Browse Quality Data
Output.
4.12 1-D Spectral Extraction
Description: Extract the 2-D spectral sub-images into 1-D spectra, using either an
optimal extraction method, and/or a straight collapse of spectra in the cross dispersion
dimension. Ideally, a weighted extraction assuming the source profile (from pre-imaging,
or the spectrally collapsed PSF) will likely provide a better result than a collapse of the
spectrum.
Input Data: The flux calibrated 2-D spectral sub-image (may be rectified or un-rectified),
with corresponding UNC and DQ extensions.
Input Reference Files: None.
Output Data: The flux calibrated 1-D spectra (may be rectified or un-rectified), with
corresponding UNC and DQ extensions. This is a Browse Quality Data Output.
4.13 1-D Spectral Data Combine
Description: At the end of the full reduction process, it will be possible to do an
automated combination of multiple spectra acquired on the same target through many
different MSA slits. Data combination at this point will be useful to increase the signalto-noise to verify the spectra, particularly for very faint sources. In practice, it should
also be possible to combine 1-D spectra acquired on the same MSA target that was
observed with different MSA configurations, different pointings, or different visits.
Executing data combination on many MSA target spectra acquired in different pointings
or visits will require a method to trace targets through different data associations within
an observing program.
Input Data: The flux calibrated 1-D spectra (may be rectified or un-rectified), with
corresponding UNC and DQ extensions.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 18 -
JWST-STScI-001859
SM-12
Input Reference Files: Reference header information on target name and spectral
calibration is needed for 1-D data combination. Further information on target data
associations will be necessary to combine data acquired in different pointings or visits.
Output Data: The combined, flux calibrated 1-D spectra (may be rectified or unrectified), with corresponding UNC and DQ extensions. This is a Browse Quality Data
Output.
4.14 ‘Browse Quality’ Data Products
As described in the previous section and presented in Figure 2, the main “browse quality”
data outputs for NIRSpec MOS observations are:
• The flux calibrated 2-D spectral sub-images for each MSA target, with UNC and
DQ extensions.
• The collapsed/extracted 1-D spectra for each target, with UNC and DQ
extensions.
• The combined 1-D spectra for a target acquired over multiple offsets, with UNC
and DQ extensions (and perhaps multiple pointings or visits).
These “browse quality” data outputs from the pipeline may fulfill the general observers
science requirement and thus could be used directly for published results. However, in
the early stages of the pipeline implementation, the “Browse Qualtiy” data outputs might
not meet the NIRSpec or user requirements, and further processing may be warranted. In
the NIRSpec MSA pipeline processing early in the JWST mission lifetime, it is likely
that more manual interaction with the data processing steps is necessary, and the GO
could re-run the data reduction. The hope is that the Browse Quality outputs will be
replaced by better calibrated science quality outputs as we learn more about the data
reduction and calibration process. These outputs would serve as the viewable result of
calibrated data within the JWST science archive.
4.15 “Next Level” Reduction
The primary challenge for “next level” NIRSpec data reduction in MOS mode is applying
an aperture flux correction to spectra which will accurately take into account the light lost
through the slit as a function of wavelength and source PSF shape (see the Data
Processing Requirements document for further discussion and description). Work by the
NIRSpec Science Team has shown that the difference between an aperture flux correction
assuming a point-source PSF and assuming a r1/4 deVaucouleurs galaxy surface
brightness profile can be as large as 1 magnitude at some wavelengths. Since many
NIRSpec science targets will not be point-sources, we know that a reduction routine or
post-pipeline tool will be necessary in order to apply the proper aperture flux correction
to all NIRSpec slit spectroscopy. To help define target profile shape, perhaps this task or
tool will include the NIRSpec ‘Confirmation Image’ that is acquired during MSA target
acquisition, or even the NIRCam pre-image used to define the target positions. A future
goal should be to incorporate a proper aperture slit correction based on target profile
shape into the automated MSA data reduction pipeline. Further work is TBD to define
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 19 -
JWST-STScI-001859
SM-12
and implement this (and it will most likely not be automated in the early part of the
mission).
An additional challenge for “next level” MOS mode reduction is tracking and combining
spectra of the same target taken through different MSA slits or at different times (visits or
pointings). As such, the ‘data combine’ steps for 2-D sub-image and 1-D spectra in the
MOS data pipeline flow chart should be able to take inputs from multiple observations of
a given target. The complexity of this next level reduction arises from the need to track
and locate all target spectra to be combined within all the visits in a science program.
