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Skynet Algorithm for Single-dish Radio Mapping. I. Contaminant-cleaning, Mapping, and Photometering Small-scale Structures

  • Authors: J. R. Martin, D. E. Reichart, D. A. Dutton, M. P. Maples, T. A. Berger, F. D. Ghigo, J. B. Haislip, O. H. Shaban, A. S. Trotter, L. M. Barnes, M. L. Paggen, R. L. Gao, C. P. Salemi, G. I. Langston, S. Bussa, J. A. Duncan, S. White, S. A. Heatherly, J. B. Karlik, E. M. Johnson, J. E. Reichart, A. C. Foster, V. V. Kouprianov, S. Mazlin, and J. Harvey

2019 The Astrophysical Journal Supplement Series 240 12.

  • Provider: AAS Journals

Caption: Figure 14.

Top row: data from Figure 13 background-subtracted, with 6 (left), 12 (middle), and 24 (right) beamwidth scales (the map is 24 beamwidths across). Bottom row: data from the top row minus the data from Figure 13 (residuals). Background-subtracted data are biased neither high nor low. To first order, the noise level of the background-subtracted data is ≈98.0% (left), ≈98.8% (middle), and ≈99.3% (right) that of the original data, and the rms of the residuals is only ≈20.1% (left), ≈15.4% (middle), and ≈12.3% (right) of the noise level of the original data (see Figure 15 for second-order effects). Locally modeled surfaces (Section 1.2.1; see Section 3.7) have been applied for visualization only.

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