TECHNICAL
REPORT
Title: NIRSpec in the Dense Field Limit:
An Observing Scenario
Doc #:
Date:
Rev:
Authors: J. Tumlinson
Release Date: 16 November 2010
Phone: 410338-4553
JWST-STScI-002129, SM-12
June 22, 2010
1.0 Abstract
This study examines a NIRSpec Observing Scenario for multi-object spectroscopy
(MOS) of dense fields of stars, such as the centers of globular clusters or the Galactic
bulge. Two potential practical limits to using NIRSpec in dense fields are considered
here: source crowding and the confusing effects of bright “spoiler” sources in the field.
These effects are studied with a quantitative model that places MSA shutters on realistic
fields and identifies targets with shutters including the effects of geometric optical
distortion, shutter failures, and intra-shutter transmission variations. Test cases are
calculated for the globular cluster Omega Cen and Galactic bulge fields at three different
Galactic latitudes. While the effects of source crowding and spoilers reduce the number
of sources that can be observed in a given pointing, 10 – 40% of sources can still be
observed cleanly (i.e., by themselves in the acceptance zone of a shutter) in multiple
MSA configurations without moving the telescope. This result obtains in the Omega Cen
case and in the bulge outside ~3º; inside this radius source crowding becomes prohibitive.
An observing strategy with no dithering may be optimal for dense fields, since arbitrary
pointings place a large fraction of available targets in shutters naturally. Spoiler sources
reduce the raw dynamic range of the observations, but faint sources may be recoverable
by use of a “spoiler cancellation” exposure taken through an all-closed MSA and
subtracted from the science exposure. Thus NIRSpec can be used effectively in very
dense stellar fields provided that the user’s scientific requirements are satisfied by a
statistical subsampling of the available targets. Some other operational consequences of
these science use cases are also considered.
2.0 Introduction
NIRSpec’s driving science case is based on high-redshift galaxies. While these are
interesting, they are not the only object on the sky for which we might desire spectra.
NIRSpec will be extremely effective for multi-object spectroscopy of Galactic stars; it
can obtain spectra with S/N = 20 per resolution element in about 5 minutes at J ~ 20 at a
resolving power of 2700. With a quarter of a million apertures packed into ~11 arcmin2,
Operated by the Association of Universities for Research in Astronomy, Inc., for the National
Aeronautics and Space Administration under Contract NAS5-03127
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NIRSpec may also be suitable for very dense stellar fields where the density of sources
approaches within an order of magnitude of the density of shutters. While it is easy to
guess that very dense fields would test the limits of NIRSpec design and operations, the
practical limits are not defined well enough to say for sure. The goal of this study is to
quantitatively assess these practical limits, to explore the observing strategies that might
be best suited for observations of dense fields, and to gauge the operational consequences
of these strategies. Because real fields will also include sources at a wide range of
brightness (e.g., giants and main-sequence stars in an old population), we must also
explore the limits of NIRSpec’s dynamic range. Sufficiently bright sources can act as
“spoilers” by shining light on the detector even if they are behind closed shutters (which
have a finite contrast); a mixture of faint science targets and bright spoiler sources
becomes more likely as the field gets denser. This investigation attempts to address some
of the practical issues associated with observing dense fields using simulated
configurations of real test-case fields in Omega Cen and the Galactic bulge.
3.0 When is a Field Dense?
A NIRSpec field could be considered dense according to several different definitions, of
roughly increasing density:
1. The density of sources is high enough to make the “standard 3x1 slitlet” approach
to MSA configuration and dithering (OCD Section 7.3.3.1) impractical (because
too many “background” shutters would contain sources).
2. The density of sources is high enough for each MSA row (encompassing two
quadrants) to contain more than one source. Multiple MSA configurations are
therefore required (except perhaps for R = 100 prism spectra).
3. The density of sources is high enough for each shutter to contain 1/2 source, and for
some shutters to contain 2 or more sources.
The first criterion is easy to understand and quantify. The “standard 3x1 slitlet” approach
to placing and dithering MSA targets is designed to supply high-fidelity measurements of
sky backgrounds for subtraction from faint sources, such as high-redshift galaxies.
Figure 1: 2MASS image of the Galactic
bulge with the four HST Treasury fields
from Brown et al. (2009, AJ, 137, 3172)
marked with diamonds. Grid lines mark 5°
intervals.
