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the spectroscopic pipeline for the fors instruments can handle long - slit and multi - object spectroscopic data using slitlets . in order to be as flexible as possible most information is obtained directly from the observed calibration data , minimizing the need for assumptions ( for details of the underlying principles see the contribution by izzo et al . ) .
a first guess of the positions and lengths of the slitlets ( or long slit ) is obtained from an arc lamp frame ( see fig .  [
moehler : fig : arc_raw ] ) .
the positions of arc lamp spectra on the ccd are determined by a pattern matching technique applied to the detected emission lines .
for that an estimate of the linear dispersion as well as a line catalog ( which may both be provided by the user ) are required .
adjacent slitlets with no offsets in dispersion direction are not distinguished and will form one longer slitlet .
the spatial curvature is determined by tracing the edges of the corresponding flat field spectra ( cf .
[ moehler : fig : spat_curv ] ) when possible .
the curvature is fit by default with a second order polynomial .
the final wavelength calibration is obtained taking into account the spatial curvature of the spectra .
the pipeline provides the following output calibration files :    * slit positions on the ccd at the central wavelength of the grism * coefficients for the dispersion relation and the spatial curvature * master bias * master flat field ( normalized and not - normalized ) * wavelength map and spatial map * reduced arc lamp frame * spectral resolution and line widths    the wavelength map specifies at the position of each original ccd pixel its corresponding wavelength .
thus the user can avoid the re - sampling of the spectra usually done after the wavelength calibration .
the spatial map provides in the same manner the position of the ccd pixel within its associated slitlet .
the reduced arc lamp frame allows to judge the quality of the calibration ( straight arc lines , no wriggles , no offsets ) . if successful ( see sect .
[ moehler : sec : weak ] ) the pipeline achieves an accuracy of 0.1 pixel both in spatial and dispersion direction .
the default reduction of spectroscopic science data is as follows : the data are first corrected for bias .
then the extraction mask derived above ( i.e. the positions of the slitlets and the curvatures of the resulting spectra ) is applied to the science data ; they are flat - fielded and re - mapped eliminating the optical distortions .
afterward they are rebinned to constant wavelength steps .
the wavelength calibration can be adjusted using sky emission lines .
this allows to correct for shifts between night - time science and day - time calibration data .
such shifts ( of the order of 1 pixel ) are known to happen due to flexure for mxu observations .
finally object spectra are detected and extracted , together with the corresponding error spectra and sky spectra .
for long - slit spectroscopy the sky background is determined using a median .
this is a valid approach if not more than half of the pixels contain flux from objects _ and _ there is no spatial gradient in the sky background . for multi - object spectroscopy instead the sky is subtracted before remapping , i.e. when the spectra are still in the original ccd coordinate system .
the sky is determined with a robust linear fitting , which allows for a linear spatial gradient in the background .
also in this case , however , not more than half of the pixels may contain flux from the object(s ) .
the output science files are    * positions and widths of object spectra * extracted object spectra , error spectra , and sky spectra * unmapped ( corrected for spatial distortions , not rebinned ) science frames * mapped ( i.e. corrected for spatial distortions and rebinned ) science frames * adjusted dispersion coefficients and wavelength map
the major strength of the new pipeline is its flexibility and robustness .
it requires only very limited input information ( mainly dispersion estimate and line catalog ) and can therefore be applied to a large variety of instrument configurations .
thus it is now possible for the first time to _ automatically _ reduce long - slit ( lss ) and multi - object spectroscopic data ( mos / mxu ) from the two fors instruments for all grisms and both collimators ( standard and high - resolution ) . to verify its flexibility
the pipeline was applied to data from the low - resolution spectrograph of the hobby eberly telescope , which it handled without any problems .
the pipeline does have problems if there are too few arc lines , e.g. for slitlets with large offsets .
in such cases it sometimes fails to correctly identify the slitlet .
this can be most easily recognized by comparing the not - normalized and the normalized screen flat ( cf .
[ moehler : fig : bad_mos ]  one slitlet is partly missing , another one was not processed at all ) .
large gaps between arc lines can be problematic as non - linear dispersion terms can become important , while the pipeline currently uses only linear dispersion estimates for the pattern matching .
the pipeline will fail in case of regularly spaced arc lines , like those created by a laser comb , as its pattern matching ( see contribution by izzo et al . )
does not work for such data .
in order to check the quality of observational data a pipeline is of great importance . otherwise it is difficult to distinguish between well - known and correctable instrument effects and real problems .
quality control shall ensure that the data observed with the forses can be calibrated .
in addition the trending of certain parameters allows to have an eye on the instrument health ( see http://www.eso.org/dfo/quality for more details ) .
therefore the resolution , central wavelength , and number of identified arc lines are regularly monitored .
a change in the number of identified lines can indicate reduced flux of the arc lamp and possibly impending failure of the lamp .
a change in resolution could indicate focus problems .
079@xmath0 0@xmath1007 with respect to chip 1 ( the known value being 0@xmath1083 ) . the reduced arc lamp image corresponding to the coefficients in the dashed box
is shown in fig .
[ moehler : fig : pccx_bad ] ]    the situation for the quality control of fors data has vastly improved with the new pipeline , as now almost all data can be reduced ( with polarimetric data being the only exception ) .
moreover , the new pipeline also provides additional information on the instrument .
for example , the combination of the curvature coefficients can be used to monitor grism alignment : the coefficients c1 and c2 describe the slope and the curvature of a slitlet , respectively . if the curvature is 0 , then the slope should be 0 as well  otherwise this indicates that the grism is not well aligned .
[ moehler : fig : coeff ] shows the coefficients obtained for the 600z fors2 grism .
obviously the correlation for the majority of solutions passes through zero .
some solutions , however , show significant offsets from the general trend .
[ moehler : fig : pccx_bad ] shows a reduced arc lamp image for such a deviating case ( marked by the dashed box in fig .  [ moehler : fig : coeff ] ) . obviously the wavelength calibration obtained for several slitlets is rather bad ( wriggles instead of straight lines ) .
thus diagrams like fig .
[ moehler : fig : coeff ] allow to look for bad solutions efficiently in case of large data volume , which may prohibit checking all solutions individually .    .
]    other new parameters become available if there are more than 12 slitlets distributed across the ccd . in this case
the pipeline calculates a global distortion model , which contains good indicators for the monitoring of the instrument s health , like the instrument scale .
the new spectroscopic pipeline for the fors instruments has been a great success in that it allows to reduce _ automatically _ long - slit and multi - object spectroscopic data for all fors grisms . the fact that it does not need a first guess from an instrument model makes it a promising candidate for the reduction of spectroscopic data from a variety of instruments .
in addition this approach is extremely helpful in the presence of instrument instabilities as it allows to reduce data whose configurations deviate from the expected one ( e.g. due to instrument aging or earthquakes ) .
i would like to thank c. izzo and u. hopp for their valuable comments on this manuscript .
since october 1 , 2006 , spectroscopic data from the two fors instruments have been reduced with a new pipeline , which is based on a bottom - up calibration approach . 
 i give a short description of the pipeline and discuss first experiences with automatic data reduction using this software , which has significantly increased the percentage of processed data for both instruments . 
 i will also describe possible new options for quality control .