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Fig. 2 shows the sky coverage of released observations, with the full four-year survey footprint shown for reference. The color coding indicates the version of the pipeline used for processing (see Apercal “Versions applicable to the release” for details), where “None” means no processing was performed (see “Notes on specific observations”). “AMES” refers to observations in the medium-deep footprint, and Fig. 3 shows these repeated medium-deep fields separately as individual observations of a field may be processed by different versions of the pipeline.
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 Fig. 2 The sky coverage of released observations, with the full four-year survey footprint shown for reference. The color coding indicates the version of the pipeline used for processing (see Apercal “Versions applicable to the release” for details). |
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 Fig. 3 A view of the processing for medium-deep fields with repeat visits. The observations are ordered in time-order and the color code refers to the same processing as for the figure above. |
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A machine-readable summary table of these observations can be exported using the VO infrastructure, more details are provided in section “User Interfaces”.
Primary Beam Response
Overview of primary beam shapes for Apertif
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We wish to emphasize that the use of the classic WSRT primary beam correction is not appropriate for Apertif. In addition to the fact that the compound beams can have non circularly symmetric shapes (see Fig. 4), the sizes of the primary beams are different from the classic WSRT. The Apertif front-ends fill the focal plane more efficiently than the old MFFE frontends, leading to a smaller primary beam shape. Fig. 5 shows one set of measured compound beam shapes divided by the classic WSRT primary beam shape. In addition to the elongated shapes (and offsets) visible in outer beams, the Apertif primary beam value is generally smaller than the classic WSRT primary beam value, confirming the smaller primary beam shape for Apertif.
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 Fig. 4 Beam maps for all 40 apertif beams reconstructed from drift scans. Contour levels are: 0.1, 0.2, 0.4, 0.5, 0.6, 0.8. Red contours highlight the 10% and the 50% sensitivity level. These drift scans were measured in September 2019 and channel 7 corresponds to a frequency of ~ 1.363 GHz. |
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 Fig. 5 Compound beam shapes derived from drift scans divided by the classic WSRT primary beam. Contours are: 0.2, 0.4, 0.6, 0.8, 1.0. |
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Drift scan observations are scheduled using the aperdrift code : https://github.com/kmhess/aperdrift
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 Fig. 6 Illustration of drift scan observations. The dots represent the beam centres of the 40 Apertif beams, and the lines represent individual drifts across the field of view of the Apertif footprint. |
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The code to produce the beam maps is available at: https://github.com/apertif/aperPB
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 Fig. 7 Beam maps for all 40 apertif beams reconstructed from drift scans. Contour levels are: 0.1, 0.2, 0.4, 0.5, 0.6, 0.8. Red contours highlight the 10% and the 50% sensitivity level. These drift scans were measured in September 2019 and channel 7 corresponds to a frequency of ~ 1.363 GHz. |
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Beam weights are measured at the start of every imaging observing run and are typically used for 2 weeks in a row. The beam weights define the shape of the compound beams. They depend on the quality of the beam weight measurement, (e.g. RFI at the time of the measurement) and also on the health of the system (e.g. broken elements on the PAFs, dysfunctional antennas). Drift scans are typically measured once per month due to the time intensive nature of the measurement. The beam models derived from drift scans observed at different times typically vary by a few percent (rms of the difference).
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 Fig. 8 Normalised distribution of the pixel by pixel difference between beam maps observed in September 2019 and in October 2019. The rms of the distribution is 0.018. |
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Beam shapes and sizes change across the field of view of Apertif with the central beams being more symmetric and the beams along the edge of the field of view more elongated. Fig. 9 and Fig. 10 show the average beam size (FWHM), and the FWHM along the x and y axis as a function of beam number. Fig. 9 shows the beam size for frequency bin 7 (1.363 GHz) and Fig. 10 shows the same for frequency bin 9 (1.399 GHz).
