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The Fundamental Stellar Parameters of FGK Stars in the SEEDS Survey

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The Fundamental Stellar Parameters of FGK Stars in the SEEDS Survey MNRAS 000, 1–?? (2017) Preprint 17 September 2018 Compiled using MNRAS LATEX style file v3.0 The Fundamental Stellar Parameters of FGK Stars in the SEEDS Survey Evan A. Rich1 John P. Wisniewski1 Michael W. McElwain2 Jun Hashimoto3,4 Tomoyuki Kudo5, Nobuhiko Kusakabe4, Yoshiko K. Okamoto6, Lyu Abe7, Eiji Akiyama 5, Wolfgang Brandner8, Timothy D. Brandt9, Phillip Cargile10, Joseph C. Carson11, Thayne M Currie5, Sebastian Egner5, Markus Feldt8, Misato Fukagawa12, Miwa Goto2, Carol A. Grady13,14,15, Olivier Guyon5, Yutaka Hayano5, Masahiko Hayashi4, Saeko S. Hayashi5, Leslie Hebb16, Krzysztof G. He lminiak17, Thomas Henning8, Klaus W. Hodapp18, Miki Ishii4, Masanori Iye4, Markus Janson19, Ryo Kandori4, Gillian R. Knapp20, Masayuki Kuzuhara21, Jungmi Kwon22, Taro Matsuo23, Satoshi Mayama24, Shoken Miyama25, Munetake Momose26, Jun-Ichi Morino4, Amaya Moro-Martin27,28, Takao Nakagawa29, Tetsuo Nishimura5, Daehyeon Oh30, Tae-Soo Pyo5, Joshua Schlieder31,8, Eugene Serabyn32, Michael L. Sitko33,34, Takuya Suenaga4,35, Hiroshi Suto4, Ryuji Suzuki4, Yasuhiro H. Takahashi4,22, Michihiro Takami36, Naruhisa Takato5, Hiroshi Terada5, Christian Thalmann37, Daigo Tomono5, Edwin L. Turner9, Makoto Watanabe38, Toru Yamada39, Hideki Takami4, Tomonori Usuda4,24, Motohide Tamura4,22 Note: All affiliations are located after the conclusion section of the paper. Accepted 2017 August 8. Received 2017 August 7; in original form 2017 February 2 ABSTRACT Large exoplanet surveys have successfully detected thousands of exoplanets to-date. Utilizing these detections and non-detections to constrain our understanding of the formation and evolution of planetary systems also requires a detailed understanding of the basic properties of their host stars. We have determined the basic stellar properties arXiv:1708.02541v1 [astro-ph.SR] 8 Aug 2017 of F, K, and G stars in the Strategic Exploration of Exoplanets and Disks with Sub- aru (SEEDS) survey from echelle spectra taken at the Apache Point Observatory’s 3.5m telescope. Using ROBOSPECT to extract line equivalent widths and TGVIT to calculate the fundamental parameters, we have computed Teff , log g , vt , Fe H , chromospheric activity, and the age for our sample. Our methodology( was) calibrated[ / ] against previously published results for a portion of our sample. The distribution of Fe H in our sample is consistent with that typical of the Solar neighborhood. Ad- ditionally,[ / ] we find the ages of most of our sample are < 500Myrs, but note that we cannot determine robust ages from significantly older stars via chromospheric activity age indicators. The future meta-analysis of the frequency of wide stellar and sub- stellar companions imaged via the SEEDS survey will utilize our results to constrain the occurrence of detected co-moving companions with the properties of their host stars. Key words: (stars:) planetary systems – stars: fundamental parameters – stars: abundances © 2017 The Authors 2 Rich et al. 1 INTRODUCTION to correlate the observed detections of brown dwarfs and Jovian-mass planets with the properties of their host stars. Since the discovery of the first exoplanet surrounding a Fundamental stellar atmospheric parameters such as ef- Sun-like star (Mayor & Queloz 1995), dedicated planet sur- fective temperature (T ), surface gravity (log g ), and iron eff ( ) veys such as, those utilizing the Kepler Space Telescope abundance ( Fe H ), can be calculated using a variety of [ / ] (Borucki et al. 2009, 2010, 2011), the California Planet well tested and vetted codes. For example, MOOG (Sneden Search (Howard et al. 2010; Wright et al. 2011) and the 1973) utilizes plane-parallel atmospheric models to perform Anglo-Australian Telescope planet search (Tinney et al. Local Thermodynamic Equilibrium spectral analysis or syn- 2001; Butler et al. 2001; Wittenmyer et al. 2014) have ex- thesis, given a set of equivalent widths (EW) measured from panded the number of confirmed exoplanets to-date to more a stellar spectrum and a line list. Spectroscopy Made Easy than 3,000 exoplanets (exoplanets.org). These surveys have ∼ (SME; Valenti & Piskunov 1996; Valenti & Fischer 2005; yielded sufficient numbers of detections to enable correla- Piskunov & Valenti 2017) uses Kurucz (Castelli & Kurucz tions with their host star properties, such as mass and metal- 2004) or MARCS (Gustafsson et al. 2008) atmospheric mod- licity, to better constrain our understanding of how planets els and line data from the Vienna Atomic Line Database form. (VALD; Kupka et al. 1999, 2000; Ryabchikova et al. 1997; A variety of studies have sought to identify trends be- Piskunov et al. 1995) to fit synthesized spectra to observed tween the frequency of exoplanets and a given host star’s spectra. Temperature Gravity microtrubulent Velocity IT- fundamental parameters. Shortly after the first detections of erations (TGVIT; Takeda et al. 2002a, 2005) employs tab- exoplanets, it was recognized that there was a trend between ulated EWs computed from a grid of atmospheric models the occurrence