Papers › Optimal extraction of echelle spectra: getting the most from observations
Optimal extraction of echelle spectra: getting the most from observations
Nikolai Piskunov, Ansgar Wehrhahn, Thomas Marquart
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The price of instruments and observing time on modern telescopes is quickly increasing with the size of the primary mirror. Therefore, it is worth revisiting the data reduction algorithms to extract every bit of scientific information from observations. Echelle spectrographs are typical instruments in high-resolution spectroscopy, but attempts to improve the wavelength coverage and versatility of these instruments results in a complicated and variable footprint of the entrance slit projection onto the science detector. Traditional spectral extraction methods fail to perform a truly optimal extraction, when the slit image is not aligned with the detector columns but instead is tilted or even curved. We here present the mathematical algorithms and examples of their application to the optimal extraction and the following reduction steps for echelle spectrometers equipped with an entrance slit, that is imaged with various distortions, such as variable tilt and curvature. The new method minimizes the loss of spectral resolution, maximizes the signal-to-noise ratio, and efficiently identifies local outliers. In addition to the new optimal extraction we present order splicing and a more robust continuum normalization algorithms. We have developed and implemented new algorithms that create a continuum-normalized spectrum. In the process we account for the (variable) tilt/curvature of the slit image on the detector and achieve optimal extraction without prior assumptions about the slit illumination. Thus the new method can handle arbitrary image slicers, slit scanning, and other observational techniques aimed at increasing the throughput or dynamic range. We compare our methods with other techniques for different instruments to illustrate superior performance of the new algorithms compared to commonly used procedures.
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