This will likely require that a unique target name and/or on-sky coordinates are linked to
the open MSA target slits and propagated through the APT and the data headers. So, the
name or coordinates must be linked to each target slit at all pointings and visits (e.g., as in
the MSA header information which includes target placement within a slit). Further work
is TBD to better define this. (In HST-speak, I think this section would be called
“CALNIRSPECB”).
5.0
NIRSpec Fixed Slit Data Reduction
Figure 4 presents the flow chart diagram for data reduction in the NIRSpec fixed slit
mode. In practice, the FS data reduction is identical to the MSA data reduction, with the
exception of the throughput and flat fielding correction steps. There are a set number of
fixed slits, and because they are always open flat fields can be acquired simultaneously
with MSA flats for each of the FSs for every spectral configuration. As a result, the flat
field and throughput correction can be done in a single processing step using an empirical
flat field image, without decoupling the P-Flat for the full-frame images (as for MSA
mode).
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 20 -
JWST-STScI-001859
SM-12
Figure 4: The flow-chart diagram for NIRSpec fixed slit data reduction
The NIRSpec FS data reduction pipeline, which processes the data using an empirical
flat, should be constructed to work with MSA slit spectra. If a flat field spectrum is
acquired through an MSA slit, then the MSA science data obtained through the same slit
should be reduceable through the FS data pipeline using the corresponding flat field.
Sections 5.1 and 5.2 describe briefly the slit sub-image extraction and flat field and
throughput correction processing step for FS data reduction, all other steps included in
the flow chart diagram in Figure 4 are identical to the MSA reductions (from section 4).
Discussion of the details of these steps is not repeated here. Some FS data may be
acquired using subarrays defined for their associated slits. So, in these cases extraction of
the data into 2-D windows is not necessary.
Because FS data is acquired for all observing programs in all modes (even if the slits are
on blank sky), every NIRSpec science exposure should have the associated FS data
processed through the reduction pipeline. At the present time, it is assumed that the
pipeline data processing of spectra acquired through the 1.”6 x 1.”6 square wide aperture
is the same as FS data reduction. In practice many datasets from this wide aperture may
need to be processed in a different manner, because the data can be acquired in small
subarrays with no reference pixels. The pipeline processing for this data is TBD.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 21 -
JWST-STScI-001859
SM-12
5.1
2-D Slit Extraction
Description: Extract FS spectra into smaller 2-D sub-images, one sub-image associated
with each FS. This processing step is only necessary for FS data that was acquired in full
frame or “ALLSLITS” mode, not for spectra taken with a subarray associated with the
specific slit.
Input Data: The count rate image (combined from multiple frames, where applicable) of
2040 x 2040 size, with corresponding UNC and DQ extensions (or with 256 x 2040 size,
for data taken in ALLSLITS mode).
Input Reference Files and Reference Information: A table of real numbers (or
array/image, for consistency with MOS reduction) with dimensions of 5x4 that maps out
the spectral extraction boxes for each of the five FSs. The first dimension of this array
corresponds to the five FSs, the second dimension are the pixel values of the extraction
box. As described in section 4.4, four pixel values are needed to define the pixel
coordinate image extraction locations for each slit (Figure 3). One reference file will be
necessary for each spectral configuration, for a total of 9 files (see section 4.4). The
reference file must be able to distinguish between pixel locations on the two NIRSpec
detectors (spectra in the R=2700 mode will extend over both detectors).
Output Data: 2-D spectral slice images that are x_spec x y_spatial in size, where the x
dimension is the spectral dispersion dimension (x_spec) and y is the length in the cross
dispersion direction that the MSA spectra extends over (y_spatial). This processing step
will output 5 spectral sub-images, one for each of the FSs, with associated UNC and DQ
extensions.
5.2
Flat Field and Throughput Correction
Description: Correct FS (or MSA) spectra for flat field and throughput effects using
empirical flat images acquired with the NIRSpec CAA lamps.
Input Data: The 2-D extracted sub-image associated with each FS (or MSA slit), with
corresponding UNC and DQ extensions.
Input Reference Files: The calibrated and throughput corrected 2-D extracted subwindow flat field image associated with each FS (or MSA slit), with corresponding UNC
and DQ extensions.
Output Data: The flat and throughput corrected 2-D extracted sub-image associated with
each FS, with corresponding UNC and DQ extensions.
5.3
‘Browse Quality’ Data Products
The FS data reduction output “browse quality” data products will be the same as the data
products for MOS mode:
• The flux calibrated 2-D spectral sub-images for each FS target, with UNC and DQ
extensions.