Since this scheme uses 3 shutters in the cross-dispersion direction and, for R = 2700, all
the other shutters in three rows over two MSA quadrants must remain closed, it
effectively assigns 3 x 2 x 365 = 2190 shutters to a single object in a single MSA
configuration. There is a theoretical maximum of 114 such regions on the array
(=171x2/3, leaving aside failed shutters). Thus having more than approximately 100
sources in a NIRSpec field would qualify as “dense”. If we take the HUDF catalog as an
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example of NIRSpec’s primary science case and a typical high-redshift field, we find
roughly 1000 high-redshift dropout sources in the NIRSpec FOV of 11 arcmin2, or ten
times denser than this lowest limit. Yet the science team probably does not consider their
fields “dense”, and in any case Muzerolle (2009) has already studied the optimization
scheme for targets at this density for the particular case of the UDF. For the 3x1 slitlet
scheme approximately 8 configurations are required to cover 50% of the galaxies; for
75% coverage 15 configurations are required and for complete coverage 50
configurations are needed. We will set our boundary for “dense” at a higher value.
2
Figure 2: Cumulative density of sources per arcmin versus R magnitude for Omega Cen and
versus J magnitude for the Milky Way bulge at three Galactic latitudes. The MSTO for Omega
Cen lies at R~18, for the bulge at J~19. NIRSpec will easily reach below the turnoff in both cases.
At a density roughly three times higher (so, 3 per 2190 shutters) it will become desirable
to abandon the 3x1 slitlet scheme, especially for brighter targets where high-fidelity sky
backgrounds are less critical to the analysis. yet this is still only ~300 targets per FOV,
and not qualitatively different from the UDF case.
Another way to ask the question is whether there are interesting fields where the density
of targets is far higher. The Galactic bulge (Figure 1) and the globular cluster Omega Cen
are good examples of dense fields. The cumulative number counts for these fields are
displayed in Figure 2 where they are compared to the UDF. Omega Cen and the bulge
achieve 1 source per ten shutters at 20th mag (here, R band for Omega Cen and J band
for the bulge), far higher density than the maximum of 1 source per 250 shutters in the
UDF. At 23rd mag, where NIRSpec spectroscopy is still practical in terms of sensitivity,
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the bulge and Omega Cen fields begin to approach the limit where there is one source for
roughly every two shutters. The numbers are of course still higher as the Galactic center
is approached, with 1 source / shutter at 20th mag in a field at 1° above the plane.
We therefore conclude that astronomically interesting fields exist in multiple places
within the Galaxy where the source density is 1-3 orders of magnitude higher than the
HUDF and approaches the density of shutters in the MSA. We must find the practical
limits to using NIRSpec effectively in this regime.
4.0 Test Case 1: Carbon Abundances in Omega Centauri
For a test case we adopt a simple but challenging scientific goal: to measure abundances
of carbon in 1000 - 10000 stars in the globular cluster Omega Cen. Carbon abundance in
Pop II stars can show signatures of binarity or unusual nucleosynthesis. We want to
obtain abundances for stars in three different regions of Omega Cen to look for gradients
in C abundance with location in the cluster or stellar orbits within the potential well of the
cluster.
For this scenario, the region is the Omega Cen ACS mosaic provided by Jay Anderson
(Figure 3). Within this region, there are three fields separated by more than 20”, as
shown. The middle field lies at the center of the cluster and is taken as the limiting case
for further analysis. In this field, there are NFOV = 157696 objects down to the R = 24
mag limit of the image. For practicality of the overall observing program, we take the
limiting flux at which we will work to be the magnitude, R ~ 20, where the NIRSpec H
gratings (with spectral resolving power of 2700) will deliver 1 count per second per resel.
Thus, each exposure of some sources will reach S/N ~ 20 in ~ 400 seconds exposure time
(NRSRAPID, NGROUPS = 40 gives 424 sec). Down to this limiting magnitude, there
are “only” NFOV = 61098 objects within the four corners of the MSA. Placing the MSA
center somewhat arbitrarily at ra,dec = 201.70, -47.48, we find that 27278 of these stars
are within the geometric open area of a shutter (i.e. not behind an inter-shutter bar or
between quadrants). So roughly half of the targets of interest are in a shutter for this
random centering of the MSA. How will we cover these 27278 targets?
