2. In the second step one selects a value for the fudge factorλ, e.g.,λ= 0.001.
3. In the third step one solves the equation (4.20) forδaand then estimates theχ2(a+ δa).
4. In the last step one evaluates the χ2(a+δa).
(a) Is theχ2(a+δa)≥χ2(a) then the factor λis increased by a reasonable factor of e.g., 10 and then the procedure goes back to the third step.
(b) Is theχ2(a+δa)< χ2(a) then the factor λis reduced by a reasonable factor of e.g., 10 and the trial solutiona←a+δaupdates. After this one goes back to the third step.
It is necessary to define a boundary condition to stop the iteration process. The boundary condition for the 2D wavelength solution is a fractional change of the χ2(a) of 0.001 or less.
4.6 Summary
The data reduction pipeline for the blue channel works fully automatically and stably. To control the reduction flow, log files are created during the reduction. In case of an error in some positions in the reduction flow, a message is written to a file. Using the error and log files the astronomer can check the reduction flow.
It is also possible that the astronomer uses this pipeline manual and checks the results after the individual reduction steps.
The pipeline is implemented in IDL and therefore the astronomer can modify the pipeline and the sub-procedures very simply and quickly. Furthermore, the HEROS pipeline is very flexible and so it can be easily supported.
Although, the pipeline works automatically and is stable there are two main problems in the data reduction: One problem in the reduction pipeline is the identification of faint outliers in the science image. An outlier correction is implemented in the procedure slit func.pro (Sect. 4.4.10.4). The accuracy of this correction depends on the size of the swath width. With the small swath width, which is used for the pipeline, the accuracy of the outlier correction is not very high. Furthermore, the continuum normalisation process of late type stars may have problems in finding the quasi-continuum segments of the spectrum.
A future task will be the development of a data reduction pipeline for the red channel. In general, this pipeline will have the same structure as the pipeline for the blue channel.
Finally, the optimisation and regular support of the both pipelines is important to obtain the best possible outputs.
Chapter 5
Air pressure, temperature and humidity dependence of the wavelength solution
Dependences of the wavelength solution on air pressure and air temperature were observed for the UVES (UV-Visual Echelle Spectrograph) (Dekker et al. 2000) and HARPS (High-Accuracy Radial velocity Planetary Searcher) (Mayor et al. 2003) spectrograph. The UVES spectrograph is not pressurised or temperature-stabilised and Dekker found a line shift of 84 m/sec/hPa and -219 m/sec/K (Dekker et al. 2000). They conclude, that the variations in the line position are caused by variations of the refractive index of air inside the instrument. Mayor observed a line shift of 100 m/s per mbar for HARPS, if HARPS does not operate in vacuum (Mayor et al. 2003). A dependence between the wavelength solution dependent on the air pressure was also found for the STELLA I spectrograph (private communication Granzer).
To check the air pressure and air temperature stability of the wavelength solution at the HEROS spectrograph, I used 121 ThAr spectra.
5.1 Results
I estimated the pixel shift between the spectra of each order with a reference ThAr spec-trum using a cross correlation. The reference ThAr specspec-trum is used for the automatic wavelength calibration.
I found, that the pixel shift is correlated with the air pressure, the air temperature and also the humidity. The average multiple linear correlation coefficient is 0.7987±0.0004.
In the following, the individual correlations of the air pressure, the air temperature and humidity are investigated.
In Fig. 5.1-5.3, the pixel shift of the relative order 6 vs. air pressure, air temperature and humidity is shown for example. The solid line in the Fig. 5.1-5.3 is a linear fit to estimate the slope. One sees in all three plots a correlation between the shift and air pressure, air temperature and humidity. To check the significance of these correlations, the t-Student test is used. With this test, the independence between two samples is tested. From the correlation coefficient r and the number of degrees of freedom, equal to n-2, where is n the
Figure 5.1: The pixel shifts of the rela-tive order 6 vs. the air pressure.
