DHARMA PLANET SURVEY OF FGKM DWARFS: SURVEY PREPARATION AND EARLY SCIENCE RESULTS

By SIRINRAT SITHAJAN

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA

2018 ⃝c 2018 Sirinrat Sithajan I dedicate this to my family and friends who always love and support me unconditionally. ACKNOWLEDGMENTS I greatly appreciate many people for helping me achieve this success. Firstly, I would like to thank my parents for encouraging me to follow the career path I love, being an astronomer.

This has brought my life full of great experiences and happiness. I especially want to thank my husband, San, who loves me unconditionally and always stands by my side. He usually gives me good advice and cheers me up when I have a hard time. I also would like to thank my advisor, Dr. Jian Ge, who has given me many great work opportunities including working at Kitt Peak Observatory. Staying overnight at the observatory, monitoring target stars, and taking data were a marvelous experience and made me more appreciate my research work. I would like to thank my dissertation committee: Dr. Jian Ge, Dr. Jonathan Tan, Dr. Katia Matcheva, and Dr. James Fry for their constructive advice. I want to thank my close friends from high school for always keeping in touch with me wherever they are in the world. We have been sharing stories about our life living abroad and how to deal with various difficulties. We sometimes meet up and travel together, so I never feel lonely. I would like to thank Dr. Bo Ma who gave me valuable advice when I got stuck on some particular points of my research, and he also helped me find bugs in my programs. I want to thank Nolan Grieves for proofreading my papers and giving constructive comments. Finally, I would like to thank UF classmates who have worked and had fun together. They made me had a wonderful graduate student life in the

US.

4 TABLE OF CONTENTS page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8 ABSTRACT ...... 11

CHAPTER

1 INTRODUCTION ...... 13

1.1 Dharma Planet Survey ...... 13 1.2 History of Radial Velocity Method to Detect Planets ...... 14 1.3 Motivations for Improving Planet Detection Sensitivity of Radial Velocity Surveys 15 1.4 Challenges in the Detection of Low Mass Planets ...... 17 1.4.1 Observing Cadence ...... 17 1.4.2 Measurement Precision ...... 18 1.5 Areas of Emphasize ...... 20

2 TARGET SELECTION ...... 21

2.1 Method ...... 21 2.2 Results ...... 23 2.3 Conclusions ...... 25

3 SIMULATIONS OF OBSERVATIONS ...... 37

3.1 Simulation Method ...... 37 3.2 Results ...... 42 3.3 Conclusions ...... 48 4 SIMULATIONS OF PLANET DETECTIONS ...... 53

4.1 Simulation Method ...... 53 4.1.1 Precision of Radial Velocity Measurements ...... 53 4.1.2 Simulated Radial Velocity Data ...... 55 4.1.3 Planet Detectability ...... 58 4.2 Results ...... 60 4.2.1 Planet Detectability of the Dharma Planet Survey ...... 60 4.2.2 Planet Yields ...... 60 4.3 Conclusion ...... 61

5 EFFECT OF TELLURIC LINES ON RADIAL VELOCITY MEASUREMENTS .... 65

5.1 Simulation Method ...... 66

5 5.1.1 Simulated Observed Spectra ...... 66 5.1.2 Precision of Radial Velocity Measurements ...... 70 5.1.3 Telluric Line Correction ...... 72 5.1.3.1 The masking method ...... 72 5.1.3.2 The modeling method ...... 72 5.2 Results ...... 75 5.2.1 The Effect of Telluric Lines in Optical, Broad-Optical, and NIR Regions 75 5.2.2 Treatment of Telluric Contamination ...... 76 5.2.2.1 Optical wavelength region ...... 76 5.2.2.2 Broad-Optical and NIR wavelength regions ...... 77 5.3 Conclusions ...... 79

6 CURRENT STATUS OF THE DHARMA PLANET SURVEY AND EARLY SCIENCE RESULTS ...... 88 6.1 Instrument and Pipeline Performance ...... 88 6.2 Observation ...... 90 6.3 Planet Detection ...... 96 REFERENCES ...... 115

BIOGRAPHICAL SKETCH ...... 120

6 LIST OF TABLES Table page

2-1 Reference sources of stellar properties used in the target selection...... 24

2-2 List of target stars for the Dharma Planet Survey and their stellar parameter values. 32

2-3 Minimum and maximum values for stellar parameters of the DPS targets in each spectral type...... 36

3-1 Percentage of available time that can be used for observation...... 41

3-2 Summary of observations for the target stars...... 49 4-1 Planet yields of 150 target stars observed in the Dharma Planet Survey ...... 62

5-1 Signal to noise ratio of the simulated spectra with different effective temperatures (or spectral types) at 1.25 µm and 0.55 µm...... 70

5-2 The fraction of stellar spectrum remaining for the RV precision calculation after contaminating telluric lines with different strengths are masked...... 74

6-1 The DPS target stars observed between October 2016 - April 2018...... 93

6-2 List of the planet and planet candidates from the Dharma Planet Survey...... 97

7 LIST OF FIGURES Figure page

1-1 Detection probability of planets with different masses and orbital periods around the stars observed in the Lick-Carnegie Exoplanet Survey ...... 18 1-2 Detection probability of planets with different masses and orbital periods around the stars observed in the Eta-Earth Survey ...... 19

2-1 Distribution of the Right Ascensions and Declinations of the DPS target stars ... 25 2-2 Distance distribution of the DPS Targets ...... 26