The very simplest optimization scheme is not to “optimize” at all but rather to leave the
MSA pointed where it is and fill out the coverage of the field only by reconfiguring the
shutters instead of dithering. One way to do this is to start with the first (leftmost, lowest
x coordinate) target in each row, open a shutter for the target in that shutter and take an
exposure. Then reconfigure the array to place an open shutter on the next target in each
row. And so on until most or all of the targets in each row have been observed. This
simple example assumes that it is possible to use every row without spectra from adjacent
rows overlapping due to curvature or distortion and that we do not require adjacent
shutters for measuring sky background. In this concrete example, there are 20 - 30
sources per row for Q1 and Q3 and 35-45 sources per row on Q2 and Q4 (with rows
encompassing two quadrants since the spectra in R = 2700 span both SCAs). The small
gradient comes from the increasing density towards the center of the cluster and nearer
Q2 and Q4.
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Figure 3: ACS mosaic of Omega Cen courtesy of Jay Anderson. This region contains three MSA fields
numbered 1 to 3 from left to right.
This observation required about forty 400 sec exposures (exclusive of overhead) using
every MSA row. This adds up to 40x171x2 ~ 12000 spectra total in 400 sec * 40 =
16000 sec, or 4.4 hours exclusive of overheads. Very roughly estimated overheads are 1
minute per MSA configuration and one minute per exposure for set up and cleanup,
which adds 2 minutes to each config/exposure pairing and so adds 80 minutes and brings
the total to about 6 hours. This is a reasonable allocation, and would cover all the
available “hits”, 1/2 of the targets within the four corners in that time.
4.1
Results for the Theoretical Ideal
We begin by studying the “theoretical ideal” performance, in terms of the fraction of stars
in the field that can be observed at a single pointing with multiple MSA configurations.
This “theoretical ideal” meets the following conditions: (1) every MSA shutter is
assumed to be operable, (2) shutters that are found to contain more than one target are not
excluded, (3) every row is used for science targets, i.e. no rows are saved exclusively for
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backgrounds and it is assumed that there are no problems with spectral overlap between
adjacent rows, and (4) the full open area of each shutter is assumed to be available for
target placement. In all these tests only stars with R ≤ 18 are considered (there are N =
51918 stars to this limit in the entire catalog for the cluster, and N = 305443 to R ≤ 20).
Figure 4: Zoom of field 2, with R < 20. Inoperable shutters are colored as detailed in the text. Targets in an
open shutter are marked with a green plus, and targets outside an operable shutter are marked with red x.
Table 1 displays the results of three fields as shown in Figure 3 for a magnitude cutoff of
R = 18. A zoom-in of Field 2 in this configuration is shown in Figure 4 (including bad
shutters, as explained below), and quantitative results for these fields as a function of
magnitude are shown in Figure 5.
The number of stars in the entire FOV (NFOV) and the number within the four corners of
each quadrant (NQ) are given in the first two rows; they do not change as the assumptions
(1)-(4) are relaxed. For the case of the ideal MSA (no failed shutters), we find that
⟨NS/NQ⟩ ~ 65-70% of stars in each quadrant fall within the open area of a shutter, where
NS gives the number of targets within a shutter for each quadrant. Summing over each
row (assumed to span two quadrants) reveals that the number of targets in each row
varies widely, between 6 and 29 targets per row for Field 2. Thus, in theory one could use
29 configurations to cover this field. In this example, the row with 29 targets in it would
contain a “target” shutter in all of the exposures, while the row with only 6 targets would
be used 6 times for science targets and 23 times for background shutters. Summing over
all the rows we find 10712 target spectra and 11518 background spectra in total. The
coverage fraction is typically ⟨SNS/NQ⟩ ~ 0.5 (e.g. 5306/10887), which is impressive
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considering that there is no dithering and that many background spectra are obtained
automatically.
