Hochauflösende Spektroskopie von Objekten aus dem CARMENCITA-Katalog. High-resolution Spectroscopy of CARMENCITA Objects

Transcription

1 Master s Thesis Hochauflösende Spektroskopie von Objekten aus dem CARMENCITA-Katalog High-resolution Spectroscopy of CARMENCITA Objects prepared by Patrick Schöfer from Northeim at the Institut für Astrophysik Submission date: 16th October 2015 First referee: Second referee: Prof. Dr. Ansgar Reiners Dr. Sandra Jeffers

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3 Abstract Context. The CARMENES instrument consists of two high-resolution spectrograph covering the visible-light and the near-infrared range. It is designed to find radial velocity variations as low as 1 m s 1 in order to detect Earth-like planets in the liquid water zone of M dwarfs. Aims. To increase our knowledge about the stars in the CARMENES input catalog CARMENCITA, we determine radial velocity, spectral type, and magnetic activity strength from high-resolution spectra. Methods. From cross-correlation of each spectrum and a synthetic spectrum, we derive the radial velocity. Using several spectral indices sensitive to M dwarfs, we calculate the spectral type and thereby estimate the effective temperature. The magnetic activity strength follows from the effective temperature and a measurement of the pseudo-equivalent width of the Hα line. Results. We present the measured quantities of 480 CARMENCITA objects and 41 other stars including several radial velocity standards. The radial velocity measurements revealed 33 new single-lined and double-lined spectroscopic binary candidates. 138 CARMENCITA stars were identified as active. Additionally, we identified problems with the information written to the headers of the spectra files. Conclusions. Our results for the standard stars suggest that our radial velocity measurements are accurate to 1 km s 1, which is more accurate than most of the literature values of CARMENCITA stars. While the spectral types show a trend toward earlier types, the pseudo-equivalent widths of the Hα line are in agreement with the literature values. The identification of active stars and new probable binaries helps with the target selection for the CARMENES survey, because these stars may cause problems for high-precision radial velocity measurements. Key words: stars: activity stars: late-type stars: low-mass iii

4 Zusammenfassung Kontext. Das CARMENES-Instrument besteht aus zwei hochauflösenden Spektrographen, welche das sichtbare Licht und das nahe Infrarot abdecken. Es ist dafür konzipiert, Radialgeschwindigkeitsschwankungen in der Größenordnung von 1 m s 1 zu beobachten, um erdähnliche Planeten bei M-Zwergen in einem Abstand, der die Existenz von flüssigem Wasser zulässt, nachzuweisen. Ziele. Um unser Wissen über die Sterne aus dem CARMENCITA-Katalog der möglichen CARMENES-Ziele zu erweitern, bestimmen wir die Radialgeschwindigkeit, den Spektraltyp und die Stärke der magnetischen Aktivität aus hoch aufgelösten Spektren. Methoden. Mittels Kreuzkorrelation jedes Spektrums und eines künstlichen Spektrums bestimmen wir die Radialgeschwindigkeit. Unter Verwendung einiger für M- Zwerge sensitiver spektraler Indizes berechnen wir den Spektraltyp und schätzen somit die effektive Temperatur ab. Die Stärke der magnetischen Aktivität folgt aus der effektiven Temperatur und einer Messung der Pseudo-Äquivalentbreite der Hα- Linie. Ergebnisse. Wir stellen die gemessenen Größen für 480 Objekte aus dem CARMEN- CITA-Katalog und 41 weitere Sterne vor, darunter einige Standardsterne für Radialgeschwindigkeiten. Die Radialgeschwindigkeitsmessungen zeigten 33 bisher unbekannte spektroskopische Doppelsternkandidaten auf. 138 CARMENCITA-Sterne wurden als aktiv identifiziert. Zusätzlich erkannten wir Probleme mit den Informationen, welche in die Dateiheader der Spektren geschrieben werden. Fazit. Unsere Ergebnisse für die Standardsterne lassen darauf schließen, dass unsere Radialgeschwindigkeitsmessungen auf 1 km s 1 genau sind, was die meisten Literaturwerte für CARMENCITA-Sterne in der Genauigkeit übertrifft. Während die Spektraltypen eine Tendenz zu früheren Typen aufweisen, stimmen die Pseudo- Äquivalentbreiten der Hα-Linie mit den Literaturwerten überein. Die Identifizierung von aktiven Sternen und neuen möglichen Doppelsternen helfen bei der Auswahl der Beobachtungsobjekte für CARMENES, da diese Sterne Probleme bei hochpräzisen Radialgeschwindigkeitsmessungen verursachen können. Stichwörter: aktive Sterne massearme Sterne rote Zwerge iv

5 Non est ad astra mollis e terris via Lucius Annaeus Seneca v

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7 Contents 1. Introduction 1 2. Theoretical Background Stellar Spectra Black-Body Radiation Spectral Lines Spectral Types Effective Temperatures Doppler Shift CARMENES Detecting Exoplanets: The Radial Velocity Method Instrument CARMENCITA Spectrographs CAFE FEROS HRS Data Sample and Analysis Methods Used Data Reduction Normalization Target Identification Further Problems Measurement of Radial Velocities Spectral Typing Determination of an Activity Indicator vii