• The collapsed/extracted 1-D spectra for each target, with UNC and DQ
extensions.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 22 -
JWST-STScI-001859
SM-12
The combined 1-D spectra for a target acquired over multiple offsets, with UNC
and DQ extensions.
Every NIRSpec observation will have associated FS pipeline data products.
•
5.4
“Next Level” Reduction
As for MOS mode observations, FS spectra may be acquired on targets in multiple
pointings or over multiple visits. Hence, for combining multiple spectra in a “next level”
reduction, the method in which target names are associated with MSA slit spectra and
tracked through a general observing program will also be applicable to FS observations.
(In HST-speak, I think this section would be called “CALNIRSPECB”).
6.0
NIRSpec IFU Data Reduction
Figure 5 presents the flow chart diagram for data reduction in the NIRSpec integral field
unit (IFU) mode. In practice, the initial processing steps for IFU data reduction are
identical to the FS data reduction, with the 2-D image extraction process creating subimages for the 30 IFU virtual slits instead of the 5 FSs. The flat field and throughput
correction for the IFU data will be the same as the FS mode, using flat images acquired
with the NIRSpec CAA lamps. The IFU reduction deviates from the FS process only
after the wavelength calibration step. The below sections describe only the extraction of
the IFU virtual slits into sub-images, and the reduction steps after the wave and spatial
calibration which construct and work with the 3-D IFU datacube files.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 23 -
JWST-STScI-001859
SM-12
Figure 5: The flow-chart diagram for NIRSpec integral field spectroscopy data reduction.
6.1
2-D Slit Extraction
Description: Extract IFU spectra into smaller 2-D sub-images, one sub-image associated
with each of the IFU virtual slits.
Input Data: The IFU data count rate image (combined from multiple frames, where
applicable) of 2040x2040 size, with corresponding UNC and DQ extensions.
Input Reference Files and Reference Information: A table of real numbers (or
array/image, for consistency with MOS reduction) with dimensions of 30x4 that maps out
the spectral extraction boxes for each of the thirty IFU virtual slits. The first dimension
of this array corresponds to the IFU slits, the second dimension are the pixel values of the
extraction box. As described in section 4.4, four pixel values are needed to define the
pixel coordinate image extraction locations for each slit (Figure 3). One reference file
will be necessary for each spectral configuration, for a total of 9 files (see section 4.4).
The reference file must be able to distinguish between pixel locations on the two
NIRSpec detectors (spectra in the R=2700 mode will extend over both detectors).
Output Data: 2-D spectral slice images that are x_spec x y_spatial in size, where the x
dimension is the spectral dispersion dimension (x_spec) and y is the length in the cross
dispersion direction that the IFU spectra extends over (y_spatial). This processing step
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 24 -
JWST-STScI-001859
SM-12
will output 30 spectral sub-images (extensions), one for each of the IFU slits, with
associated UNC and DQ extensions.
6.2
Reformat the Data into a 3-D cube
Description: Reformat the wavelength calibrated, spatially rectified IFU virtual slit
spectra into a 3-D datacube with dimensions 30 x ~30 x spec. The x dimension
corresponds to the 30 virtual slits, and the z dimension is the extent of the spectra. The y
dimension is ~30 because the slit extraction and spatial rectification process in the crossdispersion direction may alter the spatial dimension by a small amount.
Input Data: Wavelength and spatially calibrated IFU virtual slit spectra, with 30 subimage science extensions and corresponding UNC and DQ extensions.
Input Reference Files: None. (For an instrument-specific IFU cubing routine, no
reference information is necessary. The way the slits map to the sky to make the 3-D
datacube can be hard-coded in the routine. For a flexible IFU cubing routing, such as one
that might also work for MIRI data, a reference file that describes the mapping of the
virtual slits to the sky would be needed).
Output Data: A 3-D IFU datacube, x and y being the spatial axes and z the spectral
dimension, with corresponding UNC and DQ extensions.
6.3
Absolute Flux Calibration
The absolute flux calibration of IFU data is essentially identical to the MSA and FS
absolute calibration step, consisting only of a multiplicative calibration factor applied to
the count-rate data. Some users may wish to work with IFU data that has not been
processed through the 3-D cube stage. As such, the absolute flux calibration task should
be able to process the IFU data outputs from the wavelength and spatial rectification step
(prior to constructing the 3-D datacube). Browse Quality Data Output.
