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The literature on echelle data reduction shows that such inconsistencies are commonly observed and usually handled by rather heuristic interactive procedures.
For both instruments it seems to be the calibration unit that introduces the bulk of the problem through errors in the flat field. We discuss strategies to treat the problem and to remove the inconsistencies before merging the spectral orders with minimal use of interactive, subjective algorithms.
The projection of the spectral orders on the detector over an observing run of 4 nights was stable in the wavelength direction at the level of 0.1 pixel (except for an explained, and meanwhile removed oscillation, due to short-term temperature fluctuations of 1 K in the FEROS room) and in the spatial (cross-order) direction at the level of 0.5 pixel (Figure 1).
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However, the position of the blaze profile in wavelength changed by of the
order of 10 pixels (i.e., of the wavelength) in a highly correlated
way with the changes in spatial direction (Figure 1). These slow temporal
changes apply as well to calibration unit flat-fields as to dome
flat-fields or scientific exposures. Such changes, if not taken into
account when flat-fielding a science frame, lead to inconsistencies at the
level of several percent of the flux in the overlap of spectral orders.
The data suggest a very similar shift of the blaze function over all orders in the case of FEROS, leading to larger overlap mismatch in the narrower orders in the blue spectral region i.e., with larger gradients in the blaze function.
Temporal changes in the blaze profile are detected on shorter time-scales, which is presumably related to the fact that the spectrograph is operating in the dome and not in a controlled room. The changes vary smoothly over subsequent orders, but cannot be represented by a simple small shift in wavelength of the blaze function. This may be related to the fact that the blaze profiles produced by HEROS are not so near to the theoretical predictions than in the case of FEROS: the blaze profiles of different spectral orders are almost identical when expressed in the coordinate
where refers to the spectral order,
to the wavelength and
to
in order
at the peak of the blaze
intensity. De Cuyper & Hensberge (2003) discuss the similar case for FEROS,
but the shape and the width do not scale accurately in the same way for HEROS.
Since flat-fields are commonly taken with each science exposure at
Ondejov (because of the fast low-frequency temporal changes of the
calibration images), efforts to address the order merging problems were
directed to the study of the unblazed science frames rather than
considering the calibration images and the science frames separately, as
in the FEROS case.
In order to visualize the lack of consistency in the order overlap regions more clearly, we present figures where the global wavelength dependency of instrument and object is removed from the separate orders. This step, the normalization of the merged spectrum, comes last in a real data reduction chain.
Figure 2 shows the separate spectral orders and the level of inconsistency in the regions of spectral order overlap. Overplotted is a correction function with identical shape (in pixel space) in all 32 spectral orders, but smoothly varying amplitude. Dividing by this function reduces the inconsistencies in the overlap of spectral orders to well below the 1% level.
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If sufficient attention is paid to understand the calibration unit, in order not to introduce spurious low-frequency patterns in the flat-fielded science data, merging spectral orders becomes a trivial exercise.
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