4.2
Departures from the Theoretical Ideal
As with any ideal, the foregoing case is unattainable! The reason is that although
assumptions (2) - (4) are within user control to a limited degree, we will fly an MSA with
failed shutters and so (1) cannot possibly hold. The flight candidate arrays have already
been characterized (with maps current as of April 2010) and we can include them
precisely in our analysis1. The assumed bad shutter masks are shown in Figure 6. Taking
these into account but retaining conditions (2)-(4), we find that the drop in coverage is
only at the level of ~10% (lower rows in Table 1, e.g. 9771 / 10712 = 0.91). This loss
term agrees roughly with the total 8.2% filling factor of bad shutters (20515 out of
249660). The actual losses exceed the geometric filling factor slightly since the bad
shutters are not randomly distributed; some entire rows and columns are inoperable.
Table 1: Analysis of the three fields in this region assuming a single center position for each and the “no
dither” statistical sampling technique with backgrounds. Results are shown for the case of an “ideal MSA”
and for a realistic map of failed shutters (current as of April 2010). The results correspond to “science
targets” down to R = 18, the MSTO for this cluster.
Field 1
Field 2
Field 3
α = 201.77, δ = -47.50
α = 201.70, δ = -47.48
α = 201.63, δ = -47.46
0
Ideal
N
N
N
ΣN
MSA
maxNrow
FOV
Q
S
S
N
Totals
bckgnd
Non
Ideal
MSA
N
ΣN
ΣN
S
S
row
Totals
Acc
N
ΣN
Zone
maxNrow
acc
S
Totals
0
0
0
0
0
5894
10887
5037
881, 1351, 583, 851 1985, 2071, 1856, 1850 979, 693, 1596, 1017
615, 892, 401, 564
1382, 1406, 1271, 1244
647, 493, 1095, 699
2472 (49% of N )
5306 (49% of N )
2934 (50% of N )
1 - 17
6 - 29
1 - 19
3342
4612
3564
10712 objects and 11518 backgrounds in 65 configurations
581, 812, 361, 505
1275, 1279, 1149, 1135
613, 446, 982, 637
2259 (45% of FOV)
4838 (44% of FOV)
2678 (45% of FOV)
1 - 13
4 - 28
2 - 18
9771 objects in 59 configurations
325, 482, 229, 307
772, 742, 694, 691
382, 272, 620, 391
1342 (27% of N )
2889 (27% of N )
1665 (28% of N )
0 - 11
2 - 19
0 - 13
5896 objects in 43 configurations
FOV
FOV
FOV
FOV
FOV
FOV
Figure 5 shows a zoom-in on a portion of Q1 showing a green “+” for targets in an
operable shutter and a red “x” for targets not in an operable shutter. Optical distortion is
included here but the MSA is plotted in physical coordinates so the shutters appear
rectangular while the “sky” is distorted. Bad shutters are color-coded. Even with failed
shutters and target crowding effects accounted for, the coverage rate is > 35%. This is
1
The adopted mask files are: MSA_map_Q1-37-137, MSA_map_Q3-26-106, MSA_map_Q2-42-128, and
MSA_map_Q4-55-116.
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still a limiting case, since it keeps assumptions (2) - (4). We now relax these in order to
estimate their impact on the total coverage of these fields.
Figure 5: Performance statistics for the three Omega Cen fields, 1 to 3 from left to right. Top panels:
Cumulative totals for NFOV (solid lines), number within operable shutters (dotted line), number of targets
falling into operable shutters with no confusing sources (red dashed lines), and number within the
acceptance zone of operable shutters with no confusing sources (blue dash-dot line). The middle panels
show the ratios of the other three curves above to the NFOV curve. Bottom panels: the minimum, maximum,
and mean number of targets in operable shutters per row after screening for acceptance zones and source
confusion. These quantities, particularly the maxima, are good approximations for the number of MSA
configurations needed to obtain the number of target spectra plotted in the top row.
Impact of multiple targets in a shutter: Relaxing condition (2), we find that a fraction of
shutters contain more than one target and should be avoided since the spectra cannot be
separated. We’ll call these “crowding losses”. In field 3, with a brightness threshold of R
= 20, we find that 27278 targets are in shutters (Ns,tot), and 22688 of these are there by
themselves with no confusing sources (counting stars down to R = 24 as contaminants). If
we count only stars within the “target acceptance zone” where the slit transmission
correction is known to <10%, the numbers are Nacc,tot = 16493 with 13856 in
acceptance zones with no other targets in the shutter (whether in the acceptance zone or
not). Thus 16% of science targets down to R = 20 are lost to same-shutter contaminants
down to R = 24. Given the extreme density in this field, these losses seem tolerable.