8 Contents 4. Results Radial Velocities Spectroscopic Binary Detections Spectral Types Hα Emission and Activity Discussion Radial Velocities Spectroscopic Binary Candidates Spectral Types Hα Activity Conclusion 61 A. Long Tables and Figures 63 A.1. List of Spectra A.2. Non-CARMENCITA Objects Result Tables A.3. CARMENCITA Objects Result Tables A.4. RV Measurements of SB1 Candidates A.5. CCF Plots of SB2 Candidates Bibliography 113 List of Figures 125 List of Tables 127 viii

9 1. Introduction After humans had realized that the stars are distant suns, the question arose whether they are also orbited by planets, perhaps even planets populated by creatures asking the same question. While it had been believed for a long time that there was no way to detect these exoplanets, the first discoveries of planets around a pulsar by Wolszczan & Frail (1992) and around a Sun-like star by Mayor & Queloz (1995) led to an increasing interest in this research field and instrumental improvement. Subsequently, the number of confirmed exoplanet detections has grown to nearly 2000 as of September 2015 (Schneider, 2015), with a major part detected by NASA s Kepler mission, which identified more than 3500 exoplanet candidates (Batalha, 2014), many of which yet to be confirmed. In contrast to Kepler, which detects exoplanets by finding periodic variations in the brightness of a star caused by a planet transiting its host star (transit method), the Calar Alto high-resolution search for M dwarfs with Exo-earths with Near-infrared and optical Echelle Spectrographs (CARMENES, Quirrenbach et al., 2014) project carried out by a consortium of German and Spanish institutions will redefine the limits of the radial velocity method, which uses spectroscopy to find a periodic motion of the star with respect to the observer caused in reaction to a planet orbiting the star. Such a motion can be observed as a shift of the spectrum in accord with the Doppler effect, which is well known for changing the pitch of the siren on a passing ambulance. Unlike the transit method, the radial velocity method is not limited to planetary systems with the orbital plane close to the line-of-sight. However, as the radial velocity semi-amplitude of the Sun caused by the Earth is 0.09 m s 1 (Perryman, 2011, sect ), the precision needed to find an Earth-twin around a Sun-like star is still an order of magnitude below the limits of CARMENES. The targets of the 1

10 1. Introduction project are hence red dwarfs or M dwarfs, which are cool stars with a significantly lower mass than the Sun. As they are cooler and thus emit much less energy than the Sun, the liquid water zone, where water is neither vaporized because of high temperatures close to the star nor frozen because of low temperatures far away from the star, is located closer to the star. In addition to the lower mass, the closer liquid water zone increases the radial velocity signal caused by an Earth-like planet to 1 m s 1 for a planet with mass M P 2 MC in the middle of the liquid water zone of a M5 dwarf (Quirrenbach et al., 2014), making it feasible to detect blue earths around red dwarfs with CARMENES. Not all M dwarfs are equally suitable for high-precision radial velocity measurements, because strong magnetic activity or close stellar companions may cause additional radial velocity variations. Therefore, to find the most promising targets, the CAR- MENCITA catalog collecting all relevant information on potential targets has been created. In this thesis, to further improve the data in CARMENCITA, we derive the radial velocity, the spectral type, which is closely related to other parameters like temperature, mass, and radius of the star, and an activity indicator of several hundred CARMENCITA objects from high-resolution spectra obtained from the spectrographs CAFE, FEROS, and HRS. Following a basic introduction to stellar spectra, we outline the CARMENES project and the three spectrographs used to obtain the spectra analyzed for this thesis in chapter 2. In chapter 3, we define our sample, discuss problems with the data, and explain the methods used to analyze the spectra, before we present our results and investigate potential instrumental effects of the different spectrographs in chapter 4. To discuss the quality of our results, we compare them with literature values from various sources in chapter 5. Finally, we sum up our findings in chapter 6. 2

11 2. Theoretical Background 2.1. Stellar Spectra Black-Body Radiation As a starting point for understanding stellar spectra, we assume that a star absorbs any incoming radiation and has a constant temperature. A body fulfilling these requirements is called black body and emits thermal radiation obeying a law derived by Planck (1901). According to Planck s law, the spectral radiance per unit wavelength B λ at temperature T is given by (Karttunen et al., 2007, sect. 5.7) B λ pλ, T q 2hc2 1 λ 5 hc exp λk B T, (2.1) 1 where h the Planck constant, c the speed of light, and k B the Boltzmann constant. Two important observations are suggested by fig. radiance at three different temperatures: 2.1, which shows the spectral at every wavelength λ, a hotter body has a higher spectral radiance than a cooler body with decreasing temperature, the wavelength λ max of the maximum spectral radiance increases The latter observation is described by Wien s displacement law (Karttunen et al., 3

12 2. Theoretical Background 2007, sect. 5.7) λ max T b const., (2.2) where b m K (Mohr et al., 2015) is Wien s displacement constant. Integration of eq. (2.1) over all wavelengths and multiplication with π yields the flux density F, which is given by the Stefan-Boltzmann law (Karttunen et al., 2007, sect. 5.7) F σt 4, (2.3) where σ the Stefan-Boltzmann constant. Therefore, if the star was a black body, we could calculate its temperature from the flux density. While our assumptions are only an approximation, the Stefan-Boltzmann law is still used to define an effective temperature T eff. Spectral radiance B λ [W sr -1 m -2 Å -1 ] T = 4000 K (~K7.0) T = 3000 K (~M5.5) T = 2500 K (~M8.5) Wavelength λ [Å] Figure 2.1.: Spectral radiance according to Planck s law 4

