We present a single-dish mapping algorithm with a number of advantages over traditional techniques. (1) Our algorithm makes use of weighted modeling, instead of weighted averaging, to interpolate between signal measurements. This smooths the data, but without blurring the data beyond instrumental resolution. Techniques that rely on weighted averaging blur point sources sometimes by as much as 40%. (2) Our algorithm makes use of local, instead of global, modeling to separate astronomical signal from instrumental and/or environmental signal drift along the telescope’s scans. Other techniques, such as basket weaving, model this drift with simple functional forms (linear, quadratic, etc.) across the entirety of scans, limiting their ability to remove such contaminants. (3) Our algorithm makes use of a similar, local modeling technique to separate astronomical signal from radio-frequency interference (RFI), even if only continuum data are available. (4) Unlike other techniques, our algorithm does not require data to be collected on a rectangular grid or regridded before processing. (5) Data from any number of observations, overlapping or not, may be appended and processed together. (6) Any pixel density may be selected for the final image. We present our algorithm and evaluate it using both simulated and real data. We are integrating it into the image-processing library of the Skynet Robotic Telescope Network, which includes optical telescopes spanning four continents, and now also Green Bank Observatory’s 20 m diameter radio telescope in West Virginia. Skynet serves hundreds of professional users, and additionally tens of thousands of students, of all ages. Default data products are generated on the fly, but will soon be customizable after the fact.

Skynet is an international network of over two dozen optical robotic telescopes operated out of the University of North Carolina at Chapel Hill. In response to recently acquired privileges of the 20-meter diameter radio telescope located at Green Bank Observatory, Skynet has developed a new radio image processing software which hosts a number of advantages over traditional radio image processing techniques. The most noteworthy of these advantages include the use of weighted modeling over weighted averaging—the former being a procedure that blurs astrophysical structure as high as 40%—and the use of local, rather than global, modeling to differentiate astrophysical signal from various forms of radio contaminants. Our algorithm also makes frequent use of robust Chauvenet rejection (RCR), a new outlier rejection algorithm. RCR is capable of resolving both accurate and precise mu and sigma values for distributions containing as high as 85% contaminated data, making it particularly advantageous for removing radio contamination. Together, these techniques produce allow our image processing software to produce contaminant-cleaned and photometrically viable images for professional and amateur use. Fig. 1.— Green Bank Observatory 20-meter diameter radio telescope. (Photo credit: GBO)

Sigma clipping is commonly used in astronomy for outlier rejection, but the number of standard deviations beyond which one should clip data from a sample ultimately depends on the size of the sample. Chauvenet rejection is one of the oldest, and simplest, ways to account for this, but, like sigma clipping, it depends on the sample's mean and standard deviation, neither of which are robust quantities: both are easily contaminated by the very outliers they are being used to reject. Many, more robust measures of central tendency, and of sample deviation, exist, but each has a trade-off with precision. Here we demonstrate that outlier rejection can be both very robust and very precise if decreasingly robust but increasingly precise techniques are applied in sequence. To this end, we present a variation on Chauvenet rejection that we call "robust" Chauvenet rejection (RCR), which uses three decreasingly robust/increasingly precise measures of central tendency and four decreasingly robust/increasingly precise measures of sample deviation. We show this sequential approach to be very effective for a wide variety of contaminant types, even when a significant-even dominant-fraction of the sample is contaminated, and especially when the contaminants are strong. Furthermore, we have developed a bulk-rejection variant, to significantly decrease computing times, and RCR can be applied both to weighted data and when fitting parameterized models to data. We present aperture photometry in a contaminated, crowded field as an example. RCR may be used by anyone at https://skynet.unc.edu/rcr, and source code is available there as well.

Based on five years of observations with the 40-foot telescope at Green Bank Observatory (GBO), Reichart & Stephens (2000) found that the radio source Cassiopeia A had either faded more slowly between the mid-1970s and late 1990s than Baars et al. (1977) had found it to be fading between the late 1940s and mid-1970s, or that it had rebrightened and then resumed fading sometime between the mid-1970s and mid-1990s, in L band (1.4 GHz). Here, we present 15 additional years of observations of Cas A and Cyg A with the 40-foot in L band, and three and a half additional years of observations of Cas A, Cyg A, Tau A, and Vir A with GBO's recently refurbished 20-meter telescope in L and X (9 GHz) bands. We also present a more sophisticated analysis of the 40-foot data, and a reanalysis of the Baars et al. (1977) data, which reveals small, but non-negligible differences. We find that overall, between the late 1950s and late 2010s, Cas A faded at an average rate of $0.670 \pm 0.019$ %/yr in L band, consistent with Reichart & Stephens (2000). However, we also find, at the 6.3$\sigma$ credible level, that it did not fade at a constant rate. Rather, Cas A faded at a faster rate through at least the late 1960s, rebrightened (or at least faded at a much slower rate), and then resumed fading at a similarly fast rate by, at most, the late 1990s. Given these differences from the original Baars et al. (1977) analysis, and given the importance of their fitted spectral and temporal models for flux-density calibration in radio astronomy, we update and improve on these models for all four of these radio sources. In doing so, we additionally find that Tau A is fading at a rate of $0.102^{+0.042}_{-0.043}$ %/yr in L band.