NIR spectroscopy of the Sun and HD20010
Key Words.:data reduction: high resolution spectra – stars individual: HD20010 – stars individual: Sun
Context:Effective temperature, surface gravity, and metallicity are basic spectroscopic stellar parameters necessary to characterize a star or a planetary system. Reliable atmospheric parameters for FGK stars have been obtained mostly from methods that relay on high resolution and high signal-to-noise optical spectroscopy. The advent of a new generation of high resolution near-IR spectrographs opens the possibility of using classic spectroscopic methods with high resolution and high signal-to-noise in the NIR spectral window.
Aims:We aim to compile a new iron line list in the NIR from a solar spectrum to derive precise stellar atmospheric parameters, comparable to the ones already obtained from high resolution optical spectra. The spectral range covers to , which is equivalent to the Y, J, H, and K bands.
Methods:Our spectroscopic analysis is based on the iron excitation and ionization balance done in LTE. We use a high resolution and high signal-to-noise ratio spectrum of the Sun from the Kitt Peak telescope as a starting point to compile the iron line list. The oscillator strengths () of the iron lines were calibrated for the Sun. The abundance analysis was done using the MOOG code after measuring equivalent widths of 357 solar iron lines.
Results:We successfully derived stellar atmospheric parameters for the Sun. Furthermore, we analysed HD20010, a F8IV star, from which we derived stellar atmospheric parameters using the same line list as for the Sun. The spectrum was obtained from the CRIRES-POP database. The results are compatible with the ones found in the literature, confirming the reliability of our line list. However, due to the quality of the data we obtain large errors.
Effective temperature (), surface gravity (), and metallicity ([M/H], where iron is normally used as a proxy) are fundamental atmospheric parameters necessary to characterise a single star, as well as to determine other indirect fundamental parameters, such as mass, radius, and age from stellar evolutionary models (see e.g. Girardi et al., 2000; Dotter et al., 2008; Baraffe et al., 2015). Precise and accurate stellar parameters are also essential in exoplanet searches. Planetary radius and mass are mainly found from lightcurve analysis and radial velocity analysis, respectively. The determination of the mass of the planet implies a knowledge of the stellar mass, while the measurement of the radius of the planet is dependent on our capability to derive the radius of the star (see e.g. Torres et al., 2008; Ammler-von Eiff et al., 2009; Torres et al., 2012).
The derivation of precise stellar atmospheric parameters is not a simple task. Different approaches often lead to discrepant results (see e.g. Santos et al., 2013). Interferometry is usually considered as an accurate method to derive stellar radii (e.g. Boyajian et al., 2012), however, is only applicable for bright nearby stars. Asteroseismology, on the other hand, reveals the inner stellar structure by observing the stellar pulsations at the surface. From asteroseismology it is possible to measure the surface gravity and mean density, and therefore calculate the mass and radius (e.g. Kjeldsen & Bedding, 1995).
A key parameter for the indirect determination of stellar bulk properties is the effective temperature. In that respect, the infrared flux method (IRFM) have proven to be reliable for FGK dwarf and subgiant stars. However, the IRFM needs a priori knowledge of the bolometric flux, reddening, surface gravity, and stellar metallicity (Blackwell & Shallis, 1977; Ramírez & Meléndez, 2005; Casagrande et al., 2010).
Finally, the use of high resolution spectroscopy along with stellar atmospheric models is an extensively tested method that allows the derivation of the fundamental parameters of a star (see e.g. Santos et al., 2013; Valenti & Fischer, 2005). The procedure depends on the quality of the spectra, their resolution, and wavelength region. For low resolution spectra () it is preferred to fit the overall observed spectrum with a synthetic one (see e.g. Recio-Blanco et al., 2006). For higher resolution spectra of slowly rotating stars (below 10 to 15 ) we are in the regime where the equivalent width (EW) method can be used (for details see Sect. 2).
The derivation of stellar atmospheric parameters from high resolution spectra in the optical is now based on a standard procedure (see e.g. Valenti & Fischer, 2005; Sousa et al., 2008). With the advancement of high resolution NIR instruments, we will now be able to use a similar technique as used in the optical part of the spectrum (see e.g. Meléndez & Barbuy, 1999; Sousa et al., 2008; Tsantaki et al., 2013; Mucciarelli et al., 2013; Bensby et al., 2014). At the moment, the GIANO spectrograph installed at TNG is already available (Origlia et al., 2014), as well as the IRD spectrograph installed at Subaru (Kotani et al., 2014). Three new spectrographs are planned for the near future: 1) CARMENES for the telescope at Calar Alto Observatory (Quirrenbach et al., 2014) had first light at December 2014, 2) CRIRES+ at VLT (Follert et al., 2014) with expected first light in 2017, and 3) SPIRou at CFHT (Delfosse et al., 2013; Artigau et al., 2014) with expected first light in 2017 as well. The spectral resolutions for these spectrographs range between and .