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Figure 6: Bad shutter mask assumed in this analysis. Failure mode is coded in color as noted; the mask file
provided for Q4 did not specify the failure mode for each shutter and so all inoperable are marked in black.
Impact of using less than every row: Relaxing (3), we can consider the effect of using
less than all the available rows. This might be necessary, for instance, if the user desires
to place background shutters adjacent to the target shutter in the next row, where
possible. This might also occur if it turns out that spectra taken in adjoining rows overlap
on the detector due to curvature (though this is not expected). It might also happen that
the spectrum from a target near the bottom of its shutter will overlap with a spectrum
from a target in the top of the shutter just below. Since all these details are either
indeterminate until further instrument testing, or subject to user choice, we regard the
choice of how many rows to use as rather arbitrary; there is no obvious limit to using all
the operable ones. Without modeling this in quantitative detail, we can easily see that
breaking the 28 configurations in Field 2 for the non-ideal MSA (bottom section of Table
1) and using the even-numbered rows in one set and the odd-numbered rows in another
would roughly either halve the number of acquired targets and the coverage or would
require twice as many configurations and therefore twice as much time to cover the same
targets. The coverage is still quite effective, even if inefficient (~10%), even if it proves
necessary to use less than unity filling factor of all the rows (e.g. every other row).
Impact of using target “acceptance zones”: Finally, the most serious hit to efficiency
comes from relaxing condition (4) and using only the target “acceptance zone”
(sometimes equivalently called the “sweet spot”). The dashed blue curves in Figure 5
show the magnitude of this effect. Restricting the targets to the inner, flat portion of the
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slit transmission curves excludes targets outside ~60 mas from the center in x and outside
180 mas from the center in y. Since targets are rather evenly spread through the open area
of shutters in a field such as this, the number in the acceptance zone declines in
proportion to the fractional area of the shutter used. Enforcing the 10% error rule for
Band I (blue curves in Figure 5) reduces the total number of targets to ~62% of the total
using all the shutter area (red curves).
Summing up, we find that source losses are ~10% due to bad shutters and ~16% due to
source crowding. We find that 25 - 40% of targets within a FOV will be observable at a
given pointing, roughly independent of source magnitude of interest (and leaving aside
dynamic range for now). This result is seen in the relatively flat blue and red curves in
the middle panels of Figure 5. This is a key result.
Impact of low contrast shutters: Some shutters on the flight MSAs transmit more than
1/2000 of the light to which they are exposed because the shutter doors do not close
completely or because the MSA substrate is not sufficiently opaque. Sources behind low
contrast shutters can contaminate the spectra of targets in that same MSA row, so it is
generally desirable to avoid having more than a trivially small number of counts in a
spectrum coming from a target within a low-contrast shutter in the same row as a science
target. If a bright star of magnitude M falls behind a shutter with contrast C it behaves
like a star with magnitude M+2.5log(C) behind an open shutter. This “spoiler” star will
contribute a fraction X of the total counts, on average, to a source with magnitude S =
M+2.5log(C) + 2.5log(X) in the same row. The shutter masks we use here contain only
19 shutters marked as low contrast, C < 2000, including two intermittent failures.
To assess the quantitative impact of these low-contrast shutters, we simplify the
calculation and assume the worst case, in which any star behind a low-contrast shutter
contaminates every target in that row2. To derive concrete numbers, we first identify all
targets in a low-contrast shutter for each of the three fields and count up the total number
of targets in each affected row. The magnitude of the problem is represented well by
Field 2, where the density of sources is highest; here we find that considering all stars
down to R = 20 as possible spoilers, there are 5 targets in low-contrast shutters. These
rows contain 611 targets within operable shutters, which cannot now be observed because
they are contaminated by the 5 (note that the total 611 includes targets in shutter with
other targets, which would be excluded anyway). For comparison, there are 25190 targets
in open shutters by themselves (N1), so the low-contrast shutters potentially remove 2.4%
of the eligible targets that remain after screening for inoperable shutters and multiple
targets within shutters.
Thus low-contrast shutters remove only a small percentage of eligible targets; sensibly
enough, since there are not many of them. We emphasize again that this is a worst case,
since, for example, R = 20 stars are counted as possible contaminants for R = 18 stars,
which they will not really be if the logic in the MSA planning tool is able to account for
this. This issue should be studied further.