6.4
Combine Datacubes
Description: Combine datacubes that were acquired at a given pointing position (small
offsets). The IFU dither patterns at a given position have been defined so that targets can
be moved within the IFU image field to remove detector artifacts (Tumlinson 2009b). As
such, many IFU observations will be executed using these small, in-field dithers. IFU
data acquired using these “canned” dither patterns could be combined in the automated
pipeline to form a datacube that is free of detector artifacts.
Input Data: A set of multiple 3-D IFU datacubes acquired using a default dither pattern,
with corresponding UNC and DQ extensions.
Input Reference Info.: Header keyword information that describes the offset pattern used
to acquire the IFU data.
Output Data: A single combined IFU datacube that has slightly wider spatial extent than
the input cubes (depending on the offset pattern), with corresponding UNC and DQ
extensions. Browse Quality Data Output.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 25 -
JWST-STScI-001859
SM-12
6.5
‘Browse Quality’ Data Products
The “browse quality” data products of the IFU data reduction pipeline will be an absolute
flux calibrated datacube of size 30 x ~30 x spec, and (where applicable) a dither
combined, calibrated datacube of slightly greater spatial extent.
6.6
“Next Level” Reduction
The NIRSpec IFU component in APT will provide the option to construct wider-field
mosaics consisting of many IFU tile positions (Tumlinson 2009b). Additionally, the
option will exist to dither the IFU by integer slice widths, or use sub-slice offsets in the
spatial or dispersion dimension. The former offsets will be used to remove instrument
and pixel defects, and the latter dithering strategy will improve the spatial PSF and
spectral LSF sampling. As a result, “next level” reduction will be needed to combine
dithered IFU patterns and mosaic IFU datacubes together to form a larger spatial extent
datacube.
Most IFU mosaics will probably be contained within a single visit, with spatial extents of
<20” (same guide star). Though, it is possible that tiles for very large or very deep IFU
mosaics must be acquired using different guide stars, in different visits, or at different
times. Associating and tracking IFU data acquired through multiple visits may be
important for large IFU mosaics. (In HST-speak, I think this section would be called
“CALNIRSPECB”).
7.0
NIRSpec “Imaging” Mode
NIRSpec “Imaging Mode” data will be acquired using the grating mirror and one of the
filters used for target acquisition. Observations in this mode will be important for
measuring field distortion and PSF shape during the commissioning of the instrument,
and for monitoring instrument performance. During the TA process, a flat field lamp
image is acquired using the NIRSpec CAA to verify the position of the grating mirror, a
cosmic-ray rejected image of the science field is acquired through the TA filter with the
MSA shutters open. An optional ‘Confirmation Image’ is observed using the science
filter and the science MSA configuration, to confirm the science target placings within
the MSA shutters. While it is not assumed that NIRSpec “Imaging Mode” observations
will be used extensively for science, images acquired during the standard MSA target
acquisition process will prove useful for target position verification and user-interactive
aperture flux corrections. As a result, it will be important to provide accurately reduced
and calibrated NIRSpec images. The following sections describe the main processing
steps for NIRSpec imaging mode observations (the steps described in the “CALWebb”
processing are assumed to apply for the initial reduction to count rate images).
7.1
Flat Field Correction
Description: Correct the NIRSpec images for flat field effects. Lamp flats acquired
using the CAA may be used, with the note that the light from the calibration lamps does
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 26 -
JWST-STScI-001859
SM-12
not go through the NIRSpec filters. Additionally, the MSA is always in place, which
causes a grid pattern modulation of the flat field illumination on the detector. Yet, CAA
lamp images will be acquired simultaneously with the acquisition images during the TA
process, and these will have the same grating mirror shift and can serve well to correct
the imaging flat field effects. Correction to the lamp flat using sky flats and/or a
throughput correction (model) image may also be needed.
Input files: The reduced NIRSpec count rate images of dimension 2040x2040, with
corresponding UNC and DQ extensions.
Input Reference Files: The reduced lamp flat images of dimension 2040x2040, with
corresponding UNC and DQ extensions. If necessary, sky flat / throughput correction
images with corresponding UNC and DQ extensions.
Output Files: The flat-field corrected NIRSpec images of dimension 2040x2040, with
corresponding UNC and DQ extensions.
7.2
Absolute Flux Calibration
Description: Calibrate the NIRSpec images to an absolute flux scale (units are TBD).
Input files: The flat-field corrected NIRSpec images of size 2040x2040, with
corresponding UNC and DQ extensions.
