Band merging extracted point sources observed in multiple wavelength bands is generally done purely on the basis of positional information in order to avoid photometric biases. Automated merge decisions can be more optimal with better position estimation and more realistic modeling of positional estimation errors. Unfortunately, extraction software often does not provide the most accurate positional information possible, and so post-band merge utilities have been developed and implemented to refine both the source positions and the error modeling. Subsequent band merging of the refined detections improves the completeness and reliability of the multi-band source catalog. Application to Spitzer Space Telescope mapping observations of the Pleiades star cluster demonstrates some aspects of the improved band merging

Fowler, J. W.

Laher, R. R.

English

Caltech Authors - Main

Astronomical Data Analysis Software and Systems XVI
ASP Conference Series, Vol. 376, 2007
R. A. Shaw, F. Hill and D. J. Bell, eds.
Post-Band Merge Utilities Applied to Spitzer Pleiades
Data
Russ R. Laher, John W. Fowler
Spitzer Science Center, California Institute of Technology, Pasadena,
CA, USA
Abstract. Band merging extracted point sources observed in multiple wave-
length bands is generally done purely on the basis of positional information in
order to avoid photometric biases. Automated merge decisions can be more op-
timal with better position estimation and more realistic modeling of positional
estimation errors. Unfortunately, extraction software often does not provide the
most accurate positional information possible, and so post-band merge utilities
have been developed and implemented to refine both the source positions and
the error modeling. Subsequent band merging of the refined detections improves
the completeness and reliability of the multi-band source catalog. Application
to Spitzer Space Telescope mapping observations of the Pleiades star cluster
demonstrates some aspects of the improved band merging.
1. Introduction
Over the last several years at the Spitzer Science Center (SSC), we have de-
veloped a number of software tools for merging point sources extracted from
multiple-wavelength image data acquired by the Infrared Array Camera (IRAC)
and the Multiband Imaging Photometer for Spitzer (MIPS), two of the science
instruments onboard the Spitzer Space Telescope. Basic band merging for be-
tween two and seven instrument channels or bands is provided by the band merge
program1, which uses decision theory, the positions, and positional uncertainties
associated with the multi-band input point-source detections to optimally find
the best set of mutually exclusive merges. In quantitative terms of completeness
and reliability, this approach yields results that are superior to simple general
source association (which uses only positional information to match multi-band
point-source detections). A number of post-band merge utility programs are
also available, which perform various useful functions. Of direct interest to this
paper are the utilities that make use of the band merge output statistics and
ancillary information, in order to refine both the source positions and their un-
certainties, so that a second pass through the band merge program will yield
even better results. There are also utilities to estimate the upper limit of the
flux for detections that are absent from certain bands, to estimate completeness
and reliability based on Monte Carlo methods, and more.
These software tools are now being used by SSC astronomers and legacy
science teams (e.g., Shupe et al. 2007). An effort is currently underway at the
1http://ssc.spitzer.caltech.edu/postbcd/download-bandmerge.html
461
462 Laher and Fowler
Figure 1. Spatial distributions of point sources extracted from the Pleiades
data set for Spitzer IRAC channels 1-4.
SSC to make these software tools available in a GUI environment to astronomers
(Pesenson et al. 2006).
In this paper, we demonstrate some aspects of post-band merge processing
in an application to Spitzer IRAC observations of the Pleiades-star-cluster re-
gion. “Before” and “after” cases are presented for direct comparison, in order
to illustrate the post-band merge improvements.
2. Pleiades Data Set
We processed all four channels of IRAC data from AOR 6526464, which com-
prises mapping observations of a 0.4 × 0.4-degree region in the vicinity of the
Pleiades star cluster. The SSC’s Mopex software generated the mosaics and
point-source extractions, which numbered 7513, 6957, 2019, and 1999 in channels
1-4, respectively. Figure 1 shows that the spatial distributions of the extracted
point sources are fairly uniform over the instrument’s field of view.
3. Detection Position Refinement
The average position offsets computed by post-band merge utility getoff were
subtracted from the detections in IRAC bands 2 through 4 (IRAC band 1 is
the reference frame). The input information used by getoff is from the band
merge program’s offsets between matched detections for band pairs that have
“clean” matches, i.e., no confusion and S/N > 10. The positional uncertainties
were adjusted to give average chi-square values closer to the expectation val-
ues. Table 1 gives outputs from the post-band merge utility statq. The offsets
(AveDX and AveDY) are all improved, except for the AveDY value for the 2-4
