The Allen Telescope Array Twenty-centimeter Survey—A - CIRA

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– 6 – were flagged a priori because of poor sensitivity due to filter roll-off, so the effective bandwidth was 84.4MHz. 4. Data Reduction As the number of antennas and the volumes of data from radio telescopes increase, calibration, imaging, and flagging RFI by hand start to become impractical. As a result, automated routines are required to get the best results from the data. Data reduction was undertaken using a software suite developed for the reduction of ATA data, RAPID (Rapid Automated Processing and Imaging of Data; Keating et al. 2009). RAPID consists of C shell scripts that call MIRIAD (Sault et al. 1995) tasks. It performs identification and excision of RFI, calibrates the visibilities using a primary flux calibrator, and images the individual pointings. It then performs phase self-calibration and amplitude flagging. RAPID currently operates only on ATA data but is freely available by contacting the authors. Mosaicking, catalog creation, and matching to NVSS are performed by additional custom-written scripts. 4.1. Flagging, Calibration, and Imaging RAPID is able to detect and flag RFI on a channel-by-channel basis by looking for channels that often rise above the mean amplitude for all cross-correlations. RAPID also identifies any problem antennas that show signs of corrupted spectra, and flags them. It then makes an image of the calibrator, using full multifrequency synthesis (to avoid bandwidth smearing), and iteratively tests for phase and amplitude closure, flags data outside of a certain amplitude range, and makes a new image. Onceall baselines fall withinphase RMSandclosure limits, iterationstopsandanimage of the calibrator is produced. Baselines that were determined to be bad in the calibrator data are also flagged in the source data. RAPID then makes images of the source fields, and iteratively applies phase self-calibration and amplitude flagging while attempting to maximizetheimagedynamicrange. Onceamaximumindynamicrangeisachieved, iteration stops - for our images, this results in a median number of CLEAN components per image of 5710, a medianRMSof3.56mJybeam −1 (2.2timesthe theoretical RMSof1.6mJybeam −1 ), and a median dynamic range of 120. Images were made using natural weighting which gives about a factor 3 lower noise than uniform weighting for the ATA. Superuniform weighting was also tried, but the results were unsatisfactory, both in terms of image quality, and much higher noise than natural weighting.
– 7 – 16 15 14 13 Right Ascension Fig. 1.— <strong>The</strong> field centers of the 361 ATATS pointings.
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Page 3 and 4: - 3 - ATATS observations were sprea
Page 5: - 5 - would be likely to be radio s
Page 9 and 10: - 9 - Table 1. ATATS epochs Epoch U
Page 11 and 12: - 11 - Fig. 4.— A typical synthes
Page 13 and 14: - 13 - without some averaging of th
Page 15 and 16: - 15 - Table 2. The ATATS catalog R
Page 17 and 18: - 17 - Fig. 7.— Comparison of the
Page 19 and 20: - 19 - Fig. 8.— Comparison of the
Page 21 and 22: - 21 - to achieve this. We found th
Page 23 and 24: - 23 - We are also able to estimate
Page 25 and 26: - 25 - associated with multiple NVS
Page 27 and 28: - 27 - Fig. 13.— The same data as
Page 29 and 30: - 29 - Fig. 14.— Histogram of the
Page 31 and 32: - 31 - Fig. 16.— Comparison of th
Page 33 and 34: - 33 - Fig. 17.— The six sources
Page 35 and 36: - 35 - in practice we gain somewhat
Page 37 and 38: - 37 - survey at the same frequency
Page 39 and 40: - 39 - Marscher, A. P., Jorstad, S.
Page 41: Snapshot Rate (deg −2 ) 10 1 10 0

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