2-3 V magnitude distribution of the DPS Targets ...... 27

2-4 H-R diagram of the DPS targets ...... 28

2-5 Stellar mass distribution of the DPS Targets ...... 29

2-6 Stellar rotational velocity distribution of the DPS Targets ...... 30

′ 2-7 Log RHK index distribution of the DPS Targets ...... 31 3-1 Simulated observing schedule for December 29th, 2018 ...... 39

3-2 Kitt Peak weather statistics during 1999-2006 ...... 40

3-3 Comparison between simulated weather patterns of January 2017 and January 2018 42

3-4 Simulated weather in the night of December 29th, 2018 ...... 43 3-5 Simulated observing report for December 29th, 2018 ...... 44

3-6 The number of observations received by the target stars at the end of observing period ...... 45 3-7 Time span the target stars needed to obtained 100 observations ...... 45

3-8 Time span the target stars needed to obtained 100 observations as a function of RA 46

3-9 Simulated Observations of GJ 761.1 ...... 46

3-10 Simulated Observations of GJ 708.4 ...... 47

3-11 Simulated Observations of GJ 892 ...... 48 4-1 The overall detection efficiency of the TOU spectrograph as a function of wavelength 55

4-2 Estimated RV measurement precisions of the DPS target stars ...... 56

4-3 Simulated RV data set 1 ...... 58

8 4-4 Simulated RV data set 2 ...... 59 4-5 Distribution of planet eccentricities used in simulating RV data ...... 60

4-6 Simulated RV data set 3 ...... 61

4-7 Planet detectability of 0.5-M⊙ star ...... 62

4-8 Planet detectability of the Dharma Planet Survey in the baseline case ...... 63 4-9 Planet detectability of the Dharma Planet Survey in the pessimistic case ...... 64

5-1 Synthetic atmospheric transmission used in simulating observed stellar spectra ... 68

5-2 Simulated stellar spectra with and without telluric lines ...... 69

5-3 Radial velocities measured from simulated observed spectra with different input barycentric velocities ...... 71

5-4 Spectral regions that are masked out from RV precision calculation due to strong telluric lines ...... 73

5-5 Simulated observed spectra after telluric lines are modeled and subtracted out ... 76 5-6 Radial velocity measurement error caused by telluric lines in the Optical, Broad-Optical, and NIR regions...... 77

5-7 Radial velocity precision of the Optical, Broad-Optical, and NIR spectra contaminated with telluric lines, assuming SNR=200 ...... 78

5-8 Radial velocity precision of the Optical, Broad-Optical, and NIR spectra contaminated with telluric lines, assuming SNR=1000 ...... 79 5-9 Radial velocity precision of the Optical region when strong telluric lines are masked out, assuming SNR=1000 ...... 80

5-10 Radial velocity precision of the Optical region when telluric lines are subtracted out from the spectra, assuming SNR=1000 ...... 81

5-11 Radial velocity precision of the Broad-Optical region when strong telluric lines are masked out, assuming SNR=200 ...... 82 5-12 Radial velocity precision of the Broad-Optical region when telluric lines are subtracted out from the spectra, assuming SNR=200 ...... 83

5-13 Radial velocity precision of the NIR region when strong telluric lines are masked out, assuming SNR=200 ...... 84

5-14 Radial velocity precision of the NIR region when telluric lines are subtracted out from the spectra, assuming SNR=200 ...... 85

9 5-15 Radial velocity precision of the Broad-Optical region when strong telluric lines are masked out, assuming SNR=1000 ...... 86

5-16 Radial velocity precision of the Broad-Optical region when telluric lines are subtracted out from the spectra, assuming SNR=1000 ...... 87

6-1 Instrumental drift correction residuals measured over two months ...... 89

6-2 Radial velocity measurements of the RV stable star HD 10700 ...... 89

6-3 Phased radial velocities of the known planet host star HD 1461 ...... 90

6-4 Radial velocities of the known planet host star HD 190360 ...... 91 6-5 Cumulative exposure time of the DPS ...... 92

6-6 Planet HD 26965 b ...... 97

6-7 Planet candidate 1 b ...... 98

6-8 Planet candidate 2 b ...... 99

6-9 Planet candidate 2 c ...... 100 6-10 Planet candidate 3 b ...... 101

6-11 Planet candidate 4 b ...... 102

6-12 Planet candidate 5 b ...... 103

6-13 Planet candidate 5 c ...... 104 6-14 Planet candidate 6 b ...... 105

6-15 Planet candidate 6 c ...... 106

6-16 Planet candidate 6 d ...... 107

6-17 Planet candidate 6 e ...... 108

6-18 Planet candidate 7 b ...... 109 6-19 Planet candidate 7 c ...... 110

6-20 Planet candidate 8 b ...... 111

6-21 Planet candidate 9 b ...... 112

6-22 Locations of the discovered planet and planet candidates from the DPS on the mass-period detectability plane (baseline case) ...... 113

6-23 Locations of the discovered planet and planet candidates from the DPS on the mass-period detectability plane (pessimistic case) ...... 114

10 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DHARMA PLANET SURVEY OF FGKM DWARFS: SURVEY PREPARATION AND EARLY SCIENCE RESULTS

