Abstract
Thomas had already reduced spectroscopic data with both MIDAS and IRAF. It was never in question that his choice would be these professional tools. However, since he had to run Linux, of course a second computer was needed. He installed the other operating system on a second PC and he also bought a switch to use only one keyboard and monitor. This configuration sometimes crashed, though, but there was no better solution. Thomas did not yet know virtual machines which can emulate a LINUX-MIDAS environment…
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Notes
- 1.
IRAF is just as powerful as MIDAS but has a different task structure. Both programs were developed to satisfy all requirements for reliable astronomical data reduction. MIDAS was developed by the Image Processing Group at ESO in Garching whereas IRAF was written by the IRAF programming group of the National Optical Astronomy Observatories (NOAO ) in Tucson, Arizona. Both packages use line commands.
- 2.
The unit “ADU” is Analog Digital Unit. For a 16-bit CCD amplifier the CCD dynamics is therefor 216 = 65,536 ADU.
- 3.
This behavior is known as “law of large numbers” .
- 4.
The linear behavior should be examined for any CCD camera with real data series.
- 5.
If the time behaviour of the dark level is not linear, it is possible to estimate the appropriate function with many measurements of different exposure times for data reduction.
- 6.
One can in principle approximate any function \(f(x) \equiv I(\lambda )\) (here our spectrum), which is sufficiently often differentiable, at each point λ 0 as the sum of a power series: \(I(\lambda ) = I(\lambda _{0}) + \frac{I^{{\prime}}(\lambda _{0})} {1!} (\lambda -\lambda _{0}) + \frac{I^{{\prime\prime}}(\lambda _{0})} {2!} (\lambda -\lambda _{0})^{2} +\ldots +\frac{I^{n}(\lambda _{0})} {n!} (\lambda -\lambda _{0})^{n} + R_{n}(\lambda )\). This so-called Taylor series is the associated power series \(I(\lambda ) =\sum _{ k=0}^{\infty }\frac{1} {k!}I^{k}(\lambda _{0}) \cdot (\lambda -\lambda _{0})^{k}\). The remainder \(R_{n}(\lambda ) = \frac{f^{n+1}(\lambda _{0})} {(n+1)!} (\lambda -\lambda _{0})^{n+1}+\ldots\) becomes smaller and converges to zero with higher orders. I(λ) is thus a function of the type \(I(\lambda ) = a_{n} \cdot x^{n} + a_{n-1} \cdot x^{n-1} +\ldots +a_{0}\). The powers of the individual summands thus represent the “flexibility” of the individual terms and the coefficients defining their appropriate weighting. This means that any function I(λ) can be arbitrarily closely approximated by a polynomial series. The more complex is I(λ), the more terms need to be selected in increasing order. One might therefore map each spectrum one-to-one by a series expansion, as long as one considers adding infinitely many terms. The remainder R n (λ) would then be zero and we would unnecessarily also fit the spectral noise.
- 7.
Professional data reduction tools like MIDAS and IRAF offer this fitting procedure.
- 8.
Alternatively sometimes combinations of LEDs of different colors are proposed. However, it should be noted that these sources have typical spectral bands which can be as wide as an entire echelle order. In addition, the combined fluxes of all flat LEDs must cover the entire echelle range, which is hardly feasible in contrast to halogen lamps with an almost black body curve.
- 9.
Neon lamps also show a number of lines in the blue wavelength range that are suitable for calibration. However, they are much weaker than the lines in the red range and the exposure times must be adjusted accordingly.
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Eversberg, T., Vollmann, K. (2015). Data Reduction . In: Spectroscopic Instrumentation. Springer Praxis Books(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44535-8_12
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