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 Fig. 9 FWHM as a function of beam number for channel 7 (1.363 GHz). The black line shows the average FWHM when fitting a 2D Gaussian function to the beam maps. The blue line shows the FWHM of the 2D Gaussian along the x-axis (r.a.) and the orange line shows the FWHM along the y-axis (dec). The shape of the CBs is not perfectly Gaussian, but a 2D Gaussian function is a good approximation for the beam shapes within a few percent. |
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 Fig. 10 FWHM as a function of beam number for channel 9 (1.399 GHz). The black line shows the average FWHM when fitting a 2D Gaussian function to the beam maps. The blue line shows the FWHM of the 2D Gaussian along the x-axis (r.a.) and the orange line shows the FWHM along the y-axis (dec). |
Beam sizes change linearly with frequency. The frequency dependence is on average: -2.108e-08 · freq [Hz] + 63.47. This is based on fitting a 2D Gaussian to each beam map at each frequency, taking the average FWHM from the 2D Gaussian fit and then fitting a first order polynomial to the FWHM vs. frequency for each bin. The results were then averaged for 14 different drift scan measurements. Fig. 11 shows the average beam size for each 40 beams as a function of frequency bin for a set of drift scans (grey lines). The dashed black line shows the average fitted line to the data. Some of the beams occasionally show non smooth variation with the beam size (for example beam 7, see also in Fig. 9 and Fig. 10). The cause for this in most cases is due to the effect of RFI in certain frequency bins.
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 Fig. 11 FWHM of CBs as a function of frequency bins. We divide the 150 MHz bandwidth into 10 frequency bins when constructing the CB maps. The grey lines show the average FWHM from the 2D Gaussian fit to each CB, while the dashed black line shows the average fitted line (a and b are the parameters of the line). |
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These all-antenna CB models correspond to the middle frequency of the 150 MHz band and can be scaled further to be used for the HI or polarization cubes.
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 Fig. 12 Top row – the total flux ratio of APERTIF to NVSS and the corresponding GPR. Bottom row – the GPR middle slices along RA and Dec. |
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The key characterization of the primary beam images is to understand the impact they have on the flux scale. Flux validation of continuum images takes an initial look at this using a single medium-deep field to look at the internal consistency of the flux scale and compare to NVSS. With the full primary beam characterization, this can be examined for each compound beam in aggregate over the full data release. While the originally returned primary beam images from the Gaussian process regression match the NVSS flux scale by construction, it is informative to undertake the comparison for the normalized primary beam images as this provides information about any overall differences in the flux scale between Apertif and NVSS (which would also be seen in primary beam images derived from the drift scan approach). Cross-matched sources were filtered to have a deconvolved major axis in the NVSS catalog < 45” and to have a measurement error on the ratio of integrated fluxes between Apertif and NVSS <0.1. Table 1 provides (as a csv file) the median ratio between the integrated fluxes, along with the standard deviation of the flux ratios and the median measurement error on the flux ratio. Table 2 provides (as a csv file) these same values but limited to the inner part of the primary beam images where the response level is ≤50%. The typical value is 1.11 in both regimes, indicating the Apertif fluxes are systematically ~10% higher than those from the NVSS catalog. The Apertif fluxes are expected to be ~2% higher based on a typical spectral index of -0.7 and the difference in center frequency in between Apertif and NVSS. In addition, the NVSS integrated fluxes are catalog flux values, corrected for various biases, while the Apertif integrated fluxes are measured directly from the images and may include calibration and clean biases. This will be examined in more detail in the forthcoming data release paper (Adams et al., in prep).
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The Apertif calibration pipeline Apercal is a combination of different modules, which are usually executed one after another. An overview of the whole reduction pipeline is given in Fig. 13. Each rectangular box represents a single module. The grey boxes encapsulate the astronomical software packages used within the individual modules. Arrows illustrate the data and workflow within the pipeline. The dashed arrows and lines are routines which are currently in development.
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 Fig. 13 Apercal structure diagram |
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Ghosts:
Channels 16 and 48 of each subband have a “ghost”; bad signal in these channels cause a false source to appear at the center of images. Thus, any source identified at the exact center of a pointing should be treated with extreme caution.
Aliasing:
The coarse channelization of the data into subbands uses a filter that does not have a perfectly sharp frequency response. This results in some overlap of response between adjacent subbands. This effect is strongest for channels near a subband edge and also results in a sharp drop in overall response for channels at the subband edges, namely channels 0, 1 and 63 of every subband. Currently, no correction is done for the aliasing. A brute force approach is used to deal with the suppressed signal at the edges of the subbands – the low signal channels are flagged. An offline anti-aliasing filter is under development; when it is available, aliased signals will be removed, and the sharp drop in subband response will be evened out. Until then, we note that aliased signal may occur in the presence of strong HI emission and that 3/64 channels are flagged at full spectral resolution. The impact of this flagging on the spectrally-averaged line cubes is described in the “External comparison” section of the HI validation.
Telescope specific issues:
Due to operational needs (exceptional maintenance) or because of failures (the above mentioned high residual delay issue, tracking issues, extreme RFIs, etc), one or more telescopes could be missing from an observation. Information about specific observations can be found in “Notes on specific observations” below.
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