By

Sirinrat Sithajan August 2018

Chair: Jian Ge Major: Astronomy

The Dharma Planet Survey (DPS) is a high-cadence and high-precision radial velocity exoplanet survey. It aims to discover and characterize close-in low-mass planets, sub-Jovian planets, and potentially habitable planets around nearby bright FGKM dwarf stars. My work involves four tasks in the DPS preparation. First, I select target stars for the survey. One hundred and fifty-six stars, including 34 F, 66 G, 30 K and 26 M dwarfs, have been selected. Their intrinsic properties were carefully reviewed to ensure that they can provide the sensitivity for low-mass planet detection. Secondly, I develop simulation codes for determining the feasibility to complete the observation of selected targets within a given time and with a desired observing cadence. The codes were used with 150 selected FGKM stars to be observed

100 times per star during October 2016-December 2020. The results show that about 80 percent of the stars can receive 100 observations by the end of December 2020 and each star needs about 490 days to obtain 100 visits. Third, I use a simulation method to estimate the survey completeness and the number of planets that could be detected around the 150 stars.

The results show that 0-1 Earth, 4-25 Super-Earths, 44-55 Neptunes, 26 Sub-Jovians, and 1-8

Super-Earths in habitable zones could be detected. Finally, I use simulations to study the effect of telluric lines on RV measurements. TOU spectrograph using in the DPS has the wavelength coverage from 0.38 to 0.9 µm, but the region between ∼0.6 to 0.9 µm is contaminated with relatively strong telluric lines. The result from the simulations shows that the RV error caused by telluric lines in this region is ∼4 m s−1 . However, if telluric lines stronger than 5-10 percent

11 are masked out, or they are modeled and removed to 90 percent, the RV error will substantially be decreased giving this region benefits RV measurements in M dwarfs. The DPS has been observing target stars for about one and a half year, and 79 stars are being monitored. It can discover a super-Earth (8.47 M⊕) orbiting a K dwarf, HD 26965, and identify 15 low-mass planet candidates so far.

12 CHAPTER 1 INTRODUCTION

1.1 Dharma Planet Survey

The Dharma Planet Survey (DPS) is a high-cadence and high-precision radial velocity

exoplanet survey. It aims to detect and characterize close-in low-mass planets and sub-Jovian planets around nearby bright FGKM dwarfs. The ultimate goal of the survey is to detect

potentially habitable super-Earth planet candidates for follow-up programs in future space

missions such as JWST, WFIRST-AFTA, EXO-C, EXO-S, and LUVOIR to identify possible

biomarkers supporting life (Ge et al., 2016). The DPS uses the dedicated 50-inch automatic telescope at Mt. Lemmon, called the

Dharma Endowment Foundation Telescope (DEFT). The telescope is equipped with TOU

(formerly called EXPERT-III), a fiber-fed, cross-dispersed echelle spectrograph, which provides a spectral resolution of 100,000 and wavelength coverage of 3800-9000 A,˚ and a 4kx4k

Fairchild CCD detector (Ge et al. 2012; Ge et al. 2014b). The instruments are environmental controlled giving the temperature and pressure stable within 1 mK and 0.01 mTorr over a

month.

The highlights of the DPS is its high-cadence observation and high RV measurement

precision. These aspects will improve planet detection sensitivity and accuracy to assess the

survey completeness and to determine occurrence rates of low-mass planets. They also help resolve controversial low-mass planet discoveries previously claimed by different RV surveys,

which often suffer from sparse and irregular observation cadences due to sharing requirements.

The survey was officially started in October 2016. Between October 2016-December

2020, it will monitor 150 FGKM dwarfs nearly homogeneously. Each star will be initially observed ∼30 consecutive observable nights to probe close-in low-mass planets, and after

that, it will be additionally observed ∼70 times randomly over ∼420 nights. The automatic

nature of the telescope and its flexible queue observation schedule are keys to realizing this

nearly homogenous high cadence. Because of the high observing cadence and homogeneity of

13 observation for every survey star, both detections and non-detections from the survey can be reliably used for statistical studies.

1.2 History of Radial Velocity Method to Detect Planets

Radial Velocity (RV), or Doppler, method is one of the successful techniques to detect

exoplanets. It can discover a planet by observing a star’s radial velocity, which varies over time

due to the gravitational pull from the orbiting planet. Starting in the 1970s, the suggestion of Griffin (1973) and Griffin & Griffin (1973) to use the Earth’s telluric lines for a fiducial

wavelength scale for general purpose spectrographs allowed ∼10 m s−1 RV measurement precision to be achieved and made it possible to use RV method to detect exoplanets. The

first bonafide detection of exoplanet using RV method was shown in 1995 by Mayor & Queloz (1995) and in the same year Walker et al. (1995) could use their achieved 13 m s−1 long-term

(12 years) RV precision to constrain the occurrence rate of Jupiter-mass planets within 5 AU of solar-type stars to 5 percent. A major improvement in the RV technique was shown by

Butler et al. (1996). Instead of using telluric lines for the wavelength reference, they used the

absorption lines produced by an iodine gas cell placed in front of the spectrometer entrance slit. Iodine absorption lines imprinted in science spectra allow 3 m s−1 RV precision to be

achieved. By using this technique, the Lick-Carnegie Exoplanet Survey Team (LCES) can

discover over 200 planets from the 1600 stars they observed for 20 years since 1994 using