13 2.1. Stellar Spectra Spectral Lines The spectrum resulting from Planck s law is a continuum. However, already in the early nineteenth century, dark lines in the spectrum of the Sun were discovered and described in detail by Fraunhofer ( ) as shown in fig Figure 2.2.: Fraunhofer lines in the spectrum of the Sun (Fraunhofer, ) Similar absorption lines can be observed in spectra of other stars. Quantum mechanics can explain these lines: The electrons in an atom populate discrete energy levels. If the energy E of a photon is equal to the difference between two energy levels of an atom in the stellar atmosphere, it can be absorbed and the atom is raised to an excited state. To get back to the lower energy state, the atom may re-emit a photon of the same energy E in a random direction (radiative de-excitation) or lose the energy in a collision (collisional de-excitation). Overall, less photons with energy E reach the observer, who detects less light with wavelength λ hc E. Atoms may also be excited by other mechanisms than absorption. For example, in cool active stars, strong magnetic heating occurs due to electromagnetic induction as a result of convection and rotation (Reid & Hawley, 2005, ch. 5). de-excitation then causes emission lines, which are thus indicators of activity. Radiative Because different elements have different energy levels, each spectral line can be associated with a transition between energy levels of a certain element or molecule. 5

14 2. Theoretical Background The presence and strength of an absorption line depends not only on the abundance of the element or molecule, but also on the temperature: In a cool star, there are significantly less high-energetic photons according to Planck s law, hence, some transitions cannot occur. In a hot star, however, the atoms may be in a higher energy state or ionized and, therefore, require photons with a different energy for excitation or cannot be excited at all, if fully ionized. While the fixed differences between the energy levels suggest that a spectral line is infinitesimally narrow, there are several effects which influence the line shape, e.g.: natural broadening: According to Heisenberg s uncertainty principle, time and energy are connected by the relation E t h (Heisenberg, 1927). Since the lifetime t of the excited state is not infinite, the uncertainty of the energy is E 0. thermal broadening: The atoms and molecules in the stellar atmosphere move with different velocities following a Maxwell distribution related to the temperature T. Therefore, some atoms and molecules move toward the observer and others move away, causing different Doppler shifts of the spectral lines (see also sect ). pressure broadening: Higher pressure in the stellar atmosphere (i.e. higher surface gravity) leads to more collisions of the atoms or molecules, thereby shortening the effective lifetime of excited states because of collisional deexcitation and increasing the effect of natural broadening. rotational broadening: If the star rotates about an axis not pointing directly to the observer, one part of the star moves toward the observer, while the other part moves away, again resulting in different Doppler shifts and a line broadening. Hence, while we use only the positions and strengths of the spectral lines in this thesis, much information about the star and its atmosphere is contained in the line shapes. Not all spectral lines have their origin in the stellar atmosphere. As the light travels through the Earth s atmosphere, absorption by molecules like water vapor or oxygen 6

15 2.1. Stellar Spectra creates additional lines, which are called telluric lines. Their strength varies with the zenith distance of the observed star and the weather conditions, while due to the temperature of the Earth s atmosphere being T 275 K, the effect of thermal broadening is less pronounced than for lines of stellar origin, hence telluric lines tend to be narrower (Adelman et al., 1996). Telluric lines may cause bad results if they blend with the studied stellar lines Spectral Types Already before understanding how spectral lines are created, the concept of spectral types (also called spectral classes) was invented to categorize stars with similar features in their spectra into groups. In the late nineteenth century, Williamina Fleming classified thousands of stars into classes A to N with decreasing strength of hydrogen lines (Giridhar, 2010). Cannon & Pickering (1901) discarded many of these classes and reordered the remaining classes by color and temperature: O B A F G K M It was believed that the hot blue stars are young and the cool red stars are old, therefore, the adjectives early (toward O) and late (toward M) are traditionally used for spectral types. Usually, the spectral types are subdivided into ten subclasses denoted by arabic numbers from 0 to 9, as also introduced by Cannon & Pickering (1901), and sometimes even further by giving a decimal place, which is commonly rounded to half subclasses. Morgan et al. (1943) extended the system by adding the luminosity classes listed in table 2.1, because the appearance of spectral lines depends also on pressure as described in sect and therefore on surface gravity, which is significantly different for stars of different luminosity classes. Stars of the same luminosity class are in the same populated region of the Hertzsprung-Russell diagram (fig. 2.3), which shows the absolute magnitude versus the spectral type or related quantities. The classification scheme was further revised by William W. Morgan and Philip C. Keenan (Johnson & Morgan, 1953) and is therefore called MK system. While some standard stars defining a subclass have been added or replaced over time 7

16 2. Theoretical Background Ia Ib II III IV V most luminous supergiants less luminous supergiants luminous giants giants subgiants main sequence stars (dwarfs) Table 2.1.: Luminosity classes (Karttunen et al., 2007, sect. 8.3) Figure 2.3.: Hertzsprung-Russell diagram with names of various classes of stars (Arp, 1959) (e.g., Kirkpatrick et al., 1991) and new classes L and T for ultracool dwarfs were introduced by Kirkpatrick et al. (1999), in general, the MK system is still used today. Some subclasses have never been defined by a standard star (e.g. K6, K8, K9) and are thus not commonly used. According to Keenan (1984), some undefined subtypes like G3 are in fact used for interpolation between defined subtypes, hence K6 might be used for stars classified between K5 and K7, while K8 and K9 might be used for stars between K7 and M0. In this thesis, we will nevertheless use the notations K5.5 for stars between K5 and K7 and K7.5 for stars between K7 and M0 to avoid the usage of K6, K8, and K9. 8

