DISCUSIO NES DE LO S RESUL TADO S

With the aim of judging the performance of the PASS0 difference imaging pipeline on SuperWASP data, we rereduced the 688 SuperWASP images. The rereduction was carried out using the original raw images and calibration frames from the SuperWASP camera. Standard calibrations of debi- asing, dark frame subtraction and flat-fielding were carried out as described in Section 3.1.1. On close inspection of the calibrated SuperWASP images it was noted that the PSF is undersampled with a FWHM on average of around 1.9 pixels and that the images suffer from a strong spatially and time varying sky background. In Figure 4.2, we show the PSF shape as a function of position in the SuperWASP CCD using a 5 by 5 grid of positions taken from a calibrated SuperWASP science image.

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Figure 4.2: A grid of typical star images (PSFs) taken from a corresponding grid of positions on a typical SuperWASP calibrated science image. Each image stamp is 50_×50 pixels.

Figure 4.3: A 300x200 pixel subregion of a typical SuperWASP difference image produced by the PASS0 difference imaging pipeline with no image blurring (test N). Highly complicated residuals at the positions of the brighter stars are clearly visible. We supply a zoom on two of the stars to show the residuals in greater detail.

First attempts at using the PASS0 difference imaging pipeline to produce good quality differ- ence images were fruitless. The undersampled PSF on the SuperWASP images made it impossible to derive an accurate convolution kernel. We experimented with different resampling methods for aligning the images and determined that the resampling method was not the cause of the problems with deriving the kernel. We also tried to construct a stacked reference image from a selection of the best-seeing images in order to increase the signal-to-noise of the reference frame, but this failed to solve the problem.

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Figure 4.4: A typical SuperWASP difference image produced by the PASS0 difference imaging pipeline. The background residuals range from approximately −80 ADU to 100 ADU, which is the intensity scaling used on this image.

In Figure 4.3 we display a section of a difference image produced with the standard PASS0 difference imaging pipeline. You can clearly see complicated residuals at the positions of the brighter stars. The maximum and minimum scaling on the image is -350 to 350 ADU. In Figure 4.4 we show a full frame typical difference image. It now also becomes clear that SuperWASP images suffer from high spatial frequency fringing at the level of∼100 ADU per 50 pixels along with low spatial frequency background variations of the same size.

To attempt to reduce the effect of the undersampling we have taken the approach of blurring each image with a Gaussian convolution kernel. By convolving an image with a Gaussian, we are forcing the image sampling to improve, the amount of which depends on the sigma of the Gaussian used. Since it is not clear what value the sigma of the Gaussian kernel should take, we have tested a grid of values. We also note that because the reference image is required to be the best-seeing image for the technique of difference imaging, we choose to convolve the reference image with a Gaussian that has a smaller sigma than the Gaussian used to convolve the rest of the images.

Since image subtraction is based on the idea of “blurring” the reference image to match the current image, the question arises as to why we pre-blur the reference image with a Gaussian? The reason is that the undersampled nature of the SuperWASP images causes problems for the reference flux analysis of the PASS0 pipeline using DAOFind and DAOPhot. Convolving the reference image with a Gaussian improves the determination of the reference fluxes, which we show later on in Section 4.3.3.

For this test we considered a subset of 198 images from a 3 night period between UTC dates 5th-7th June 2004. This time period includes the most densely covered transit event on the night of 5th June 2004. In Table 4.1 we list the FWHMs of the Gaussians used to blur the reference image and the remaining images, and we include a test V′

where we do not blur the reference image in order to analyse what effect, if any, the blurring of the reference image has on the lightcurve accuracy that we obtain.

For each test, the data are reduced using the PASS0 pipeline and blurring is done just before image alignment to the reference image, but after the derivation of the spatial transformation link- ing the two images. This is to minimise the effect of resampling on a (possibly) undersampled image, but maximise the signal of the stars used for deriving the transformation. The alignment process is very robust since the images are all centred on the same field in the sky and there is very little rotation between images. The blurred reference image is analysed with DAOFind and DAOPhot PSF fitting photometry in exactly the same way as described in Section 3.1.2, except that since the PSFs are circularly symmetric, we use the roundness statisticRbetween the normal limits of -1 and 1 to detect stars. In this way we construct a list of detected stars on the reference image with associated reference fluxes. Difference images are produced by the PASS0 pipeline which are then measured to produce the difference fluxes for each star. When we scale the empiri- cal PSF to the difference image at the position of each star, we also fit a local sky background as a constant, which removes the need to model the complicated differential background visible in the typical difference image in Figure 4.4.

Constructing lightcurves for the SuperWASP data is much simpler than for the PASS0 data since there is just a single reference image. For each object the total fluxFtot = Fref+ ∆f /p (see Equation 3.8) at each time is calculated as in Section 3.1.4 and converted to an instrumental magnitude using Equation 3.9. We find that for our particular choice of reference image, by taking

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Table 4.1: The list of blur tests using Gaussians of different FWHMs.

Test FWHM (pix) of Gaussian FWHM (pix) of Gaussian A B C used to blur the used to blur the (mmag) (mmag) (mmag) reference image remaining images

N No blurring No blurring 33.6 9.86 11.3 P No blurring 0.5 33.4 9.49 11.2 Q 0.5 1.0 26.2 11.0 9.09 R 1.0 1.5 9.96 9.90 7.88 S 1.5 2.0 8.47 11.9 7.04 T 2.0 2.5 7.10 11.2 7.55 U 2.5 3.0 6.78 11.0 8.60 V 3.0 3.5 6.69 10.7 9.68 V′ No blurring 3.5 7.27 10.3 9.72 W 3.5 4.0 6.58 11.8 11.0 X 4.0 4.5 5.91 13.0 12.2 Y 4.5 5.0 5.80 14.3 14.1 Z 5.0 5.5 4.41 16.4 15.3

M0 =19.73 mag, we bring the lightcurves on to the same magnitude scale as the SuperWASP

lightcurves (derived from the 100 brightest star matches).