HIRES spectrometer at Keck-I telescope (Butler et al., 2017). In 2003, Mayor et al. (2003) demonstrated an unprecedented RV precision of 1 m s−1 they could achieve with the High Accuracy Radial velocity Planet Searcher (HARPS) at the European Southern Observatory

(ESO). Unlike the prior RV instruments, HARPS was put in an extremely stable environment where the pressure and temperature variations were kept below 0.01 mbar and 0.01◦C, giving the instrumental RV drifts always below 1 m s−1 . The precision that HARPS achieved results in a large number of planet discoveries and its discoveries contribute more than a half of all known planets less massive than the Neptune mass. Some examples of its outstanding discoveries include a system of three Neptune-mass planets (HD 69830, Lovis et al. 2006), the

14 first Super-Earth (7.7-M⊕) in habitable zone (GJ 581 c, Udry et al. 2007), and an Earth-mass planet in habitable zone of the Sun’s nearest star (Proxima Centauri b, Anglada-Escud´eet al.

2016). Currently, several RV instruments can routinely achieve ∼1 m s−1 precision (Fischer

et al., 2016). However, the RV community still desires to improve RV measurement precision

down to ∼0.1 m s−1 in order to probe new regimes of planets and detect Earth analogs. 1.3 Motivations for Improving Planet Detection Sensitivity of Radial Velocity Surveys

Since the Radial Velocity method was used to discover the first exoplanet in 1995 (Mayor

& Queloz, 1995), it has been improved rapidly and can detect over 700 planets in the past 20 years1 . However, the RV community still wants to improve the RV technique in order to probe

the population of planets at the low-mass end.

Our understanding about the planet population and their formation was challenged when

the NASA’s Kepler space mission using the transit technique revealed a large number of small planets in tight orbits (the radii between 1-4 R⊕ and orbital periods < 100 days). With the occurrence rate of about one planet for every two observed stars (Fressin et al. 2013;

Burke et al. 2015), this class of planets absented from our Solar System was turned to be

the most abundance planet class known to date. Many research groups (e.g. Raymond et al.

2008; Chiang & Laughlin 2013; Hansen & Murray 2013) have proposed models to explain

the formation of these planets, and to verify and constrain the models requires observed properties such as the planet mass and eccentricity, which can be derived from Radial Velocity

data. However, detecting the RV signals from small (low mass) planets is not a trivial task.

The difficulty in detecting the RV signals from those planets is reflected in the small number

(∼140) of discovered RV planets with masses (Msini) < 20 M⊕ (equivalent to R < 4 R⊕, Weiss & Marcy 2014; Bashi et al. 2017) compared to the number of all discovered RV planets

1 http://exoplanet.eu/catalog

15 (∼750) even though low-mass planets are shown to be more abundant than massive planets in terms of their occurrence rate (Howard et al. 2010b; Mayor et al. 2011).

One classic question that has long been asked in human history is “Is there another habitable planet like the Earth?”. Although more than 8000 planets and candidates2 have been detected so far, only 53 of them (18 from RV method)3 are classified as the ‘potentially habitable’ planets. The potentially habitable planet is defined as a planet that is likely to be rocky and can possibly hold liquid water on its surface. It is the first-order approximation of planet’s habitability, and to confirm the true habitability needs further extensive investigation

(e.g. Selsis et al. 2007). The main reason for the small detection fraction of these potentially habitable planets is that the sensitivity of the existing planet surveys including RV surveys is not good enough for detecting the majority of them. As the RV method has been shown to be a successful method to detect planets, especially around nearby bright stars, it is promising to improve its sensitivity in order to routinely detect the potentially habitable planets.

The bulk density is an important property of a planet for understanding planet composition. It also plays important role in the planet characterization including determining the habitability of a planet. To estimate the bulk density, the planet mass is required together with the planet radius, which can be obtained from the transit technique.

Most previous and ongoing RV surveys are shown to have low detection sensitivity in the low-mass planet domain. For example, the probability to detect planets a few times Earth-mass is less than 10 percent (Marcy et al. 2005; Cumming et al. 2008; Mayor et al. 2011; Howard et al. 2010b). Improvement in the detection efficiency is needed in order to discover a large number of low-mass planets for statistical study and accurately determine their occurrence rate for constraining planet formation models.

2 https://exoplanetarchive.ipac.caltech.edu

3 http://phl.upr.edu/projects/habitable-exoplanets-catalog

16 1.4 Challenges in the Detection of Low Mass Planets

1.4.1 Observing Cadence

The first challenge discussed is the observing strategy of most exoplanet surveys.

Generally, the available time of a telescope has to be shared among different survey programs.

Therefore, each star in the exoplanet search program is usually observed with low cadence. The

low cadence of observation results in the poor sampling of the stellar RV curve and, hence, difficulty in identifying the signals caused by an orbiting planet, especially when the planet

signals are in about the same magnitude as the RV measurement precision. For example,

target stars in the LCES typically got 10-30 observations within 6-8 year observing duration

(Marcy et al. 2005; Cumming et al. 2008). As shown in Figure 1-1, the probability that the