17 2.1. Stellar Spectra While spectral classification is originally based on visual comparison of a spectrum with the standard star spectra, a more modern approach is to compute spectral indices, which are, in most cases, flux ratios with a wavelength range around a specific spectral line as numerator and a wavelength range containing only the continuum as denominator. The spectral type is then calculated as a function of one or more spectral indices (e.g., Covey et al., 2007) Effective Temperatures As noted in sect , presence and strength of absorption lines depend on the temperature of the absorbing material. Therefore, the spectral type can be used as an indicator of the star s effective temperature T eff. The effective temperature of a star can be derived by comparing a set of synthetic spectra with the stellar spectrum (e.g., Rajpurohit et al., 2013) or using photometric methods (e.g., Boyajian et al., 2012, Casagrande et al., 2008). If T eff is known for the standard star of a spectral type, it can be assumed that any star of the same spectral type and luminosity class has a similar T eff. Pecaut & Mamajek (2013) have identified the most common standard stars for most spectral types and created a table giving an effective temperature for each spectral type based on several results for the standard stars. We list the late K and M range of their table in table 2.2. SpT T eff [K] SpT T eff [K] K5.0 V 4450 M4.0 V 3200 K5.5 V 4200 a M4.5 V 3100 K7.0 V 4050 M5.0 V 3050 K7.5 V 3950 b M5.5 V 3000 M0.0 V 3850 M6.0 V 2800 M0.5 V 3800 M6.5 V 2700 M1.0 V 3680 M7.0 V 2650 M1.5 V 3600 M7.5 V 2610 c M2.0 V 3550 M8.0 V 2570 M2.5 V 3450 M8.5 V 2510 d M3.0 V 3400 M9.0 V 2450 M3.5 V 3250 M9.5 V 2400 a K6.0 V in Pecaut & Mamajek (2013) b average of K7.0 V and M0. 0V, Pecaut & Mamajek (2013) use K8.0 V and K9.0 V instead c average of M7.0 V and M8.0 V d average of M8.0 V and M9.0 V Table 2.2.: Effective temperatures T eff of late K and M dwarfs (Pecaut & Mamajek, 2013) 9

18 2. Theoretical Background Using Wien s displacement law given by eq. (2.2), we can calculate the wavelength of the maximum spectral radiance λ max. We get λ max Å for a M0.0 dwarf, λ max Å for a M5.0 dwarf, and λ max Å for a M9.5 dwarf. Therefore, M dwarfs emit most light in the near-infrared Doppler Shift If the star and the observer did not move with respect to each other, each spectral line would appear at its rest wavelength λ 0. However, if the star moves toward the observer, the observed distance between two crests of the wave decreases, because a part of the distance between star and observer is covered by the motion of the star. Conversely, the observed distance between two crests of the wave increases if the star moves away from the observer. Since the observed wavelength λ is nothing else than the observed distance between two crests of the wave, a Doppler shift λ λ λ 0 is observed, which is given by (Karttunen et al., 2007, sect. 2.10) λ λ 0 V r c, (2.4) where V r the radial component of the velocity or radial velocity (RV) of the star and c the speed of light. While this effect explains thermal and rotational broadening as noted in sect , furthermore, it causes a shift of all spectral lines and the continuum. If the star moves toward the observer (V r 0), the entire spectrum is shifted toward shorter wavelengths or blue-shifted, while conversely, if the star moves away from the observer (V r 0), the entire spectrum is shifted toward longer wavelengths or redshifted. Since the Earth orbits the Sun with an average orbital speed of VC km s 1 (Morison, 2008, sect ), a significant part of the observed Doppler shift may be caused by the Earth s motion. Hence, the true RV of the star has to be measured with respect to the center of mass (or barycenter) of the solar system by applying a barycentric correction to the observed RV. 10

19 2.2. CARMENES 2.2. CARMENES Detecting Exoplanets: The Radial Velocity Method (invisible) planet observerblue-shifted light center of mass red-shifted light star Figure 2.4.: Sketch of the radial velocity method for exoplanet detection If a star is orbited by a planet, actually both bodies move around their common center of mass. As long as there is an inclination angle i 0 between the orbital plane and a plane perpendicular to the sightline, a periodic perturbation of the radial velocity can be observed, as outlined in fig As a function of the true anomaly ν νptq, which is the angle between the direction of the orbital point closest to the center of mass (pericenter) and the position of the planet at time t, the radial velocity is given by (Perryman, 2011, sect ) V r pνq K rcos pω νq e cos ωs, (2.5) where ω the argument of pericenter, which is the orbital angle of the pericenter relative to the ascending node of the orbit, e the numerical eccentricity of the orbit, and K the radial velocity semi-amplitude which is given by (Cumming et al., 1999) K 2πG P 1 3 M P sin i pm P M q 2 3 1? 1 e 2, (2.6) where G m 3 kg 1 s 2 Newton s constant (Mohr et al., 2015), P the orbital period, M P the mass of the planet, and M the mass of the star. The resulting RV curve V r pνq is shown in fig According to Kepler s second law, the planet and the star travel faster the closer 11