2
In the more realistic case, some targets in a row with a low-contrast shutter may be just bright enough,
and the contrast in the bad shutter just high enough, to permit some of the brighter targets in the row to be
used anyway. Quantifying this case requires a more complicated algorithm than we are interested in
developing here, where limiting cases are best.
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5.0 Test Case 2: Kinematics and Abundances in the Galactic Bulge
The Milky Way’s bulge is another area of the sky where stellar densities are high but
spectroscopy to measure kinematics and abundances is desirable. As shown in Figure 2,
the density of bulge stars at a Galactic latitude b = 5° (l = 0°) closely tracks the density of
stars in the center of Omega Cen, and greatly exceeds that density inside 5° from the
center. Thus it appears that NIRSpec is most likely to reach the practical limits imposed
by crowding somewhere inside 5° from the Galactic center. Note that these models
assume no dust, which will reduce the source density somewhat at fixed Galactic latitude.
For a quantitative model of the Galactic bulge, we use population synthesis models
derived from the online TRILEGAL code developed by Leo Girardi
(http://stev.oapd.inaf.it/cgi-bin/trilegal). Three bulge models were obtained for b = 1, 3,
and 5° directly above the Galactic center at l = 0° degrees longitude. These bulge models
are displayed in Figure 2 and the results of MSA analysis of these fields are shown in
Figure 7.
Figure 7: Coverage statistics for the bulge test case, as in Figure 5.
The bulge results indicate that somewhere between b = 3° and b = 1°, NIRSpec is no
longer effective because of source crowding. At b = 5°, the fraction of sources that fall
into open shutters is 30%, and 20% for targets in the acceptance zone (red and blue
curves in the middle panel). This is, as expected based on Figure 2, similar to the Omega
Cen results. For b = 3°, these fractions are still ~11% and 7%, which is still arguably
effective and may suffice for a useful observation, depending on the user’s individual
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requirements. However, at b = 1° the number of sources remaining for study after
multiples have been cleaned out is < 1% of the total, or a few hundred sources at J = 20.
While in some cases obtaining spectra for a few hundred sources out of more than 105 is
still useful, the fact that these few hundred are selected according to random chance of
placement on the sky and not intrinsic properties will likely make this hit rate not worth
the trouble. However, in cases where a sampling of 0.1 - 1 % of bulge sources drawn
randomly is worthwhile, it still might be possible in principle. But if we take a “covering
fraction” of 10% of the field as the nominal goal, this limit is reached at b ~ 3°.
6.0 Shutter Contrast and Dynamic Range
We have estimated the impact of low-contrast shutters on these dense-field observations.
But contrast is an issue not just for shutters that are out of spec, since even shutters with
contrast > 2000 can limit the dynamic range in a particular field. For instance, if a star
with J = 12 falls behind a shutter with contrast of 10000, then that star will generate as
many counts as a star of J = 22 somewhere else in the row. If we insist that the bright star
contribute less than 1% of the counts to the spectrum of the fainter star, then the fainter
star must have J < 17. Thus for a contrast of C = 10000 and a L = 1% contamination
limit, we obtain a dynamic range of 5 mag. Generally, the dynamic range D is given by:
D = 2.5 log (CL)
Preliminary laboratory measurements of the flight MSA quadrants indicate that the
median contrast is approximately C > 8000, except on Q1 where it may be C ~ 4000. The
implied dynamic range for a 1% contamination limit is 4 - 4.75 mag. In some cases a
higher contamination limit may be tolerable, or it may be possible to subtract off the
contamination from the brighter source by differencing two exposures at the nominal
pointing: one through an all-closed MSA to record the attenuated spectra of the spoiler
sources when no target shutters are open, and another with the target shutters open to
record the sum of the main target and the spoiler. Subtracting the first exposure from the
second should, in principle, remove the contamination. Testing this method is beyond the
scope of the present study.
Since we currently have only histograms of contrast for each quadrant and not contrast
maps of all the shutters as a function of position, we cannot include this dynamic range
effect in our modeling in detail. For this reason, and since it is a somewhat distinct effect
from source crowding, we leave this issue aside for later analysis.