Input Reference Info.: Input flux calibration information, likely in the form of a
photometric calibration header keyword determined from prior calibration observations.
Output Files: The absolutely calibrated NIRSpec images of size 2040x2040, with
corresponding UNC and DQ extensions.
7.3
Field Distortion Calibration
It will be necessary to correct the NIRSpec images for field distortion effects for proper
comparison with images and catalogs from other instruments (particularly the NIRCam
or WFC3 pre-image). Observations of the JWST astrometric calibration field in the large
Magellenic Cloud will be made during commissioning to verify and calibrate the
NIRSpec field distortion (See NIRSpec Calibration Plan).
Description: Correct the calibrated NIRSpec images for field distortion.
Input files: The flux calibrated NIRSpec images of size 2040x2040, with corresponding
UNC and DQ extensions.
Input Reference Info.: The NIRSpec field distortion model, likely in the form of
polynomial coefficients that accurately describe the distortion.
Output Files: The distortion calibrated NIRSpec images, with corresponding UNC and
DQ extensions (dimensions TBD).
8.0
“Next Level” NIRCam Data Reduction for NIRSpec Pre-Imaging
There are several processing steps beyond the baseline NIRCam imaging reduction that
may be necessary in order for NIRCam images to be used efficiently for NIRSpec preCheck with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 27 -
JWST-STScI-001859
SM-12
imaging and MSA spectroscopy definition. At the present time (and to the best of our
knowledge), the plan for NIRCam imaging data reduction is to construct flat field
corrected images that have been calibrated for absolute flux for each of the 10 detectors.
Next level processing steps such as image mosaicing are not planned for initial pipeline
implementation. Below we describe three processing steps that may not be included in
the baseline initial pipeline development for NIRCam, but will be crucial for NIRSpec
pre-imaging.
8.1
NIRCam Field Distortion
NIRSpec target acquisition requires astrometric accuracy of better than 5 mas for MSA
mode observations. NIRCam imaging will be the primary means to acquire accurate
source positions for NIRSpec target acquisition reference targets (WFC3 images will
likely be used at the beginning of the mission, but may not exist for many fields). As a
result, very accurate field distortion measurement and correction for NIRCam imaging is
critical for NIRSpec. This is a ‘next level’ processing step for NIRCam images that will
impact NIRSpec if it is not implemented early in the mission.
8.2
Mosaic Dithered NIRCam Frames
The NIRSpec team will likely define a limited set of fixed dither patterns for NIRCam
imaging mode observations that will be optimized for MSA pre-imaging (e.g., Anderson
2009). Most of the NIRSpec MSA target acquisition and science observations will be
defined by general observers after the NIRCam pre-images are acquired. The NIRCam
data for pre-imaging obtained using a canned dither pattern should be automatically
reduced into a larger mosaic image by the pipeline. If general observers are expected to
process pre-imaging data into a larger mosaic to identify their target and reference
sources, this will cause delays in the definition of MSA science observations and
potential problems for scheduling.
8.3
NIRCam Imaging Source Extraction
General observers will need to estimate NIRSpec target acquisition exposure times and
identify reference stars based on calibrated NIRCam images. To do this, accurate
positions and magnitudes of sources within the images will be required. An automated
source extraction procedure should be run on the NIRCam pre-images to provide
reference magnitudes and positions for potential NIRSpec target acquisition sources. If
general observers are expected to run their own source identification procedures to
identify their reference sources for MSA target acquisition definition, this will cause
delays in the definition of MSA science observations and potential problems for
scheduling. The same software that identifies the positions of MSA science targets could
also be made to extract source profile information and save this for use the in the pipeline
spectral extraction and calculation of aperture flux corrections. More work is TBD to
demonstrate the feasibility of this.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 28 -
JWST-STScI-001859
SM-12
9.0
Open Issues Regarding NIRSpec Data Reduction and Pipeline Calibration
(Notes from and discussion w/ J. Valenti)
9.1
General Open Issues
9.1.1 Propagation of MSA Shutter and Target Information
The NIRSpec data processing pipeline will need to know a lot of information about the
MSA configuration – beyond the general shutter open/closed map that is uploaded to the
spacecraft. The pipeline will additionally need to know which open MSA shutters are on
science targets, and which are open on background flux – and when this changes with
dithers. Additionally, the pipeline should be able to capture information on the target
centering within an MSA shutter, which is information that will be captured during the
observation planning process with the MSA Planning tool in the APT. In general, a
method for automatically associating background shutters with sources for multiple MSA
configurations will be difficult. It also might not be possible in all science applications to
measure flux background close to the science sources. It should be possible to make
multiple associations for background flux for MSA science targets in the pipeline, based
on the user inputs to APT. Moreover, the pipeline should correct for known differences
in projected shutter sizes, regardless of the separation between source and background
shutters.