LCES can detect planets with mass (Msini) ∼20 M⊕ and the orbital period within 10-100 days is only ∼5 percent or less. The situation is improved when some selected stars in its sample were specially monitored for probing the Super-Earth and Neptune mass regime. In this case, each star was visited at least 20 times within 5 years, with at least one set of 6-12 observations in 12 consecutive nights (Howard et al., 2010b). The sensitivity of this observing program,

the Eta-Earth Survey, is shown in Figure 1-2. With the additional high-cadence observations,

improvement in the detection sensitivity can be seen. The planets with masses ∼20 M⊕ and

the periods within 10-100 days can be detected at the rate of more than 70 percent, and the

planets with mass ∼10 M⊕ orbiting in that period range can be detected at the rate of 20-70

percent. This result indicates that high-cadence observation can be an efficient approach for enhancing the detection sensitivity, given that the detection is not limited by the RV

measurement precision. For lower mass planets such as those ones with masses a few times the

Earth, the detection sensitivity in Figure 1-2 is still very poor, e.g. ∼10 percent or less for the

orbital period ∼10 days, even though the high-cadence observation was included. In this case,

the detection sensitivity is limited by the RV measurement precision rather than the observing cadence.

17 Figure 1-1. The detection probability of planets with different masses (Msini) and orbital periods around the 585 FGKM stars (∼80 percent are GK stars) monitored in the Lick-Carnegie Exoplanet Survey (LCES) during 1996-2004 (Figure 8 in Cumming et al. 2008).

1.4.2 Measurement Precision

To detect very low-mass planets as those ones with masses a few times the Earth’s mass

orbiting at P ≳ 10 days and the potentially habitable planets, not only the observing cadence but also the measurement precision should be improved. Improvement in the RV measurement precision involves most parts of the RV survey, which can be divided into two groups. The first group relates to instruments. It includes the improvement in environmental control, stability in the illumination of the spectrometer optics, detector quality, wavelength calibration, and using broader observing waveband. The other groups involve collecting and extracting Radial

Velocity data including dealing with stellar noise and telluric lines.

18 Figure 1-2. The detection probability of planets with different masses (Msini) and orbital periods around the 166 GK stars monitored in the Eta-Earth program, a part of the Lick-Carnegie Exoplanet Survey (Figure 1 in Howard et al. 2010b).

19 1.5 Areas of Emphasize

My work relates to the preparation of the Dharma Planet Survey to search for low-mass planets around nearby FGKM dwarfs. Firstly, I select suitable target stars for the survey. These stars are carefully selected so that their intrinsic properties, such as brightness and rotational velocity, allow the highest RV measurement precisions and the highest sensitivity to detect low-mass planets. Secondly, I estimate the length of time required to complete observation of those stars. I simulate their observing schedules based on the designed observing timeline and cadence. Realistic observing conditions such as weather, the length of the night, and the target’s observing window are taken into account. This simulation will help us know an approximated length of time needed for the survey and manage the precious telescope time efficiently. Third, I determine the survey completeness and predict the number of planets that will be detected when the survey is completed. Finally, I study the effect of telluric lines on RV measurements. As TOU spectrograph covers red wavelengths (∼0.6-0.9 µm) where telluric contamination is relatively strong. I use simulations to estimate how much telluric lines affect its RV measurement precision. I also determine the improvement in RV measurement precision when telluric lines are partially removed from the spectra using masking and modeling techniques. The results will tell us how much telluric lines affect RV measurement precision and what level of telluric correction is needed in order to achieve the desired RV precision.

They can be used as a guideline for designing and optimizing ongoing and upcoming RV surveys. Finally, I summarize the current status of the survey and present its early science results.

In summary, there are six chapters presented here. Chapter 1 gives the introduction to my work. Chapter 2-4 involve preparation the Dharma Planet Survey for observation. Chapter

5 discuss the telluric lines in RV measurements. The last chapter, Chapter 6, summarizes the current status of the survey and present early science results.

20 CHAPTER 2 TARGET SELECTION

Selecting suitable targets for observing is one of the crucial steps that determine the success of the survey. For the Dharma Planet Survey, the main science goal is to detect low-mass planets, such as rocky-planets, Neptunes, and potentially habitable planets. These planets orbiting stars cause the stars to wobble with relatively small amplitudes, and high RV measurement precision is needed for detecting them. Therefore, the stars that are selected for the survey should have properties allowing high RV measurement precision.

2.1 Method

The properties of star that are considered in the DPS target selection are listed as below:

1. Brightness. High-precision RV measurements of the DPS is limited to relatively bright stars due to the small telescope size (1.25 m). The fundamental limit of RV measurements is set by photon-noise. According to Bouchy et al. (2001), the photon-noise RV precision is

estimated from: c δVRMS = √ , Q Ne− where c is the speed of light, Q is the quality factor indicating the richness of RV information

that can be provided by a stellar spectrum, and Ne− is the number of photons in the spectrum.

The smaller value of δVRMS , the lower photon-noise and higher RV measurement precision. Since c is a constant and Q is also a constant for spectra from a given star, the RV

measurement precision of the star depends solely on the number of collected photon, Ne− , or the signal-to-noise ratio (SNR) of spectra. The more photons collected, the better RV precision achieved. For the DPS, only bright stars (V magnitude ≲ 10) can give sufficient

photons and hence high RV precision in a reasonable exposure time (≤1 hour).

2. Spectral Type. Only main-sequence stars with spectral types later than F5V are

selected for the survey. Evolved stars are not chosen because they usually pulsate causing

RV noise. Stars with spectral types earlier than F5V are not suitable for high-precision RV measurements due to the low density of absorption lines on their spectra (e.g. Borgniet et al.