of Jovian-mass exoplanets and their host with varying atmospheric parameters. In this paper, we have star metallicity (eg. Gonzalez 1997; Fischer & Valenti 2005). adopted TGVIT to characterize the fundamental properties More recently, this relation has been extended for Jovian- of the SEEDS survey target list. mass planets surrounding intermediate mass sub-giants to We present fundamental atmospheric parameters (Teff , M-dwarf hosts Johnson et al. (2010) and to terrestrial-size log g , Fe H ), microturbulent velocity, chromospheric ac- ( ) [ / ] exoplanets (R<1.7 REarth) (Wang & Fischer 2015). Jovian tivity, and age determinations of the FGK stars in the mass planets are seen to increase in frequency around SEEDS survey. In section 2 we present the observations and their host stars from M-dwarf stars to A-dwarfs stars reduction methods for our echelle spectra. Next, we discuss (Johnson et al. 2010). It has also been suggested that the our methodology for measuring line strengths (section 3.1) frequency of planets varies inversely with the lithium abun- and then using TGVIT (section 3.2) to calculate the fun- dance of the host star (Israelian et al. 2009), though this damental stellar parameters from these line strengths. We trend is still hotly debated (Carlos et al. 2016). These trends compare our analysis with a calibration sample in Section have been identified for planets detected via radial veloc- 3.3. We also discuss the chromospheric activity ages (sec- ity or transit observations; it remains unclear whether such tion 3.4) derived from our spectra. We discuss our results in relationships hold for wide-separation planets detected via Section 4. direct imaging surveys. The majority of exoplanets at small angular separations exhibit correlations with their host stars (e.g. Johnson et al. 2 OBSERVATIONS AND DATA REDUCTION 2010), which is expected from the core accretion forma- tion (Pollack et al. 1996). Since it is unclear whether exo- We observed 110 F,G,K-type stars in the SEEDS master tar- planets detected at wide separation from their host stars get list with the Astrophysical Research Consortium Echelle form via core accretion or disc instability (Boss 2001), it Spectrograph (ARCES) on the Astrophysical Research Con- is critical to robustly characterize the fundamental stellar sortium 3.5 meter telescope at the Apache Point Observa- properties of large direct imaging surveys to better under- tory (APO) (Wang et al. 2003). ARCES provides R 31,500 ˚ ∼ stand the implications and biases of their detection rates. spectra that cover the wavelength range of 3500 A to 10,200 ˚ Partial characterization of the stellar properties of com- A. These observations were made between 2010 October 2 ˚ pleted large planet imaging surveys has been performed to 2016 April 13 at a signal to noise (SNR) at 6000 A rang- (e.g. Nielsen et al. 2008 for VLT/NACO and Biller et al. ing from 83 to 483. Table 1 list the basic properties of our 2013; Nielsen et al. 2013 for Gemini/NICI surveys); and target sample. These data were reduced using standard techniques in will likely occur for ongoing surveys using Gemini GPI 1 (Macintosh et al. 2014) and SPHERE (Beuzit et al. 2008; IRAF. After bias subtraction and flat fielding, the spec- Vigan et al. 2016). The most recent large planet imaging tral orders were extracted. We utilized ThAr lamp exposures survey to be completed is the Strategic Exploration of Ex- taken after each science observation to perform wavelength oplanets and Disks with Subaru (SEEDS) survey (Tamura calibration on these data, and then applied standard helio- 2009; Tamura 2016), whose primary goal was to survey centric velocity corrections. We determined that the wave- ˚ ˚ nearby Solar analogs to search for directly imaged planets length range 4478 A - 6968 A contained a large number of Fe and the discs from which they formed. This survey has an- I and Fe II lines at sufficiently high SNR to extract accurate nounced a number of brown dwarf and exoplanet discov- fundamental stellar parameters. Thus we next continuum eries, including GJ 504 b (Kuzuhara et al. 2013), κ And b (Carson et al. 2013), GJ 758 B (Thalmann et al. 2009), 1 IRAF is distributed by the National Optical Astronomy Obser- Pleiades HII 3441 b (Konishi et al. 2016), and ROXs 42B vatory, which is operated by the Association of Universities for b (Currie et al. 2014). Characterizing the fundamental pa- Research in Astronomy (AURA) under a cooperative agreement rameters of the host stars of this survey will enable one with the National Science Foundation. MNRAS 000, 1–?? (2017) Stellar Parameters of SEEDS Survey 3 normalized the orders spanning this wavelength range using 3.2 Determining Fundamental Atmospheric continuum in IRAF (Tody 1993, 1986) and a 3rd-4th order Parameters spline function. The orders containing these continuum nor- We used the well established FORTRAN based program malized data were then merged into a single-order spectrum. TGVIT (Takeda et al. 2002a, 2005) to calculate the funda- The systemic velocity for each source (see Table 4) was com- mental atmospheric parameters (T , log g , Fe H ) and puted using an IDL-based program that cross correlated a eff ( ) [ / ] v for our sample.
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