The SWIFT BAT Software Guide Version 6.3 30 ... - HEASARC - Nasa

22 CHAPTER 4. INTRODUCTION TO BAT ANALYSIS
Figure 4.5: BAT Detector plane image (DPI) with the bright source Sco X-1 in the field of view.
The mottled texture of the image is the mask shadow pattern. The small triangular patch in the
lower right hand corner is a portion of the array not illuminated by Sco X-1. The black squares
correspond to disabled detectors.
Each detector can be illuminated by many points on the sky; and each point on
the sky illuminates many detectors. This entanglement means that one must use the special
BAT software to do the disentangling.
You cannot “select” photons with spatial regions. The BAT software does not reconstruct
where each individual photon came from. Neighboring sky image pixels are highly correlated,
so it does not make sense to “select photons” within a region. For images, it is preferred to use
the PSF fitting method of the batcelldetect task. For light curves and spectra, batmaskwtevt
is used to select a point of interest on the sky, and batbinevt is used to extract the fluxes.
The noise is gaussian, not poissonian. BAT software provides a background-subtracted
flux for a source. The deconvolution technique produces errors which are gaussian in nature, not
poissonian. This means that any downstream software that you use must be able to cope with
gaussian errors. It also means that you do not need to use “grouping” strategies commonly used
with XSPEC in X-ray astronomy (such as GRPPHA or “setplot group”).
The BAT has a very large field of view. The complete field of view is approximately 120
× 60 square degrees, although sensitivity is lower at the edges. The solid angle is approximately 2
steradians. Since this is a significant fraction of the sky, it is likely that BAT will observe a given
source even when Swift is not pointed at the source. The BAT typically covers ∼75-85% of the sky
on any given day.
The BAT is background dominated. The typical full array background count rate is
about 10,000 to 12,000 counts per second. Even the Crab is background dominated (1 Crab ∼
10% of background). Thus, the most sensitive analysis will involve removing as much background
contamination as possible.