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3. Data pre-reduction

Before the photometric analysis, the raw data are reduced using the MIDAS environment in the following steps:
bias subtraction, skimming subtraction (for RCA CCD #8), flat-fielding and cosmic-ray removal.

3.1. Bias subtraction

A "master'' bias frame is obtained by averaging over tex2html_wrap_inline2451 50 frames from which cosmic ray events have been removed. As the bias level varies with time, the bias subtraction for each scientific frame is performed in two steps:
(1) Subtraction of the master bias frame.
(2) Subtraction of the mean difference between the overscan of the scientific frame and that of the master bias frame.

3.2. Skimming subtraction

With RCA CCD #8, the raw CCD frames have a skimming pattern, characterized by systematic column intensity offsets across the entire CCD. This pattern varies in intensity at low illumination levels and becomes constant above tex2html_wrap_inline2453 1000 adu, well below the sky level of all our scientific frames but above that for the photometric calibration frames. Because the skimming offsets are additive biases, we can calculate them by using flat-field frames with different illumination levels. Scaling and subtraction of two flat-field frames with different exposure times yield a preliminary skimming frame. As the skimming feature is stable along each column, the signal to noise is improved by replacing each pixel of a column by the mean value along the column. The mean level of the entire skimming frame is then adjusted to be zero. A set of skimming frames derived from pairs of flat-field frames with increasing illumination levels are calculated. Then for each science frame, the appropriate skimming frame can be subtracted. The skimming subtraction is important for the calibration frames where the sky level is very low. To preserve the quality of our photometry, the calibration frames for which we cannot adequately subtract the skimming pattern are rejected.

3.3 Flat-fielding

The flat-field frame provides a map of the sensitivity variations over the CCD chip. This map depends on the spectrum of the incoming light. As the flat-field pattern is stable from night to night, we obtain for each observing run a "super flat-field'' in each filter band by doing a median filtering of all the scientific frames obtained during the run. The median filtering removes the objects from the images and yields the large-scale sensitivity variations. This is the best flat-field frame which can be obtained, because it is derived from the sky on the science frames themselves. The number of frames must be sufficient to create a high signal-to-noise final flat-field frame. In practice, because our fields are sparsely populated, the super flat-field results from the median filtering of 5 to 20 science frames (depending on the observing run). This flat-field is normalized and is divided into each data frame. The large-scale residual variations in the background of each flat-fielded frame are tex2html_wrap_inline2455.

3.4. Summation of multiple exposures

After flat-fielding, we align multiple exposures of identical fields using several unresolved objects, sum the individual exposures and calculate the mean airmass for the final frame.

3.5. Cosmic ray removal

To remove cosmic rays from the summed frame, we apply a filtering algorithm kindly provided by P. Leisy. Each pixel is compared with the mean of the tex2html_wrap_inline2457 neighbouring pixels. If the pixel value differs by more than tex2html_wrap_inline2459 from the mean, it is replaced by the mean value. The value of tex2html_wrap_inline2461 is large enough to prevent the subtraction of real objects.

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