9.1.2 Exposure-Level data processing vs. Association data processing:
The requirement that separate exposures (e.g., at the same position or different dither
locations) be combined before extracting spectra complicates data processing. All data
have to be received before any spectra can be extracted. Raw and/or intermediate
products from data associations have to be queued until processing can be completed.
There are other more subtle issues. We need to thoroughly investigate the relationship
between exposure-level processing and data association processing (including dithers and
background subtraction steps, which do likely require associated data not single
exposures). Should there be separate outputs at the exposure level and association level?
9.1.3 Data Reduction File Structures
File structure for the process of NIRSpec data reduction has not been established. More
information needs to be gathered and determined for the raw input file structure. We also
need to decide how we handle multiple integrations within a single exposure. Perhaps
the user can click a radio button in the APT that requests integrations are combined into a
single exposure image. This might clear up some confusion in the pipeline processing
and make the reduction outputs more streamlined for observations with multiple
integrations in a single exposure. (TBD).
In the data reduction process, if each MSA spectrum is stored in a separate FITS
extension or a separate FITS file, then it makes sense to store the predicted target
centering information from the MSA planning process within the associated header. If
we bundle all of the spectra into a single array, then storing the predicted centering
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 29 -
JWST-STScI-001859
SM-12
information in a single FITS header would be awkward. In the latter case, the predicted
locations should be stored in a separate extension or more likely a separate file.
9.1.4 R=2700 Mode and Detector Gap-Spanning Observations
In the R=2700 (high resolution) grating settings with NIRSpec, the useful scientific
spectral data will extend over both detectors in all observing modes (FS, MSA, IFU).
This will also happen with some MSA configurations using the R=1000 gratings. At
some point in the reduction process, we will want to join the two segments of each
spectrum that span the detector gap. We need to determine when and how in the data
reduction this is accomplished. Regarding how, we have a few choices: 1) splice the two
detector spectral segments together, leaving a discontinuity in the wavelength scale or 2)
create a continuous wavelength scale and flag the missing values. In this document
section on extraction 2-D into data sub-windows for each open MSA shutter, it is noted
that the extraction window in R=2700 mode may extend to cover both detectors. This is
the first processing step where the detector gap spanning spectral regions might be
extracted and merged together. The details of this are TBD.
9.1.5 Pipeline Output Data Products
The goal for final science quality reduced and calibrated pipeline data products for MSA
science could consist of:
• Requested and/or actual MSA configuration with predicted and/or actual target
locations
• For each target in each exposure: (a) rectified 2-D spectra, and (b) extracted 1-D
spectra. Each spectrum has associated uncertainties and data quality flags.
• For each target in a group of associated exposures: (a) combined rectified 2-D
spectra, (b) extracted combined rectified 2-D spectra, and (c) combined extracted
1-D spectra. Each spectrum has associated uncertainties and data quality flags.
Corresponding output products for FS and IFU science are assumed (with IFU outputs
being 3-D datacubes, not extracted spectra). The final outputs are TBD.
9.1.6 Temporal Variability of Instrument Performance
Instrument performance will evolve over the course of the mission. [Our knowledge of
calibration parameters will improve as well, but this paragraph is about actual changes in
instrument performance.] For example, dark rates will increase due to radiation damage.
In principle, any value that is stored in a reference file may evolve. Calibration reference
files must have one or more methods for describing this evolution.
The HST calibration database system (CDBS) contains a “use after” date for each
reference file. When processing a particular observation, HST calibration pipelines select
the reference file of the appropriate type with the latest “use after” date that precedes the
observation date. Reference files that have not evolved over the course of the mission
have a “use after” date that precedes the launch date. The “use after” formalism describes
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 30 -
JWST-STScI-001859
SM-12
instrument performance as intervals of constant performance, punctuated by moments of
discrete change. The “use after” capability is used extensively for darks, which degrade
continuously due to radiation damage and suddenly due to anneals. Even relatively stable
calibration parameters may change suddenly, for example when switching from primary
to backup electronics.
Gradual evolution of instrument performance may be described by calibration parameters
that have a piecewise linear dependence on time. CDBS does not (currently) provide this
capability, but certain HST reference files store temporal evolution data internally.