7.0 Operational Consequences
We have considered the limits of NIRSpec effectiveness in dense fields; the density of
sources is a limiting but not prohibiting factor until the source density reaches very high
values, sush as 10000 per square arcmin within a few of degrees of the Galactic center.
However, there are some notable features of these dense fields that need to be taken into
account when planning these observations. The basic strategy suggested for dense fields
places the MSA at a single position on the sky and then observes all (or most) of the
targets that fall within open shutters or acceptance zones at that position. As shown above
this total number can exceed several thousand available sources. To obtain spectra for all
these sources, approximately 50 MSA configurations are needed (lower panels of Figures
5 and 7); we’ll assume this number here for the purposes of discussion. The key point
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about these configurations is that they all refer to the same “pointing” - that is, they do
not require dithering to place targets within open shutters. So our first implication is that
observing with multiple configurations should be possible within a single visit (or a
single MSASPEC template), even if small-angle motions are not specified between them.
For these dense fields it might also be desirable to vary the exposure time to assign longer
exposures to the subset of the 50 configurations that cover stars with a range of
magnitudes within the same field, so that the bright stars get, say, 5 min while the faint
stars get 20 min. Implementing this strategy would require the capability to assign unique
exposure parameters to each of the MSA configurations within a template / visit.
Depending on the number of configurations and the desired S/N, the total of exposure
times and overheads (including MSA reconfigurations) could exceed the approximately
10000 sec interval over which a NIRSpec target acqusition (TA) is still considered valid.
If so, additional TAs will need to be inserted by APT at these breakpoints, even though
the nominal pointing of the observations has not changed.
8.0 Conclusions
This report has analyzed the possible performance of the NIRSpec MSA in regions of the
sky with extreme stellar density, on the order of one or more target every 10 - 100
shutters. Source densities of this order occur in, for example, globular clusters or the
Galactic bulge within 5° of the center. Using the ACS mosaic of Omega Cen as a
concrete test case, we have calculated the efficiency of source coverage by operable
shutters and losses of efficiency caused by inoperable shutters, low-contrast shutters,
source crowding, and restricted shutter transmission.
For realistic magnitude distributions, we find that NIRSpec can still be used effectively at
all source densities studied here except within ~3º of the Galactic bulge, where there are
more than 104-5 sources per square arcminute, or one for every few shutters. This result
holds almost independently of the magnitude of the science targets, since the rate of
crowding contamination is provided by all sources down to the magnitude limits of the
surveys and so affects equally science targets of all brighter magnitudes. While
inoperable shutters, source crowding, and low-contrast shutters all reduce the efficiency
with which the MSA can cover a dense field, even after these loss terms are accounted
for the total efficiency can be sufficient to obtain hundreds or even thousands of spectra
in these dense fields. The density of sources also has benefits: a large number of spectra
can be obtained without repointing the telescope since a large fraction of shutters cover
targets for any given pointing. So for science cases that require only statistical sampling
of stars in the dense region, rather than a particular set of stars, NIRSpec and the “nodither” strategy defined here can be quite effective.
Even though source crowding and spoiler sources do not present insurmountable barriers
to NIRspec MSA observations in dense fields, there may be other practical difficulties
that will hinder these observations and which should be examined more carefully. More
study is needed of the “spoiler cancellation” technique that could extend the dynamic
range of the MSA in dense fields or any other field containing bright stars and faint
targets of interest. The needs for open, clear “background shutters” and their spatial
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relationship to the target shutters should be investigated more thoroughly to ensure that
the background data meet basic calibration requirements; the ad hoc background strategy
proposed here may not be adequate if the resulting background shutters are far from the
corresponding targets. Other operational impacts should be followed up, such as whether
it is possible to hold the pointing of the telescope stable long enough to observe 50+
MSA configurations or whether a new target acquisition should be performed. Successful
resolution of all these issues will be needed to optimize the operations and performance
of NIRSpec in these challenging but compelling regions of the sky.
9.0 References
Böker, T., & Tumlinson, J. “NIRSpec Operations Concept Document”, JWST-OPS003212
Brown, T. M., et al. 2009, AJ, 137, 3172
Muzerolle, J., 2009, “Optimization Strategies for the NIRSpec MSA Planning Tool”,
JWST-STScI-001851
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