21 2017). In addition, those absorption lines are usually broad due to the fast-rotating nature of the stars.

3. Projected Rotational Velocity. Absorption lines of fast rotating stars are broad

resulting in the less accuracy in RV measurements. Bouchy et al. (2001) show that the RV

precision of F9V star reduces by a factor of two when the projected rotational velocity (vsini) is increased from 5 km s−1 to 10 km s−1 . For the DPS targets, we limit the values of vsini

to below 5 km s−1 for GKM dwarf stars and 8 km s−1 for F dwarfs. As F stars usually rotate

faster than GKM stars, we extend the rotational velocity cut for F stars to the higher value in

order to keep bright F stars for observation.

4. Stellar Activity. Inhomogeneities of the stellar chromosphere produce radial velocity ‘jitter’ that can mimic or obscure true RV signals from a planet. The level of jitter is correlated

with stellar chromospheric activity (e.g. Jenkins et al. 2006; Isaacson & Fischer 2010). The

chromospheric activity can be determined by measuring emission in the core of the Ca II H ˚ ˚ ′ and K lines (3968 A and 3934 A) and quantified through the parameter RHK (e.g. Noyes ′ et al. 1984; Wright et al. 2004; Astudillo-Defru et al. 2017). A lower value of RHK means ′ the star has lower activity. We use log RHK = -4.7 as the upper limit for the activity levels of FGK stars selected for the survey. For M dwarfs, we allow slightly higher activity levels where ′ ≤ log RHK = -4.4 is used as the upper limit. This is because M dwarfs have low mass (M∗

0.5 M⊙) and low-mass planets orbiting them produce larger RV amplitudes than the planets orbiting FGK dwarfs.

5. Presence of stellar companion. To avoid light contamination from the neighboring stars, we do not select any star that has a stellar companion within 5 arcseconds.

According to the criteria described above, we create a list of stars suitable for the Dharma

Planet Survey by following these steps: 1. We use SIMBAD service (Wenger et al., 2000) to create a list of stars (sorted by V

magnitude, brightest first) that are in the field of view of our telescope, -10 < Dec < 70. Then

each star is examined one-by-one, starting from the brightest.

22 2. We reject the star if its spectral class is not in between F5V to M9V, where ‘V’ means the ‘main-sequence’ luminosity class.

3. We reject the star if it is a spectroscopic binary or has a stellar companion within 5”.

4. We obtain log g, effective temperature (Teff ), and the distance from the Earth of the star. These properties can help to reject the star if it is misclassified as a main-sequence star in Step 2.

5. We reject the star if it rotates faster than the desired value, i.e. vsini > 8 km s−1 for F star and > 5 km s−1 for GKM star.

′ 6. We reject the star if it is more active than the desired level, i.e. log RHK > -4.7 for FGK stars and > -4.4 for M stars. 7. If the star passes the above screening, it will be put in the DPS target list. In some cases when the required stellar properties are missing derive from available properties and we are unable to reject the star, we will keep it in the target list and mask it as the star required a pre-survey. The stellar properties used in the target selection process above are compiled from literature, which is listed in Table 2-1. Since most bright nearby stars have been observed

extensively from previous surveys and their properties have already determined and published,

we make use of the published data in order to reduce telescope time using for pre-survey. The

pre-survey is only needed for the stars lacking the properties required for the target selection process as described above, and these stars are kept in the target list until we find that they do

not meet our selection criteria.

2.2 Results

As of June 2018, 735 stars have been inspected and 156 (∼20 percent) of them have

been selected to the DPS target list. These stars and their properties are listed in Table 2-2. Note that Column 10 in the table indicates whether a pre-survey is required for the star

(Yes/No).

23 Table 2-1. Reference sources of stellar properties used in the target selection. Property Reference Apparent V magnitude SIMBAD Spectral Type SIMBAD Distance Anderson & Francis (2011) Effective Temperature Casagrande et al. (2011); Brewer et al. (2016) Gaidos & Mann (2014); L´epineet al. (2013) Alonso et al. (1996); Allende Prieto & Lambert (1999) Valenti & Fischer (2005); Ram´ırezet al. (2013) Ram´ırezet al. (2012) Stellar Mass Casagrande et al. (2011); Takeda et al. (2007) Gaidos & Mann (2014); Newton et al. (2016) Howard et al. (2010a) Surface Gravity Casagrande et al. (2011); Takeda et al. (2007) Allende Prieto & Lambert (1999); Frasca et al. (2015) Santos et al. (2017); Ram´ırezet al. (2013) Alonso et al. (1996) Projected Rotational Velocity G l¸ebocki & Gnaci´nski (2005); Brewer et al. (2016) Valenti & Fischer (2005); Vican (2012) Herrero et al. (2012); Reiners et al. (2012) Activity Index Isaacson & Fischer (2010); Pace (2013) Murgas et al. (2013); Astudillo-Defru et al. (2017) Herrero et al. (2012); Vican (2012) Wright et al. (2004); Schr¨oderet al. (2009) Katsova & Livshits (2011); Sissa et al. (2016) Newton et al. (2016); Brewer et al. (2016)

The 156 stars comprise 35 F, 65 G, 30 K, and 26 M stars. They are randomly distributed

on sky in the telescope’s field of view (Figure 2-1). Considering their distances from the

Earth, all stars locate within ∼50 pc, with about a half of them within 20 pc (Figure 2-2). The early type stars are systematically farther than the late-type stars, where the F dwarfs

can locate as far as ∼50 pc while all M dwarfs are within 20 pc of the Earth. As explained previously that bright stars are preferred targets for the DPS, all selected stars are brighter than 10 magnitudes in V band (Figure 2-3). The median value of V magnitudes of the stars is around 6.5, which requires the exposure time. Due to their low intrinsic brightness, later type

stars tend to distribute in fainter magnitude bins. For example, all M dwarfs fall in between

7-10 magnitude bins while all F dwarfs distribute within 3-7 magnitude bins.