Even though reliable line lists for the derivation of stellar parameters using optical spectra exist, the situation is different in the near-IR regime. There exists a few, e.g. Önehag et al. (2012); Origlia et al. (2013); Rhodin (2015), for the synthesis method and the large general compilation by Meléndez & Barbuy (1999). Moreover, there are line lists compiled in the NIR for the APOGEE survey (see e.g. Smith et al., 2013; Shetrone et al., 2015). Thus, in this paper we want to explore the possibility to create a line list of iron lines in the NIR which can be applied for FGKM stars optimized for the EW method in a consistent way as it is currently done for these stars in the optical (FGK only). The paper is organized as follows: In Sect. 2 we present how to compile a line list and the method for deriving parameters with the equivalent width method for an iron line list. In Sect. 3 we present the results for the derived parameters for the Sun and HD20010. Lastly, we discuss our results in Sect. 4.
The two most widely used methods for deriving stellar atmosphere parameters from a spectrum are spectral synthesis and the EW method. The spectral synthesis method compares synthetic spectra to an observed spectrum and finds the best model by a minimization procedure (see e.g. Valenti & Fischer, 2005; Önehag et al., 2012; Blanco-Cuaresma et al., 2014). When the minimization procedure reaches a minimum, the final atmospheric parameters are found.
The equivalent width (EW) method (see e.g. Sousa et al., 2008; Mucciarelli et al., 2013; Bensby et al., 2014), which we use in this work, is based on the measurements of EWs from a list of lines combined with the matching atomic data. The EW for a single line is given as:
where is the continuum level and is the flux as a function of wavelength.
Using this method, we obtain the abundance of individual lines by the radiative transfer code MOOG (Sneden, 1973, version 2013) under the assumption of local thermodynamic equilibrium (LTE). To obtain metallicity, we expect every spectral line of the same element to produce the same abundance. In our analysis, we use neutral iron (Fe i) and single ionized iron (Fe ii) as a proxy for the metallicity. The effective temperature and surface gravity are derived from the principles of ionization and excitation equilibrium (see Gray, 2005).
A disadvantage of the EW method, may be a miscalculation of the EW. This can have the source in a misplacement of the continuum level, which leads to and over- or underestimation of the EW for the given line. Another source of error is contamination with either telluric lines or other neighbouring lines. The relative error is typically larger for the weak lines. In this work we will focus at the spectral region covered by the Y, J, H, and K bands, which covers more than .
2.1 Compiling the line list
To compile the line list we used the VALD3 database (Piskunov et al., 1995; Kupka et al., 2000). First, we downloaded a list of all iron lines present in the near infrared region, covering to . In total, iron lines were found in this spectral region ( Fe i lines and Fe ii lines). Many of these lines are too faint to be detected in a spectrum of a solar type star. A spectrum of the Sun was downloaded from the BASS2000 web page111The web page can be found here: bass2000.obspm.fr/solar_spect.php to select the best lines for this analysis. The NIR part of the spectrum were obtained from the Kitt Peak telescope (Hinkle et al., 1995) at a resolution of at to at . The spectrum was downloaded in the highest possible resolution at a given wavelength. The signal-to-noise ratio of the spectrum varies from 3000 at down to 1400 at .
We use the ARES software222The ARES software can be found here: http://www.astro.up.pt/~sousasag/ares/. The following settings were used: lambdai=7500, lambdaf=54000, smoothder=4, space=2.0, rejt=0.995, lineresol=0.07, and miniline=2.(Sousa et al., 2007, 2015) to automatically measure EWs of all the lines. Since the first version of ARES expect a 1D spectrum with equidistant wavelength spacing, the solar spectrum was interpolated to a regular grid with constant wavelength step of . This did not change the appearance of the spectrum, and hence not the EW. The EWs are measured by fitting Gaussian profiles to spectral lines. For a given line, ARES outputs the central wavelength of the line, the number of lines fitted for the final result, the depth of the line, the FWHM of the line, the EW of the line, and Gaussians coefficients for the line.