20 2. Theoretical Background planet moving toward observer, star moving away planet farthest from observer, star closest V r [K] star farthest from observer, planet closest star moving toward observer, planet moving away True anomaly ν [ ] Figure 2.5.: Radial velocity variation in units of the semi-amplitude K over one orbital period (ω 0, e ) they are to the pericenter, because the line between the orbiting body and the center of mass sweeps out equal areas in equal amounts of time (Karttunen et al., 2007, sect. 6.5). Hence, νptq is linear in t only for circular orbits and, while V r pνq is always sinusoidal, the observed RV variation over time V r ptq can be significantly non-sinusoidal for eccentric orbits (e.g., Perryman, 2011, sect ). In our solar system, the Earth (MC kg) orbits around the Sun (M@ kg) with a period P 1 year s and a numerical eccentricity e (values adopted from Morison, 2008). If we were observing the solar system edge-on (i 90 ), the resulting RV semi-amplitude of the Sun caused by the Earth would be K 0.09 m s 1, which is below the detection limit of current instruments. On its quest for Earth-like planets with liquid water, the CARMENES survey hence focuses on M dwarfs, because on the one hand, M dwarfs are less massive than solar-like stars, and on the other hand, the liquid water zone is closer to the star, as cooler stars have a lower flux density according to the Stefan-Boltzmann law given by eq. (2.3). Both aspects increase the expected RV semi-amplitude of a planet with mass M P MC to the order of 1 m s 1. 12

21 2.2. CARMENES Instrument As explained in sect , the spectral radiance of M dwarfs peaks in the nearinfrared. Therefore, the CARMENES instrument (Quirrenbach et al., 2014) consists of two separate spectrographs located at the 3.5 m telescope of the Calar Alto Observatory in Southern Spain, one of them covering the wavelength range 5500 Å : Å, which is most of the visible-light range and a part of the nearinfrared, the other one covering the wavelength range 9500 Å : Å, which is most of the near-infrared. Combining both ranges, radial velocities can be measured with an expected precision around 1 m s 1. Both spectrographs are fiber-fed échelle spectrographs (see sect. 2.3) with a resolving power of R The optical layout of the visible-light spectrograph is shown in fig While the design of the near-infrared spectrograph is as similar as possible, the different wavelength ranges require some differences. Firstly, the visible-light spectrograph operates at room temperature, but the near-infrared spectrograph needs to be cooled to 140 K to avoid contamination of the spectrum by thermal radiation of the environment (Amado et al., 2012). Secondly, the CCD used to record the visual spectrum is not sensitive in the infrared. Therefore, a HgCdTe detector is used in the near-infrared. Lastly, a UNe hollow-cathode lamp is used for absolute wavelength calibration in the near-infrared part, while a ThNe lamp is used in the visual part. Figure 2.6.: Sketch of the optical layout of the CARMENES visible-light spectrograph (Seifert et al., 2012) 13

22 2. Theoretical Background CARMENCITA We already motivated the search for Earth-like planets with focus on M dwarfs in sect Instrumental parameters and the required radial velocity precision impose additional constraints on the target selection, as the target has to be observable with the telescope and other reasons than a planet may cause RV variations. To collect the relevant information about the potential target stars, the CARMENes Cool star Information and data Archive (CARMENCITA) was created using several sources as listed by Alonso-Floriano et al. (2015). Because the geographic latitude of the Calar Alto Observatory is ϕ 37, any star with declination δ 23 does never reach a zenith distance z 60 1 and the air mass (Karttunen et al., cos z 2007, sect. 4.5), which is the distance the light has to travel through the Earth s atmosphere in units of the thickness of the atmosphere, is therefore always greater than 2. Hence, to avoid strong atmospheric effects, no star with declination δ 23 is included in CARMENCITA. Only stars with a J-band magnitude brighter than 11.5 mag are included because fainter targets require too much exposure time to collect a sufficient amount of light. Additionally, this limiting magnitude is lowered by 0.5 mag per subtype for stars with a spectral type earlier than M6, because the early-type M-dwarfs are brighter in general and the observation of only the brightest stars per spectral type saves exposure time for late-type targets. As shown in fig. 2.7, the CARMENCITA stars are assigned to classes Alpha, Beta, and Gamma according to their spectral types and J-band magnitudes. All stars with an optical or physical companion at an angular separation less than 5 arcseconds are classified as Delta, because the light of the close companion may contaminate the spectrum and binary stars may show periodic RV variations due to the components orbiting each other. Poor quality of RV measurements may also be caused by broad lines due to fast rotation as explained in sect and by artificial RV variations due to starspots (e.g., Reiners et al., 2010). Therefore, active stars need to be excluded. CARMENES will observe three sub-samples of 100 stars each, consisting of latetype ( M4), mid-type (M3 M4), and early-type ( M3) M dwarfs, which are likely selected from the Alpha class in CARMENCITA. 14

23 2.3. Spectrographs Alpha Beta Gamma Delta 9 J [mag] M0.0 M1.0 M2.0 M3.0 M4.0 M5.0 M6.0 M7.0 M8.0 M9.0 CARMENCITA SpT Figure 2.7.: Classification of CARMENCITA objects by spectral type and J-band magnitude as of 18 Jun Spectrographs The purpose of a spectrograph is to separate light into different wavelengths. This is achieved with either a prism or a grating, the latter option being much more common in modern instruments. An important parameter of a spectrograph is the resolving power R λ, where λ is the smallest resolvable wavelength difference λ at wavelength λ. The resolving power of a grating with N illuminated grooves is given by (Kitchin, 2003, sect. 4.1) R Nm, (2.7) where m the observed order of the spectrum. For high-resolution spectroscopy, it is therefore useful to observe in high spectral orders. Échelle gratings are optimized to achieve their maximum efficiency in high orders by a stepped design instead of a planar grating, the steps being tilted at a high blaze angle θ B 45. To avoid 15

