BAT X-ray Survey - I: Methodology and X-ray Identification
M. Ajello, J. Greiner, G. Kanbach, A. Rau, A. W. Strong, and J. A., Kennea

TL;DR
This paper presents a novel application of the Maximum Likelihood method to the BAT X-ray Survey, enabling high-sensitivity imaging of the hard X-ray sky and identification of numerous sources with reduced systematic errors.
Contribution
The study introduces a new image reconstruction technique for BAT data that preserves statistical information and minimizes systematic errors, improving sensitivity to faint X-ray sources.
Findings
Detected 49 hard X-ray sources above 4.5 sigma
Achieved a sensitivity of ~0.85 mCrab in the surveyed region
Identified counterparts for most sources using Swift/XRT and other catalogs
Abstract
We applied the Maximum Likelihood method, as an image reconstruction algorithm, to the BAT X-ray Survey (BXS). This method was specifically designed to preserve the full statistical information in the data and to avoid mosaicking of many exposures with different pointing directions, thus reducing systematic errors when co-adding images. We reconstructed, in the 14-170 keV energy band, the image of a 90x90 deg sky region, centered on (RA,DEC)=105,-25, which BAT surveyed with an exposure time of Ms (in Nov. 2005). The best sensitivity in our image is mCrab or erg cm. We detect 49 hard X-ray sources above the 4.5 level; of these, only 12 were previously known as hard X-ray sources (15 keV). Swift/XRT observations allowed us to firmly identify the counterparts for 15 objects, while 2 objects have Einstein IPC…
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