Once this step was done we then selected a subset of lines using the following criteria:
If the number of fitted lines by ARES for a given line is higher than 10, this line is rejected because it is believed to be severely blended.
If the EW is lower than for an absorption line, the strength is too low and it may be difficult to see the line in spectra with low signal-to-noise ratio or a spectrum with many spectral features.
If the EW is higher than for a given line, the strength is too high and we can no longer fit the line with a Gaussian profile, since the absorption line no longer has a pure Gaussian profile.
If the fitted central wavelength is more than away from the wavelength provided by VALD3, the line will also be rejected to avoid false identification.
After the automatic removal of lines following the above criteria we reduced the number of lines to 6060 and 2735 for Fe i and Fe ii, respectively.
2.2 Visual removal of lines
A visual inspection of the lines is necessary at this point in order to allow us to select only the best lines. The best lines we define as lines which are not blended, and therefore reliable EW measurements can be made.
In this step we analyzed in detail small wide spectral windows around each line. For each spectral window, the corresponding absorption lines for all elements were downloaded from the VALD3 database. The location of these lines were plotted on top of the solar spectrum, and any iron line was excluded if a line of another element was present at the same wavelength. Iron lines were also excluded when the absorption line was severely blended by other spectral lines. Many of the removed iron lines at this step have high excitation potential, compared to the final line list, since these lines are generally weaker than those with lower excitation potential. After this step we were down to 593 Fe i lines and 22 Fe ii lines.
For some spectral regions it was not clear which element or elements caused an absorption line. In these cases the iron lines were marked for further investigation with synthesis explained below.
2.3 Synthesis of selected lines
Lines from all elements in a window around an iron line marked for further investigation were used to make a synthetic spectrum. The synthetic spectra were made with MOOG with the synth driver. We use an ATLAS9 atmosphere model (Kurucz, 1993) with the following nominal solar atmospheric parameters: , , and to resemble the Sun. We used 3 different iron abundances for the synthesis. One with solar iron abundance, the second with 0.2 dex above solar and the third with 0.2 dex below solar. We consider a solar iron abundance of 7.47 as presented in Gonzalez & Laws (2000). This choice of solar parameters and iron abundances was done to match the ones already used by our team in previous papers (see e.g. Santos et al., 2013, and references therein) and thereby provide consistency within our group. If the synthetic spectra shows variation at the absorption line of interest with respect to the different iron abundances, then it is likely to be an iron line. We also changed abundances of other elements in the proximity to see if our line is blended with other elements. An example of these plots can be seen in Fig 1.
Sometimes more than one iron line might be present with very similar wavelengths so they can no longer be resolved. In order to find the iron line which is creating the observed absorption line, one of the two were excluded from the line list for the synthetic spectra. If this removed (either fully or partially) the absorption line in the synthetic spectra, then it will be the cause for the observed absorption line, otherwise we excluded the line from the line list presented in this work.
A few times two iron lines had identical wavelengths and excitation potential. In those cases the were combined (sum of the -value) to create a single line that can be analyzed with our method. We ended up with 414 and 12 lines of Fe i and Fe ii, respectively.
2.4 Calibrating the line list: astrophysical gf values
The iron abundances for each line were calculated using the same solar atmosphere model as described above for synthesis. This step allowed us to remove possible outliers based on the assumption that errors in the values from the VALD3 database would never lead to variations of the derived iron abundance of more than 1 dex. All the Fe i lines before recalibration of the oscillator strength and removal of lines which deviates more than 1 dex are presented in Fig. 9. Note that we only removed Fe i lines here, since the Fe ii lines are sparse and essential to determine the surface gravity when we reach ionization balance, as explained in Sect. 2.6. After removal of 1 dex outliers we are down to 319 and 12 lines, for Fe i and Fe ii respectively.
After the removal of lines from the complete VALD3 line list we recalibrate the oscillator strength of the lines () in order to match the adopted solar abundance, an inverse solar analysis. This allows to perform a differential analysis for other stars. Similar approaches have been done by Sousa et al. (2008); Önehag et al. (2012); Rhodin (2015). In Fig. 2 the EWs of the iron lines present in the Sun are plotted as a function of the excitation potential. This plot shows the distribution after recalibration of after the cut for lines with abundances deviating more than 1 dex from the solar value. The majority of the iron lines are found in H band as shown in Fig 3.