JWST does not have to adopt the existing CDBS and HST reference file design, but
JWST should “learn lessons” from HST. JWST reference files should have a general
mechanism for describing both gradual and sudden changes in instrument performance.
The CDBS “use after” mechanism has worked well for describing sudden changes. A
more generic mechanism is needed to describe gradual changes, for example the ability to
interpolate linearly between two reference files.
9.1.7 Correlated Errors
Random errors are straightforward to propagate and report. Correlated errors are harder
to track and describe. For example, an error in background subtraction can bias all pixels
by the same amount, leading to correlated errors in the resulting spectrum points. These
correlated errors may dominate random errors for high S/N observations. We should
think about how to track and report correlated errors.
9.2
Issues from Specific Sections in the Document
9.2.1 Reference Pixel correction
Bias and photoelectrons have different noise characteristics, so the reference pixel
subtraction step of the pipeline is the first opportunity to calculate uncertainties in pixel
values. If the reference pixel correction procedure contributes significantly to overall
uncertainty, then the pipeline must record bias uncertainties in the output data for this
step. To calculate the uncertainty for each pixel value, the pipeline would need the gain
now, rather than waiting until ramps are fitted (Section 3.7)1.
9.2.2 Dark Subtraction
We should describe the possibility that dark rates and associated uncertainties for each
pixel will be described by a few function parameters, rather than explicit values at 1000
different times in the ramp. First, a fitted function is often more precise than the data that
were fitted. The fit will have to take into account the fact that up-the-ramp samples are
correlated. Second, a reference file containing function parameters will require only 2%
1
Alternatively, the pipeline could store the uncertainty in the bias in an extension with a different name
(e.g., UNCBIAS), but there is no obvious advantage to deferring beyond this point the calculation of
uncertainties in measured pixel values.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 31 -
JWST-STScI-001859
SM-12
of the space required to store every measured sample. Assuming 4-byte floating-point
values, this reduces the file size from 33 GB to approximately 500 MB. Every user that
wants to reprocess NIRSpec data will need to download this file, so smaller is better.
9.2.3 Data Combining and Background Subtraction
The philosophy and methods for combining data and subtracting off background flux
need a more thorough investigation at all levels. Justification for data combining and
backgrounds subtraction early in the reduction process should be made. If this is best
done very early in the reduction, then reducing exposure-level data should probably
progress before all dithered observations in association-level data are acquired. If this is
best done at later stages, then this needs described as well.
Data combine and background subtraction steps were merged into the processing at early
points in the reduction in part for time and efficiency reasons. It will more efficient for a
pipeline to extract and process 150+ shutter spectra from one combined, sky subtracted
science image than to have to reduce the same 150+ shutter spectra many, many times
from the different raw (uncombined) exposures. It may not be possible to execute data
combine/background subtraction steps in this manner for all science programs, depending
on the observing philosophy. These issues need investigated more conclusively.
Automatically associating background shutters with each source for non-standard MSA
multi-shutter slit configurations is difficult, so I understand deferring implementation of
that capability. However, allowing observers to suggest such associations is
straightforward and should be implemented for Cycle 1.
In general, separate code (possible in the same program) will be required to process
original pixels versus spatially and spectrally rectified data. We should prioritize which
code to develop first. Assuming both processing options are available, we need to
develop criteria that the pipeline can use to select a processing strategy. Alternatively, we
could have the pipeline process every data set both ways (original pixels and rectified
data).
9.2.4 L-Flat and Throughput Correction
The calibration approach advocated in the ESA Pipeline Requirements document may not
be sufficient to remove the throughput dependence on wavelength and position in the
field of view. Internal lamp spectra: (a) include the reflectivity of three calibration
mirrors not used when observing external sources, and (b) exclude the reflectivity of
eight mirrors and a filter that are used when observing external sources. An optical model
could be used to correct measured flats for the three extra and nine missing optical
elements. However, external sources observed by the telescope may fill the pupil
differently than the calibration assembly, leading to different vignetting. A detailed
optical model might be able to correct measured flats for different pupil illumination, but
more likely the corrections will be determined by observing a set of external sources at
different positions in the field of view.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 32 -
JWST-STScI-001859
SM-12
Regardless of whether calibration data are obtained from internal lamp spectra, external
sources, or both, we should consider whether to store throughput end-to-end or for each
component separately. In the later case, one or more pseudo-components would be
needed to describe empirical corrections to the model.