24 2. Theoretical Background overlapping orders, a prism or another grating is used as cross-disperser, which separates the orders creating a two-dimensional spectrum as shown in fig Figure 2.8.: Spectrum of σ Vir on 10 May 2014 obtained with CAFE Both CARMENES spectrographs (sect ) and the three spectrographs CAFE, FEROS, and HRS used to obtain the spectra analyzed in this thesis (sect ) use échelle gratings. In a data reduction process, several instrumental effects are eliminated and the one- 16

25 2.3. Spectrographs dimensional spectrum F pλq is restored from the two-dimensional échelle spectrum F px, yq. A CCD converts the incoming photons to electrons and adds a constant offset to avoid loss of information from pixels with a low signal during read-out. It is possible to isolate this bias and an additional read-out noise by recording an image with closed shutter and exposure time t exp 0 s, as shown in fig The bias has to be subtracted from every science image. Additional noise due to thermal electrons (dark current) can be reduced by cooling the detector. If the dark current is significant, it can be isolated by recording an image with closed shutter and an exposure time similar to the exposure time of the science images. Light of different wavelengths travels through the spectrograph on different paths. Therefore, there may be wavelength-dependent losses of light in the optical system and pixels on the edges of the detector receive the light at a different angle than pixels in the center, causing the center to be more illuminated than the edges (vignetting). In addition to large-scale sensitivity variations of the detector, these effects are eliminated by flat-fielding. The science images are divided by a flatfield image as shown in fig. 2.10, which is obtained using a bright lamp with wavelengthindependent flux. Figure 2.9.: CAFE bias image Figure 2.10.: CAFE flatfield image 17

26 2. Theoretical Background After flat-fielding, the individual spectral orders have to be extracted and calibrated. In order to find the wavelength λpx, yq corresponding to each pixel, it is common to use a spectrum of a calibration lamp with emission lines at known wavelengths obtained with the same instrument. Finally, the wavelength-calibrated orders are merged to the one-dimensional spectrum F pλq CAFE The Calar Alto Fiber-fed Échelle spectrograph (CAFE) is located at the 2.2 m telescope of the Calar Alto Observatory in Southern Spain. Fig shows the optical layout of the spectrograph, which according to Aceituno et al. (2013) covers the wavelength range 3960 Å : 9500 Å in 84 orders with a resolving power of R It is designed to measure radial velocities of stars with V magnitudes down to 14 mag with a precision of m s 1. Figure 2.11.: Sketch of the optical layout of the CAFE spectrograph (Aceituno et al., 2013) 18

27 2.3. Spectrographs FEROS The Fiber-fed Extended Range Optical Spectrograph (FEROS) is an échelle spectrograph located at the 2.2 m telescope of the European Southern Observatory in La Silla, Chile. According to Kaufer et al. (1999), it has a resolving power of R and covers the wavelength range 3600 Å : 9200 Å in 39 orders. With two fibers, a stellar spectrum and either a sky background spectrum or a ThArNe calibration spectrum can be recorded simultaneously. The spectrograph is estimated to be sufficiently stable for radial velocity measurements with a precision of 21 m s 1. Its optical layout is shown in fig Figure 2.12.: Sketch of the optical layout of FEROS (Stahl et al., 1999) 19

28 2. Theoretical Background HRS Mounted on the 9.2 m Hobby-Eberly Telescope at McDonald Observatory, Texas, the High-Resolution Spectrograph (HRS) is most suitable for observation of the faint, late-type targets. This échelle spectrograph covers a wavelength range up to 4200 Å : Å using two CCD detectors (Tull, 1998). Fig shows the optical layout. Using three different effective slit widths, different resolving powers of R 30000, R 60000, and R are possible. In this thesis, we use spectra with a resolving power of R The wavelength range 6900 Å : 7065 Å is not covered because the corresponding spectral orders are located in the gap between the two CCD detectors. HRS is designed to be sufficiently stable to measure radial velocities with a precision 10 m s 1. Echelle Grating Fold Mirror Entrance Slit Collimator Mirror 1 Collimator Mirror 2 Camera Cross-Dispersion Grating CCD Figure 2.13.: Sketch of the optical layout of HRS (based on Tull, 1998) 20

29 3. Data Sample and Analysis Methods This chapter describes our data sample (sect. 3.1) and the preparation of the spectra for the analysis as well as the methods used to analyse the data. We reduced a subset of the CAFE spectra (sect ), normalized the spectra (sect ), and discovered problems with the target identification (sect ). Additional problems with the spectra are described in sect Using the methods presented in sect , we measured the radial velocity, spectral type, and an indicator of activity Used Data We analyze 1738 spectra of 480 M dwarfs included in CARMENCITA and 43 other stars as described below observed between September 2011 and September 2014, distributed among the three spectrographs described in sect. 2.3 as shown in table 3.1. While we have to reject 38 spectra for reasons discussed in sect , a list of all 1700 usable spectra is given in table A.1. Spectrograph # Spectra # Stars # Nights Period CAFE FEROS HRS Table 3.1.: Number of spectra and stars and observation time span for each spectrograph. Note that some stars have been observed with two spectrographs. In addition to the CARMENCITA stars, our sample contains nine primaries of CARMENCITA objects with a spectral type earlier than M listed under the CAR- 21