2.5 Removal of high dispersion lines
To chose which line-derived abundances are less prone to errors caused by the uncertainties in the EW measured, we decided to do the following test. A Gaussian distribution is made for the EW of each line. We use the width for the Gaussian distribution following the formula presented in Cayrel (1988) below:
where and we consider a signal-to-noise ratio of 50, much lower than the signal-to-noise ratio of the spectrum. This width is used to create a Gaussian distribution with a mean around the original EW.
We make 100 draws for each line and derive the abundance with solar parameters, using the same atmospheric model as described above. For each line we calculate the mean absolute deviation (MAD). The MAD values are plotted against the original EWs in the upper part of Fig. 4. We see a clear trend towards weaker lines, which is expected since a small absolute change in the EW result in a large relative change in abundance, hence a high MAD value. However, this does not mean the abundances of these lines have a high dispersion. Therefore, we detrend the data with a fitted exponential. The residuals are shown in the lower part of Fig. 4. We use the detrended data as a measurement for the dispersion of a given line. A single point above is then removed iteratively until there are no longer any points above this threshold. In this process we remove 33 lines. The final line list is presented in Table 1.
|Wavelength ||Element||EP ||EW |
2.6 Deriving parameters with the EW method
Once the EWs have been measured for all iron lines in the line list (or as many as possible), the next step is to derive the atmospheric parameters. Atmosphere models are necessary for computing abundances of the lines. The literature offers the possibility to choose from a wide variety of model atmospheres. Models like ATLAS9 (Kurucz, 1993) and MARCS (Gustafsson et al., 2008) have been the preferred atmosphere models for derivation of spectroscopic parameters for FGK stars.
We use the ATLAS9 models which, for efficiency, are created in a grid according to effective temperature, surface gravity, and metallicity. In order to search for final parameters it is necessary to interpolate models from the grid, thus allowing to look into a finer grid space (see e.g. Sousa, 2014). This grid of atmosphere models have been used extensively by our group which allow us to work consistently over multiple wavelength regions (optical and NIR).
For a given atmosphere model, abundances of all the lines in the line list are calculated. By removing any correlation between the excitation potential and abundance of all lines (from same element) the effective temperature is constrained. In a similar way, the microturbulence can be constrained by removing any correlation between the reduced EW () and iron abundances, and the surface gravity is found when there is ionization balance, i.e. the mean abundance of Fe i and Fe ii are equal. Lastly, the iron abundance comes from calculating the mean of all the iron abundances.
When there is no longer any correlation, the final atmospheric parameters are obtained from the last atmosphere model.
In order to find the best atmosphere model, a minimization algorithm is used based on the downhill simplex method (Press et al., 1992) which searches in the parameter space for the best fitting atmospheric model, i.e. the best parameters. The convergence criteria for the correlation between excitation potential and abundances is a slope lower than 0.001. A slope lower than 0.002 for the correlation between the reduced EW and the abundances, and a difference of less than 0.005 between the mean abundances for Fe i and Fe ii as used in e.g. Sousa et al. (2008) and Tsantaki et al. (2013).
The error estimate is based on the same method presented in Gonzalez & Vanture (1998). The uncertainty in is determined from the standard deviation in the slope of abundance versus reduced equivalent width, the uncertainty of is determined from the uncertainty in the slope of abundance versus excitation potential in addition to the uncertainty in , the uncertainty in the iron abundance is a combination of the uncertainties in , and the scatter of the individual Fe i abundances. The uncertainty in the surface gravity is based on the uncertainty in and the scatter in Fe ii abundances.
3.1 Derived parameters for the Sun
We derived the stellar atmospheric parameters for the Sun using the resulting line list (including the solar calibrated astrophysical values). We used the minimization procedure described in Sect. 2.6. Since the line list and values have been selected using the solar spectrum, it is with no surprise that the derived parameters for the Sun perfectly match the adopted solar values within the error bars as seen in Table 2.
Moreover, we derive parameters for different signal-to-noise ratios, namely 25, 50, 100, 150, 225, and 300. The signal-to-noise ratios are obtained by drawing EW from a Gaussian distribution with widths dependent on the EW itself and the signal-to-noise as described above in Sect. 2.5. For each considered signal-to-noise, we make 10 random line lists, giving us a total of 60 line lists. This exercise shows the expected precision for different signal-to-noise ratios with the proposed line list. The final results are presented in Fig.5. The error bars represent the standard deviation from the 10 different runs. As seen from the figure, we expect to derive precise parameters (effective temperature more precise than , surface gravity with a precision of 0.1 dex, iron abundance with a precision of 0.05, and microturbulence with a precision of 0.3) down to a signal-to-noise ratio of 50. At higher signal-to-noise ratios the precision increases. E.g. at a signal-to-noise ratio at 100 the erros are reduced by a factor of 2. The results can also be seen in Table 2. This shows that the line list is fully reliable for the whole range of signal-to-noise ratios considered, even if the precision decreases at lower signal-to-noise ratios as expected.