band pair, which was already good and remains so. The offsets do not go to zero
because the merge patterns change only slightly. The “after” chi-square values
went closer to their expected values (which are 1 for ChSqX and ChSqY, and 2
for ChSqXY), but this was somewhat limited by the relatively high band-pair
registration uncertainties required to compensate for the non-constant band reg-
istration error over the field. The number of clean merges (NSum) increased for
all but the 2-4 band pair, where higher band-4 uncertainties caused additional
cases of potential confusion, hence fewer clean merges. Another bandmerge run
was subsequently performed on the refined positions and uncertainties.
Post-Bandmerge Utilities Applied to Spitzer Pleiades Data 463
Table 1. Before vs. after mean discrepancies and chi-square values.
B-Pa AveDXb AveDYb StdDevXb StdDevYb ChSqX ChSqY ChSqXY NSum
Before:
1-2 −0.03788 −0.02522 0.21907 0.23411 0.41161 0.46603 0.87565 4743
1-3 0.06738 0.04367 0.33213 0.33471 1.35252 1.29926 2.65969 1170
1-4 0.17040 0.02944 0.50466 0.49079 1.37929 1.22884 2.60548 834
2-3 0.09337 0.03768 0.31196 0.33011 0.95717 0.94334 1.90307 992
2-4 0.18607 0.01456 0.47994 0.47722 2.02209 1.43937 3.47478 1018
3-4 0.06891 −0.01142 0.37556 0.42162 0.87378 0.98559 1.85641 652
After:
1-2 −0.00081 −0.00188 0.20912 0.21273 0.76063 0.94136 1.70134 4751
1-3 0.00456 0.02288 0.33349 0.38245 0.96237 0.98211 1.94875 1185
1-4 0.02165 0.02018 0.51410 0.50644 0.93493 0.93828 1.87326 833
2-3 −0.00301 −0.02370 0.32617 0.36626 0.88277 0.98109 1.86635 995
2-4 −0.00335 −0.02540 0.49740 0.51890 0.72225 0.87970 1.60501 1019
3-4 −0.02308 0.00798 0.38498 0.45440 0.61075 0.71369 1.32363 657
aB-P stands for band pair.
bData units are mosaic pixels (≈ 1.2′′ per side).
4. Discussion of Before/After Cases
Figure 2 shows four different band merge cases that we routinely encountered,
before and after refinement by the post-band merge utilities. The top row of
diagrams in Figure 2 shows before post-band merge processing and the bottom
row shows the corresponding cases after the refined processing.
Case 1. A typical case of a slightly better fit with the same detections
before versus after the post-band merge processing. Detections from all four
IRAC bands were merged both before and after the refined processing (explicit
links are not shown in these diagrams). The processing actually picked up weak
links from band 4 to others in band 1 and band 2, so it has the same result with
a tighter fit, and also supplied warning information in its confusion status.
Case 2. Another typical case like case 1, but here, the refined processing
moves the band-1 and band-2 detections from outside both 1-sigma contours for
bands 3 and 4 to inside of both.
Case 3. Detections from only three IRAC bands were merged before the
post-band merge processing (the merged links are shown in the diagrams as black
lines joining the detection centers), primarily because of badly underestimated
uncertainties in bands 3 and 4. There are two nearby detections in band 1 (not
shown in the diagrams), which are bright, but not nearly as bright as the missed
“before” detection that was included in the merging after the refined processing.
There was, in fact, a total of 19 post-refinement sources containing detections
in all four bands that were missing a detection in the pre-refinement merge.
Case 4. Similar to case 3, but no disturbing detections in the neighborhood,
and the oddball match in this case is from band 3, not band 1.
464 Laher and Fowler
Figure 2. Four different cases for before (top row) and after (bottom row)
refinement by post-band merge utilities. The four IRAC channels or bands
are labeled in each individual diagram. The relative sizes of the one-sigma po-
sitional uncertainty contours of the point-source detections are also depicted.
The spatial scales between each before and after case are the same.
5. Conclusions
Overall, there were 11,371 merged detections before the post-band merge pro-
cessing and 11,330 merged detections afterwards. The slightly lower number of
merges reflects fewer stranded detections left as single-band sources. We found
no cases in which a detection in a given band is lost from a source as a result
of post-band merge processing. This shows that the refined processing does no
harm, at least for this data set.
Our planned future work involves a similar analysis of a much larger data
set, which should have adequate statistics for generating sufficiently accurate
completeness and reliability performance measures.
Acknowledgments. This work was performed at the Spitzer Science Cen-
ter as part of a mission/project managed by Jet Propulsion Laboratory, Califor-
nia Institute of Technology, under a contract with the National Aeronautics and
Space Administration. The authors are grateful to John Stauffer for pointing
out the band merging challenges of the Pleiades region.
References
Pesenson, M., et al. 2006, Proc. SPIE, 6271, Paper No. 627112.
Shupe, D., Evans, T., Fang, F., Lonsdale, C., Surace, J., & Fowler, J. 2007, in ASP
Conf. Ser. 376, ADASS XVI, ed. R. A. Shaw, F. Hill, & D. J. Bell (San Francisco:
ASP), 123


Post-Band Merge Utilities Applied to Spitzer Pleiades Data

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Post-Band Merge Utilities Applied to Spitzer Pleiades Data

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