30 3. Data Sample and Analysis Methods MENCITA identifier of their companion with suffix A, eight K dwarfs, nine dwarfs with earlier spectral types, one southern M dwarf which cannot be observed with CARMENES, eight bright K and M giants, and six components of close binaries. We use them to check the accuracy of our radial velocity measurements. The remaining two observed stars could not be identified Reduction All CAFE spectra were provided in a raw format and reduced using a modified version of the IDL package REDUCE (Piskunov & Valenti, 2002) including the flatrelative optimal extraction (FOX) algorithm (Zechmeister et al., 2014). Lamert (2014) reduced all spectra recorded before May 2014 and a part of the later spectra, while we reduced the remaining 256 spectra from 26 nights for this thesis. At the beginning and at the end of each observing night, usually 10 bias images, 10 flatfield images, and 10 ThAr calibration lamp spectra as described in sect. 2.3 were recorded. Instead of using only one bias and one flatfield image to eliminate instrumental effects in the stellar spectra, we use a master bias and a master flatfield to exclude the statistical noise which is present in single images. The sumbias procedure calculates the average of two groups of bias images separately. We group the bias images recorded at the beginning and at the end of the observing night, respectively, or split the series in halves if only one series of bias images was recorded. From the difference of the averages of both groups, the read-out noise is determined by fitting a Gaussian function to the distribution. The master bias is calculated as the average of all bias images. For pixels with differences between the two group averages which significantly deviate from the Gaussian distribution, only the lower group average is used. The master flatfield is the average of the flatfield images with the master bias subtracted. To trace the spectral orders, REDUCE searches the master flatfield for clusters of pixels containing a similar signal. Quartic polynomials are fit to the positions of these clusters and clusters with intersecting polynomial fits are assumed to belong to the same order. In our CAFE flatfields, the contrast between the orders and the background is not always sufficient for REDUCE to separate neighboring orders. 22

31 3.1. Used Data Therefore, to avoid orders being blended, we had to increase the contrast by setting a threshold and further lowering the signal value of pixels with a lower signal. However, the background signal between the orders in the brightest part of the spectrum is higher than the signal at the edges of the orders or in fainter orders. Hence, different thresholds are necessary in different parts of the image and the exact values need to be adjusted until all orders are identified correctly. We were able to trace between 80 and 85 spectral orders. Figure 3.1.: Wavelength calibration using a ThAr spectrum and wavecal After subtraction of the master bias, the stellar spectra are divided by the master flatfield and the traced orders are extracted using FOX. Additionally, the ThAr calibration lamp spectra are extracted. Using the wavecal procedure, we compared the ThAr spectra with a template of known spectral lines and identified several lines as shown in fig. 3.1 in every tenth order. Based on our identifications, additional lines in all orders were identified automatically. After rejecting outliers, the wavelength solution λpx, yq was calculated and applied to the stellar spectra. We obtain the final one-dimensional spectrum by merging the orders. While the wavelength ranges of the orders in the blue part of the spectrum overlap and we calculate the mean flux weighted by the error for the overlapping ranges, there are gaps in the spectrum between the orders at wavelengths greater than 7000 Å. 23

32 3. Data Sample and Analysis Methods In contrast to the CAFE spectra, the FEROS spectra were provided in a reduced format by the FEROS Data Reduction System. We use the merged and wavelength calibrated spectra (*.1081.fits). The HRS spectra were re-reduced by Lamert (2014) using raw extracted spectra and Blaze functions obtained from REDUCE and FOX by Mathias Zechmeister Normalization For comparison of line strengths in different spectra, the spectra have to be normalized by dividing the measured flux by the flux of the continuum. We use a polynomial fit to calculate the flux of the continuum. However, this is not possible for M dwarfs, because their spectra do not contain sufficiently wide ranges without spectral lines to find the continuum. Lamert (2014) found that a polynomial of a very high order would be necessary, which then fits not only the continuum but also the absorption bands. Therefore, we use the continuum of a bright reference star observed in the same night to normalize the M dwarf spectra. While this is only an approximation because according to eq. (2.1) the continuum is temperaturedependent, instrumental effects are removed from the spectrum in this way Target Identification For 58 CAFE spectra, the object names do not correspond to the coordinates given in the FITS headers of the spectra files, as already noted by Lamert (2014) for his subset of the sample. Comparing the FITS header information with the handwritten observation logs, we find that the object names in the FITS headers agree with the observed targets according to the logs, whereas the coordinates, if they do not correspond to the observed object, are either the coordinates of the next target or the zenith park position of the telescope (α = local sidereal time, δ ) for the last object of an observing night or somewhere between the coordinates of the observed object and the next target. We therefore conclude that the coordinates in the FITS header are the current coordinates at the time the file is written. However, this problem did not occur in all observing nights. 24

