PhD Thesis (PDF) - Department of Astronomy - University of Virginia

is, these diffuse features are not due to the smearing of point sources.)
3.4 Resolved Sources
3.4.1 Detections
We used a wavelet detection algorithm (the CIAO wavdetect program) with √ 2
scales ranging from 1 to 32 pixels to identify the discrete X-ray source population
on ACIS-S3. Since the wavelet source detection threshold was set at 10 −6 , 1 false
source (due to a statistical fluctuation in the background) is expected in the entire
S3 image. Source detection was first performed on the separate observations to check
their astrometric registration; no significant offset was found. To maximize the S/N,
we analyzed the wavelet detection results from the combination of the two observa-
tions. We required our sources to have wavdetect fluxes determined at the ≥3 σ level
for all analyses except the identification of possible optical counterparts.
At that level, the minimum detected count rate in the 0.3–6 keV band was 2.7 ×
10 −4 counts s −1 ; however, the bright diffuse gaseous emission at the center of the
galaxy makes it difficult to establish a minimum detectable flux over the entire image.
For backgrounds of less than 0.05 counts pixel −1 and off-axis distances appropriate
to the S3 chip, sources with 20 counts should be detected at a roughly uniform
completeness level ( 85%; Kim & Fabbiano 2003). For sources satisfying these
criteria, the minimum detected flux was 4.7 × 10 −4 counts s −1 . To determine source
characteristics other than their flux, we used a local background with an area 3 times
that of each source’s wavdetect region. In a few cases of nearby sources, the source
or background regions initially overlapped. We slightly altered these overlapping
regions, preserving the ratio of areas and the net count rates.
72