24 Figure 2-1. Distribution of the Right Ascensions and Declinations of the DPS target stars.

Figure 2-4 shows the H-R diagram of stars in the target list. We see that all stars clearly locate in the main-sequence band. Considering their mass distribution (Figure 2-5), most

stars have the masses between 0.5-1.4 M⊙, with the median value around the mass of the Sun.

For the stellar rotational velocities (Figure 2-6), F stars tend to rotate faster than GKM stars.

All F stars rotate faster than ∼2 km s−1 while about a half of GKM stars rotate slower than ∼ −1 ′ 2.5 km s . Finally, the proxy of stellar chromospheric activity, log RHK (Figure 2-7), over ′ ′ 50 percent of the stars have log RHK less than -4.9 and more than 80 percent have log RHK ′ less than -4.7. The only stars that have log RHK greater than -4.7 are M dwarfs (due to the selection criteria). 2.3 Conclusions

We selected suitable stars for the Dharma Planet Survey. Stellar brightness, spectral type, projected rotational velocity, chromospheric activity, and the presence of stellar companion were considered in the selection process in order to obtain target stars that allow high RV

25 Figure 2-2. Distance distribution of the DPS Targets. The vertical dash line indicates the median value.

measurement precision and sensitivity for low-mass planet detection. We examined over 700

stars and selected 156 stars to the target list. The 156 stars include 35 F dwarfs, 65 G dwarfs, 30 K dwarfs, and 26 M dwarfs. Their properties are summarized in Table 2-3.

During the survey, we found several times that the selected targets were not the suitable stars as previously thought. For example, last Fall we found ∼5 percent of target stars are

spectroscopic binaries. They were not recognized as this type of star at the time we selected them, so we lost some telescope time for observing them. Those stars have already been

removed from the survey. This situation shows the importance of the target selection process,

especially for a high-cadence and high-precision RV survey where each star will be visited for

several times, such as 100 times for the DPS targets. In the future, the target list will be

updated from time to time as needed.

26 Figure 2-3. V magnitude distribution of the DPS Targets. The vertical dash line indicates the median value.

27 Figure 2-4. H-R diagram of the DPS targets.

28 Figure 2-5. Stellar mass distribution of the DPS Targets. The vertical dash line indicates the median value.

29 Figure 2-6. Stellar rotational velocity distribution of the DPS Targets. The vertical dash line indicates the median value.

30 ′ Figure 2-7. Log RHK index distribution of the DPS Targets. The vertical dash line indicates the median value.

31 Table 2-2. List of target stars for the Dharma Planet Survey and their stellar parameter values. ′ Name Vmag Spec. Dist. Teff M∗ log g vsin i log RHK Pre-S. −2 −1 (pc) (K) (M⊙) (cm s )( km s ) Gl 449 3.60 F9V 10.9 6209 1.32 4.14 5.4 -4.88 N Gl 124 4.05 G0V 10.5 6044 1.18 4.27 4.2 -5.03 N Gl 904 4.12 F7V 13.7 6300 1.25 4.16 6.1 -4.92 Y Gl 166 A 4.43 K0V 5.0 5311 0.86 4.62 1.3 -4.93 N Gl 602 4.61 F8V 15.9 5914 1.08 4.01 3.4 -4.96 N Gl 764 4.68 K0V 5.8 5321 0.82 4.54 1.4 -4.83 N Gl 197 4.71 G1.5IV-V 12.6 5949 1.13 4.29 1.3 -5.08 N Gl 407 5.04 G1V 14.1 5908 1.04 4.29 3.1 -4.97 N Gl 368 5.10 G0.5V 18.4 5946 1.20 4.13 2.9 -5.08 N Gl 303 5.10 F6V 18.3 6362 1.18 4.29 4.3 -4.83 N Gl 768.1 A 5.10 F8V 19.2 6210 1.25 4.24 2.8 -5.04 N Gl 820 A 5.21 K5V 3.5 4414 0.69 4.70 2.0 -4.74 N GJ 1043 5.23 F5V 28.9 6487 1.39 4.03 5.8 Y Gl 68 5.24 K1V 7.5 5248 0.84 4.54 1.7 -4.93 N GJ 245 5.25 F9V 16.7 6148 1.18 4.35 4.3 -4.98 N Gl 672 5.39 G0V 14.3 5712 0.94 4.29 1.6 -4.99 Y Gl 376 5.40 G3V 15.1 5825 1.09 4.40 2.1 -5.08 N Gl 606.2 5.41 G0V 17.2 5833 1.02 4.18 1.5 -5.05 N Gl 616 5.50 G2V 13.9 5826 1.02 4.42 2.3 -4.93 Y Gl 892 5.57 K3V 6.5 4870 0.79 4.59 1.8 -4.94 Y GJ 1095 5.58 G0V 16.9 5957 1.01 4.32 2.9 -4.98 N GJ 3880 5.60 F8V 25.1 6285 1.26 4.23 6.8 -5.07 N GJ 4324 5.60 F7V 20.5 6174 1.16 4.32 4.9 -4.99 N GJ 75 5.63 K0V 10.1 5398 0.91 4.52 1.3 -4.70 Y GJ 4116 5.70 F7V 25.1 6302 1.19 4.23 7.1 -5.50 Y Gl 777 A 5.71 G7IV-V 15.9 5623 0.98 4.31 0.8 -5.11 N Gl 33 5.74 K2.5V 7.5 5111 0.78 4.59 2.0 -4.98 N Gl 252 5.75 G0V 17.2 5920 1.02 4.39 2.9 -5.00 N Gl 177.1 5.77 G2IV, F8V 26.4 5951 1.25 4.11 3.6 -5.03 N GJ 3115 5.79 F8V 27.9 5987 1.12 4.05 4.2 Y Gl 779 5.80 G0V 17.8 5910 1.02 4.43 4.8 -4.85 Y GJ 596.1 5.87 G2.5V 14.7 5693 0.98 4.43 1.8 -4.81 Y Gl 27 5.88 K0.5V 11.1 5226 0.88 4.51 1.1 -5.02 N HIP 30545 5.90 F9V 33.6 6109 1.31 4.01 5.6 -4.99 N Gl 262 5.93 G0V 19.1 5917 1.02 4.38 2.9 -4.99 N Gl 788 5.93 G3V 17.6 6002 1.04 4.47 2.7 -4.89 Y Gl 484 5.95 G0V 17.4 5959 0.96 4.43 1.8 -4.97 N Gl 324 A 5.95 G8V 12.3 5250 0.96 4.48 2.2 -4.99 N GJ 1085 5.97 G2V 15.2 5778 0.99 4.51 2.7 -4.95 N HIP 102805 5.99 F5V 30.6 6440 1.22 4.22 6.5 Y