Figure 5.2: The pixel shifts of the rela-tive order 6 vs. the air temperature.
Figure 5.3: The pixel shifts of the rela-tive order 6 vs. the humidity.
number of measurements, the t value Eq. 5.1 (Bronstein et al. 2001) can be estimated.
t = r√ n−2
√1−r2 (5.1)
The value oftcutoff, for which one can consider a significant correlation between the pixel shift and air pressure, air temperature and humidity is ≥ 3.38 for a significance level
≥ 99.9% (Bronstein et al. 2001). The average correlation coefficient r and average t-Student value are given in Table 5.1. The average t values show that the significance level
|r| |t| Air pressure 0.592±0.001 8.01±0.02 Air temperature 0.304±0.002 3.48±0.02 Humidity 0.360±0.001 4.21±0.01
Table 5.1: The average correlation coefficient r and average t-Student value
5.1 Results 65
for a correlation between the pixel shift and air pressure, air temperature and humidity is higher than 99.9%. The average slopes are given in Table 5.2. In the column 2 of Table
<b [∆pix]> <b [m/sec]>
Air pressure -0.0168±0.0001 [∆pix/hPa] -67.5±0.4 [m/sec/hPa]
Air temperature -0.0135±0.0002 [∆pix/◦C] -54.2±0.8 [m/sec/◦C]
Humidity 0.0070±0.0001 [∆pix/%] 28.1±0.4 [m/sec/%]
Table 5.2: The average slope in [∆pix] and [m/sec]
5.2, the slope is shown in pixel shift and in column 3 the shift is converted to a velocity shift, with a ∆λ of 0.067 ˚A per pixel at 5000 ˚A. The individual offsets and the slopes of each order are shown in the Fig. 5.7-5.12. Additionally, I checked the correlation between the air pressure, air temperature and humidity.
A correlation between air pressure and air temperature is shown in Fig. 5.4. On the other hand, the air pressure is not correlated with the humidity, see Fig. 5.5. Finally, the air
Figure 5.4: Air pressure vs. air temper-ature
Figure 5.5: Air pressure vs. humidity
Figure 5.6: Air temperature vs. humid-ity
temperature vs. humidity is shown in Fig. 5.6. Here clearly exists a correlation. In Table 5.3, the correlations values r and t-value are given. The correlation is a strongest between humidity and air temperature and on the other hand the humidity and air pressure
Air pressure Air Temperature Humidity r | |t| r | |t| r | |t| Air pressure 1.0
Air temperature -0.33|3.8 1.0
Humidity 0.02|0.2 -0.75|12.6 1.0 Table 5.3: The correlation coefficient
are not correlated. The air temperature and air pressure are correlated slightly. These result were not unexpected, see Fig. 5.4-5.6. Next, the dependence of the air pressures, air temperatures and humidities for each order are investigated. Therefore, the offsets and slopes of the linear fits between the shifts and air pressures, air temperatures and humidities for each order are plotted and the trends are calculated, see Fig. 5.7-5.12. The slopes of these trends are given in Table 5.4.
Slope Offset of the shift air pressure trend (1.3±8.0)·10−3
Slope of the shift air pressure trend (-1.7±7.8)·10−6 Offset of the shift air temperature trend (0.1±2.5)·10−4
Slope of the shift air temperature trend (-3.9±0.9)·10−5 Offset of the shift humidity trend (-1.9±0.3)·10−3 Slope of the shift humidity trend (2.4±0.5)·10−5
Table 5.4: The slope of the offset and slope trends in the Fig. (5.7-5.12)
The offset and slope of the relation between pixel shift and air pressure are constant because the slope of these trends is consistent with 0. Furthermore, the slope in the offset of the pixel shift air temperature relation is constant. The slopes of the air temperature shift, humidity shift and humidity shift are not constant.
That implies, that the pixel shift according to air temperature and humidity also depends on the position on the CCD and so on the order.