The Modular construction of the PL requires each pipe to be initiated in sequence, to remove this burden from the LT CS a simple calling program does the work. As recommended by Conroy et al. (1998) the PL is controlled by a C shell script. Steps 1-5 of the script are looped such all the FITS files are operated on individually by each of the 5 steps before the next section of the script is implemented. The remaining steps then operate on the data generated by the first 5 steps as a whole.
# Columns: 1 star no
# Columns: 2:3 x[pixels]:y[pixels]
# Columns: 4 Search radius [pixels]
it is always created from data generated by SExtractor, it contains the brightest 10 objects in the frame that are not saturated and are considered to be stellar, as determined by their FWHMs in both the x and y being equal, and not from the SExtractor star/galaxy classification.
The generation of stars.pos is different depending on whether or not it is a standards frame. Its header is always the same however,
# Columns: 1 star no, 2:3 x[pixels]:y[pixels]
# Columns: 4 Search radius [pixels]
# Columns: 5 Star/galaxy class - 0=gal; 1=star
If the frame to be reduced is a standards frame then the data are extracted from step 3 above. The star number is then the unique PMM catalogue number which identified the star. If the FITS file is a normal observation the data used to generate the file is again taken from SExtractor.
# OPTIMAL INFO: PSF star 8 had FWsHM (0.832508743, 1.46874928).
# OPTIMAL INFO: Rotated at an angle of -1.532930066E-3 degrees from the vertical.
# OPTIMAL INFO: And a search radius of 3. pixels.
# APERTURE INFO: Aperture of 2.81488872 pixels radius.
# APERTURE INFO: 1.40999997 detected photons per count.
# APERTURE INFO: Radius searched for star 5. pixels.
# Row: 1 Filter plus fillers
# Columns: 1 Star
# Columns: 2 Standard Star Flag/PMM Number
# Columns: 3 Companion_flag
# Columns: 4 Star/Galaxy Classifier
# Columns: 5 MJD of observation
# Columns: 6 Airmass
# Columns: 7:8 X:Y [pixels]
# Columns: 9 Optimal Phot. Counts, alternate aperture photometry counts
(line starts with optimal counts)
# Columns: 10 Optimal Phot. Errors, alternate aperture photometry errors
(line starts with optimal errors)
The photometry code produces data for every object it is supplied with, for those objects that cannot be reduced an error code is generated in place of the magnitude and magnitude error in the file, other information flags are also generated. The flag is identified by a minus sign in front of the number. The error/info flags are:
-1.0: Object is too close to frame edge for optimal photometry (error).
-2.0: Object is too close to frame edge for its position to be identified, (optimal,
error).
-3.0: The sky level cannot be fitted for optimal photometry (error).
-4.0: The star is saturated (optimal error).
-6.0: The sky level cannot be fitted for aperture photometry (error).
-7.0: Object is too close to frame edge for aperture photometry (error).
-8.0: The star is saturated (aperture error).
-9.0: Object is too close to frame edge for its position to be identified (aperture
error).
-10.0: If column 3 of the file contains a -10.0, then it shows that the star is isolated
enough for photometry to take place. If the star is contaminated by light
from
another object, column 3 will contain a -5_?, where ? is replaced by the number
of the star which is doing the contaminating (info).
-11.0: If the star being fitted is a standard star its PMM number is located in column
2,
if not column 2 will contain a -11.0.(info).
The photometry step ends the looping section of the script, step 6 onwards operates on the data generated to this point as a whole.
2bar-xbar2)
Column 7, the object magnitude is computed using methods explained in Chapter 2, with the associated error being constructed from data computed and recorded to file in step 6.
Recalling that the magnitude of an object is,
 | (5.38) |
m) associated with this quantity is then derived to be:
 | (5.39) |
I is the error associated with the intensity from the object and is determined as
explained in Section 5.9.5, it is read directly from the photometry file. zp is the error
associated with the zeropoint. This is computed separately for each object at this point in
the program thus:
Having calculated the intercept and gradient of the airmass curve in step 6, the extrapolated
(or interpolated) zeropoint value at a given airmass has an associated error generated thus;
 | (5.40) |
). The covariance of a pair of terms describes how independent
the two terms are, if values of x above 
tend to occur with values of y which are above 
then the covariance is likely to be positive. Conversely if the large values of x are associated
with small values of y the covariance is negative, this is the case for the airmass curve as the
gradient is negatively valued. An anti-correlation will tend to a zero covariance. Barlow (1999)
explicitly points out that if the covariance term is left out of Equation (5.40) then the
computed error is too large. The covariance of two variables has units and is therefore not
instantly useful in comparing correlations between different variable pairs. Therefore a
correlation coefficient can be defined, dividing the covariance by the errors of the variable
pairs producing a dimensionless number. For an application of this technique see Chapters 6,
7 & 8
The zeropoint derived from the MJD calculations explained in Section 5.9.6 has its error generated using the same general formula, these errors are then added in quadrature giving the final error on the magnitude.
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