3.2 Derived parameters for HD20010
For testing our new line list we search for a well studied solar type star. The spectrum for such a target needs to be available in the NIR at both high resolution and high signal-to-noise. An ideal place to look for such a star is the CRIRES-POP database (Lebzelter et al., 2012). Here, the best target for testing is HD20010, a F8 subgiant star. This star has been part of many surveys and is therefore well studied. Different parameters from the literature are listed in Table 3.
|Favata et al. (1997)||…||…|
|Santos et al. (2004)|
|Gonzalez et al. (2010)|
|Ramírez et al. (2012)||…|
|Mortier et al. (2013)||…||…|
The data available at CRIRES-POP are in the raw format and pipeline reduced, while three small pieces of the spectra are fully reduced on the web page333http://www.univie.ac.at/crirespop/data.htm. The data is in the standard CRIRES format with each fits file including four binary tables with the data from the four detectors. In the future, the final reduced data will be presented by the CRIRES-POP team. In contrast to the pipeline reduced data, this will be of higher quality, a better wavelength calibration, and telluric correction. We measured the EWs of the pipeline reduced spectra, and where there was an overlap with the fully reduced spectrum, we measured both as a consistency check. The measured EWs from the fully reduced spectra were consistent with the measured EWs from the pipeline reduced spectra. As mentioned above, we use the Y, J, H, and K bands which are all available for this star. The spectra come in pieces of to . These pieces have overlaps between each other, and we were able to measure the EW for a single line up to five times. Unfortunately, wavelength calibration is a difficult task for CRIRES due to the rather small spectral regions measured on each detector. Each calibration was performed separately for each detector and required the availability of a sufficient number of calibration lines in the respective spectral region. This was not always the case and a default linear solution was applied. A pipeline reduced spectrum shows up as a stretched spectrum if the wavelength calibration is poor compared to e.g. a model spectrum or a solar spectrum. The wavelength calibration does not have any effect of the signal-to-noise ratio, which is generally high for the spectrum of HD20010. The signal-to-noise varies between 200 and 400 for different chunks. The pipeline reduced spectra for HD20010 contains tellurics and the wavelength is shifted in radial velocity. All this combined make the line identification very difficult. Therefore, we developed a software444The software (plot_fits) is open source and can be found here: https://github.com/DanielAndreasen/astro_scripts to proper identify the lines. This software does the following:
Plot the observed spectrum.
Overplot a model spectrum. In this particular case the solar spectrum was used since the atmospheric parameters are close enough, so the sun can serve as a model.
Overplot vertical lines at the location of lines in the list.
Calculate the cross correlation function (CCF) for the telluric spectrum with respect to the observed spectrum, locate the maximum value by a Gaussian fit and use this to shift the telluric spectrum with the found RV.
Do the same as the step above, but for the model.
Shift the lines with the same RV as found for the model/solar spectrum.
The final plot shows the shifted spectra, and the CCFs at the sides. An example of the software in use is shown in Fig. 6. The two RVs are part of the title of the plot.
Once the lines were identified, the EWs were measured with the splot routine in IRAF. The reason not to choose ARES for this task was to visually confirm the identification of the line given the relative poor wavelength calibration. We were able to measure 249 Fe i lines and 5 Fe ii lines compared to 344 Fe i lines and 13 Fe ii lines for the Sun over the whole NIR spectral region. Whenever we had more than one measurement of a line, the average was used for the final EW.