33 3.1. Used Data We provide a list of all spectra with wrong FITS header coordinates and our explanations in table A.3. To illustrate the problem, we calculate the angular distance (AD) and the position angle (PA) of the FITS header coordinates (α header, δ header ) with respect to the real coordinates (α, δ) calculated for the time of observation using the J coordinates from 2MASS (Skrutskie et al., 2006) and the IDL function precess. AD and PA are given by (Meeus, 1998, ch. 17): cos AD sin δ sin δ header cos δ cos δ header cos pα α header q (3.1) tan PA sin pα α header q cos δ header tan δ sin δ header cos pα α header q (3.2) Fig. 3.2 clearly shows the large deviations for the spectra with wrong FITS header coordinates. While we would expect the data points of good FITS header coordinates to be scattered around the origin because we did not take proper motion into account, there appears to be another minor offset. PA=0 100 AD [ ] 80 PA=0 3 AD [arcmin] Figure 3.2.: Angular distances between CAFE FITS header coordinates and calculated coordinates The CAFE FITS headers also contain J coordinates, which appear to be calculated independently from the precessed coordinates, because they are more accurate for some spectra and less accurate for other spectra. Fig. 3.3 shows AD and PA of the J FITS header coordinates with respect to the 2MASS coordinates. Close to the origin, there are two clouds of data points, the closer being only spectra from 2013, while the spectra from 2014 are farther away, which is another sign of a 25

34 3. Data Sample and Analysis Methods systematic coordinates offset. PA=0 100 AD [ ] 80 PA=0 3 AD [arcmin] Figure 3.3.: Angular distances between CAFE FITS header J coordinates and 2MASS coordinates While in general the object names given in the FITS headers agree with the observation log, there are some typographical errors like missing or additional letters and digits, which can easily be identified. In two cases, the observation log gives the wrong star: J ( :08:09): correct name and coordinates in FITS header, but J according to observation log J ( :47:27): correct coordinates, but J according to FITS header and observation log Furthermore, for some nights, both the observation log and the FITS headers give only the right ascension part of the CARMENCITA identifier, which can be ambiguous, especially in case of close binaries. We used the declination given in the FITS header to identify the star if the right ascension matched the given part of the CARMENCITA identifier, or compared the spectrum with other spectra of the potentially observed stars. In contrast to the CAFE spectra, the coordinates given in FEROS FITS headers appear to be reliable, while the object names may contain typographical errors or 26

35 3.1. Used Data be the name of another observed target. Hence, we use the FITS header coordinates to identify the observed star if the object name does not agree with the coordinates. In fig. 3.4, we plot the AD and PA of the FEROS FITS header coordinates, which are given in J2000.0, with respect to the 2MASS coordinates. The data points are equally distributed around the origin, as expected, since proper motion was neglected. PA=0 5 AD [arcmin] Figure 3.4.: Angular distances between FEROS FITS header coordinates and 2MASS coordinates For HRS spectra, the differently formatted object names ( IHHMMm DDMM ) in the FITS headers can easily be converted to CARMENCITA identifiers. While we found no problems using the CARMENCITA identifiers for identification, there is a minor issue with the coordinates given in the FITS headers. The FITS headers give as the equinox of the coordinate system, but if we calculate AD and PA with respect to the 2MASS coordinates, the effect of precession is clearly visible (fig. 3.5), while the data points are scattered around to origin if we calculate AD and PA with respect to the coordinates precessed to the time of observation (fig. 3.6). Therefore, the FITS header coordinates are likely the current coordinates at the time of observation, not J coordinates. 27

36 3. Data Sample and Analysis Methods PA=0 15 AD [arcmin] 12 PA=0 1 AD [arcmin] Figure 3.5.: AD between HRS FITS header and 2MASS coordinates Figure 3.6.: AD between HRS FITS header and calculated coordinates Further Problems 38 spectra could not be analysed or yielded bad results for several reasons as listed in table A.2. Possible reasons include both problems with the spectra themselves and observations of wrong stars. Some spectra have low signal-to-noise ratios (SNR) or contain no signal at all. Since the hand-written CAFE observation logs contain comments on the weather situation, we could identify clouds as a likely explanation for some low SNR spectra. A bad choice of the exposure time t exp caused three spectra to be unusable, while another, usable spectrum of J was obtained immediately after the rejected spectrum with the same t exp according to the FITS header. However, as this star is too faint to obtain a usable spectrum with FEROS in t exp 60 s, we assume that the t exp is wrong in the FITS header. Because J and J were observed only once and both spectra are rejected, the size of our sample is reduced to 480 CARMENCITA objects and 521 stars overall. For other spectra, the analysis revealed radial velocities and spectral types not 28

37 3.1. Used Data matching the results from other spectra of the same stars. In some cases, the observers noticed that a wrong star was observed and wrote a comment into the observation log, hence, we list wrong target as the reason for rejection. In other cases, we could not calculate a spectral type, which indicates that the observed star is an early type star. If there is a close early type companion, we give its name, otherwise, we give (bad SpT) as further explanation in table A.2. In many cases, however, we could identify the observed star as the other companion in a close binary based on both our radial velocity and spectral type result. For example, J W (Ross 486A) and J E (Ross 486B) were confused in an early CARMENCITA version and the FITS headers of spectra obtained before this mistake was corrected consequently contain the wrong identifier. We do not reject these spectra, but treat them like any other spectra of the identified stars. Another problem occured in the reduction process. According to the hand-written observation logs, there was a problem with the ThAr calibration lamp used for wavelength calibration of CAFE spectra in August As shown in fig. 3.7, several lines are missing in the ThAr spectra recorded in August Because we could not identify an adequate number of lines in all orders to obtain a wavelength solution with the wavecal procedure from the REDUCE package, we used calibration spectra of a new lamp recorded in September 2014 instead to calibrate the wavelengths of stellar spectra recorded in August However, we assume that the wavelength solution for CAFE spectra is sufficiently stable and the quality of our results is not affected. Figure 3.7.: Image sections of CAFE ThAr calibration spectra recorded on 09 Aug 2014 (left) and 25 Sep 2014 (right) 29