Every MSA shutter will have a different end-to-end throughput because vignetting
depends on location in the field of view (and hence location in the MSA). Storing two
2040 x 2040 throughput images (one per detector) for each shutter and each grating/filter
combination is not feasible because the reference file would contain 2x1013 (2 x 2040 x
2040 x 365 x 171 x 4 x 10) data values. Using parameterized functions (two-dimensional
polynomials, for example) to describe throughput as a function of MSA shutter and/or
detector pixel would significantly reduce reference file size, but vignetting may have
localized or sharp features that cannot be described by low-order polynomials.
It might be useful to factor the throughput correction into two components:
1. Sky-to-MSA throughput is a function of position in the field of view and perhaps
filter. Optical models and calibration observations will establish whether: (a) sky-toMSA vignetting depends on filter, and (b) whether the vignetting can be described by
a parameterized two-dimensional function of position in the field of view. In the
worst case, this throughput component would require one map of the MSA focal
plane for each of the 7 filters (2x106 values). In the best case, a single twodimensional function would describe the behavior for all filters (less than 100
parameters).
2. MSA-to-detector throughput is a function of MSA shutter index, location on the FPA,
and perhaps grating. Optical models and calibration observations will establish
whether: (a) MSA-to-detector vignetting depends on grating, (b) whether the
vignetting can be described as a parameterized two-dimensional function of MSA
shutter index, (c) whether the vignetting can be described as a one-dimensional
function of location along a dispersed spectrum, and (d) whether the vignetting can be
described as a parameterized two-dimensional function of location on the FPA. In the
worst case, this component of the throughput would require two 2040 x 2040 images
for each shutter and each of the 8 grating wheel elements (2x1013 values, infeasible).
In an intermediate case, each shutter would have a separate a one-dimensional
throughput vector for each shutter, but vignetting would be independent of disperser
(109 values, perhaps feasible). In the best case, a parameterized function of MSA
shutter index and location in the FPA would describe the vignetting for all dispersers
and grating efficiency would be stored separately (103 parameters).
The functional form of throughput (including vignetting) will be uncertain until
commissioning. Thus, the format of the throughput reference files must be flexible
enough to accommodate functional forms. In practice, this means the reference file must
include a parameter that specifies the functional form of the throughput description.
It might be simpler to apply the correction for chromatic slit loss later, when correcting
for location of the target in the aperture.
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 33 -
JWST-STScI-001859
SM-12
9.2.5 Aperture Flux Correction
Consider applying the chromatic slit loss correction here, rather than earlier in the
process.
9.2.6 Absolute Flux Calibration
The conversion from measured counts per second to incident flux is a strong function of
wavelength, yet the text describes the calibration information as data that can be stored in
a header keyword, which suggests a scalar. A scalar conversion factor is only possible if
the wavelength dependence has already been removed in a preceding step, for example
the “Complete Throughput… Correction”, that is described in Section 4.5. Such an
approach would distort the meaning of “counts per second” at the shortest and longest
wavelengths, where sensitivity is low and very large correction factors will be applied.
Noise at these extreme wavelengths would also be amplified. An alternative would be to
let the conversion from measured counts per second to incident flux be a function of
wavelength for each disperser. The pipeline would read this input reference information
from a reference file, rather than the FITS header.
10.0 References
Anderson, J. A. 2009 “Dither Patterns for NIRCam Imaging” JWST-STScI-001738
Fixsen, D.J., Offenberg, J.D., Hanisch, R.J., Mather, J.C., Nieto-Santisteban, M.~A.,
Sengupta, R., & Stockman, H.S. 2000, PASP, 112, 1350
Kriss, J. 2004 “Recommendations for JWST FITS Formats and Keywords” JWSTSTScI-000380
McCullough, P. 2008 “Inter-pixel Capacitance: prospects for deconvolution” WFC3
2008-26
Regan, M. et al. 2009 “The Effect of Splitting the Exposure Time on the Observed
Persistence after a Bright Exposure for a H2RG Detector” JWST-STScI-001743
Regan, M. 2007, “Optimum weighting of up-the-ramp readouts and how to handle
cosmic rays” JWST-STScI-001212
Tumlinson, J. 2009a “NIRSpec Dithering Strategy Part 3: The Micro-Shutter Array”
Tumlinson, J. 2009b “NIRSpec Dithering Strategy Part 2: The Integral Field Unit”
Check with the JWST SOCCER Database at: http://soccer.stsci.edu/DmsProdAgile/PLMServlet
To verify that this is the current version.
- 34 -