We derived the stellar parameters using the standard procedure (see Sect. 2.6) as done for the Sun. Given the relatively low quality of the spectrum of HD20010 (see below) and due to the fact that it is not corrected for telluric contamination, we make a cut in EW at 5, in order to remove the lines which are mostly affected by contamination from either telluric or other line blends. Additionally, we make a cut in EP at 666We also derived the stellar parameters without any cut in the EP, but the resulting values were always overestimated (e.g., fixing to 4.01 we obtained a temperature of 6660K and metallicity of +0.19 dex).. We make this cut since the Fe i and Fe ii lines usually used for stellar parameter determination in the optical regime are also limited to similar values (see e.g. Sousa et al., 2008). Higher excitation potential lines are also more likely to be affected by non-LTE effects. When deriving the atmospheric parameters, we make a outlier removal in the abundance iteratively until there are no more outliers present. Since we could only measure 5 Fe ii lines, for comparison we also decided to derive parameters using the same method, but fixing the surface gravity to the reference value. The resulting atmospheric parameters and iron abundances are presented in Table 4. The effective temperature, surface gravity, and metallicity agree within the errors with the literature values. Similar parameters are obtained by fixing to the average literature value or by letting it free.
The errors on the atmospheric parameters for HD20010 are quite high compared to what is achievable with other measurements in the literature, as presented above in Table 3. In order to explain these errors, we calculate the abundances for all lines which have at least two measurements of the EW. We then calculate the abundances for the highest measured EW and the lowest. The differences in abundances are presented in Fig. 7. The very large differences (more than 0.1 dex) translates to the high errors in the parameters.
The source of the large errors on the parameters can be seen more directly where abundances are compared to excitation potential or abundances versus reduced EW. Here the dispersion on the abundances can be seen directly, as shown in Fig. 8.
This test strongly suggest that errors in the EWs, likely due to the poor quality of this spectrum, are responsible for the relatively large error bars in the derived stellar parameters. Systematic errors (e.g. due to a possible non-optimal reduction of the spectrum) may be the reason for these large error bars. As the CRIRES-POP team continue their great effort in reducing the spectra optimal, it will be interesting to re-visit this star, once the entire spectrum is fully reduced.
3.2.1 Surface gravity
Although we have derived a consistent value for the surface gravity for HD20010, given the small number of Fe ii lines in the analysis, this value should be considered with caution and of low precision. However, we emphasize that from our experience in using this method (the ionization balance) in the visible, the other atmospheric parameters (, and [Fe/H]) have a low interdependency with the surface gravity. This has been shown by Torres et al. (2012) and more recently by Mortier et al. (2014). Furthermore, with the up-coming results from the Gaia mission we will get precise surface gravity for a large number of stars and thus the best option would be to fix this parameter if necessary.
In this work, we present a new iron line list for the NIR. The quality of the line list plays a key role for deriving atmospheric stellar parameters. While the line list was compiled from a solar spectrum and calibrated for the same, we tested it extensively for the slightly hotter star, HD20010. The first results with this line list are promising. We also show that for a spectrum that contain telluric lines, the best results appear when removing lines with an EW lower than . In the future, the development of new high resolution NIR spectrographs will allow us to obtain more high quality spectra of stars in the whole FGK spectral range, thus allowing us to better test and refine this line list.
Furthermore, it will be interesting to explore the use of this line list to derive parameters for M-dwarf stars using high resolution and high signal-to-noise NIR spectra. M-dwarf stars are especially interesting targets for an exoplanetary viewpoint, since they are prone to form low mass exoplanets (Bonfils et al., 2013). Hence, a precise analysis of the host star’s atmospheric parameters may greatly improve our characterization of the possible exoplanets orbiting these low mass stars.
Lastly, with the up-coming NIR spectrographs as discussed above, this work and future continuation will help the community to derive atmospheric stellar parameters.
Acknowledgements.This work was supported by Fundação para a Ciência e a Tecnologia (FCT) through the research grants UID/FIS/04434/2013 and PTDC/FIS-AST/1526/2014. N.C.S., and S.G.S. acknowledge the support from FCT through Investigador FCT contracts of reference IF/00169/2012, and IF/00028/2014, respectively, and POPH/FSE (EC) by FEDER funding through the program âPrograma Operacional de Factores de Competitividade - COMPETEâ. E.D.M. and B.J.A. acknowledge the support from FCT in form of the fellowship SFRH/BPD/76606/2011 and SFRH/BPD/87776/2012, respectively. This work also benefit from the collaboration of a cooperation project FCT/CAPES - 2014/2015 (FCT Proc 4.4.1.00 CAPES). This research has made use of the SIMBAD database operated at CDS, Strasbourg (France). This work has made use of the VALD database, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna.
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Appendix A Iron abundances before recalibrated gf
It is clear from Fig. 9 that most of the lines taken from VALD3 has bad values. This reinforces our need to use differential analysis also in the NIR.