32 Table 2-2. Continued ′ Name Vmag Spec. Dist. Teff M∗ log g vsin i log RHK Pre-S. −2 −1 (pc) (K) (M⊙) (cm s )( km s ) Gl 820 B 6.03 K7V 3.5 4349 0.56 4.71 1.6 -4.90 N HIP 68030 6.10 F6V 24.9 6158 1.09 4.34 4.5 -4.90 N Gl 226.3 6.12 G0V 25.6 5937 1.04 4.21 1.8 -4.92 Y HIP 57629 6.15 F8V 35.1 6339 1.32 4.18 6.2 -4.95 N HIP 110341 6.16 F5V 30.9 6556 1.25 4.33 5.5 -4.80 Y HIP 92270 6.18 F7V 28.8 6357 1.21 4.33 6.3 Y HIP 103682 6.19 G1V 27.2 5964 1.16 4.24 3.6 -4.99 N GJ 3669 A 6.20 G0V 23.0 6014 1.07 4.39 1.8 -4.92 N HIP 61053 6.20 F9V 21.8 6050 1.09 4.44 4.5 -4.77 N Gl 183 6.21 K3V 8.7 4831 0.82 4.59 1.4 -4.92 Y Gl 547 6.27 G1.5V 17.2 5728 0.98 4.49 1.3 -4.95 N HIP 85042 6.28 G3V 19.5 5730 1.01 4.42 0.9 -4.96 N HIP 33719 6.28 G0V 29.0 6135 1.21 4.29 4.8 -5.08 N Gl 708.4 6.30 G1V 22.8 5792 0.99 4.31 2.9 -5.00 N HIP 67246 6.30 G0V 31.7 5836 1.11 4.16 1.8 -4.98 N Gl 307.1 6.30 G1V 22.2 5853 1.02 4.37 2.7 -4.94 N Gl 754.2 6.31 G5V 25.4 5693 0.99 4.17 3.6 -4.88 Y HIP 81580 6.33 F6V 40.0 6204 1.12 4.02 4.5 Y HIP 73941 6.35 F8V 29.8 6185 1.16 4.29 3.7 -4.94 N Gl 758 6.36 K0V 15.8 5369 0.89 4.45 1.2 -5.01 N GJ 3852 6.36 G5V 23.7 5665 1.02 4.26 1.6 -5.05 N HIP 69564 6.36 F8V 42.8 6233 1.40 4.05 5.4 -4.92 Y Gl 706 6.40 K2V 11.0 5072 0.82 4.59 0.6 -4.97 N HIP 2832 6.41 F7V 37.6 6277 1.23 4.13 5.1 Y Gl 675 6.43 K0V 12.8 5329 0.82 4.59 1.3 -4.96 N Gl 797 A 6.44 G5V 20.9 5819 1.02 4.45 2.9 -4.87 N GJ 451 6.45 K1V 9.1 5168 0.60 4.69 2.9 -4.85 Y HIP 50606 6.45 F8V 31.1 5960 1.10 4.20 3.7 Y Gl 16.1 6.46 G3V 23.2 5779 1.04 4.37 1.8 -5.01 N HIP 24332 6.46 F8V 26.1 6098 1.09 4.39 3.6 -4.82 N HIP 50316 6.46 G2V 33.2 5727 1.08 4.06 0.9 -5.03 N HIP 60353 6.47 F8V 29.9 6205 1.21 4.36 5.4 -4.89 N HIP 81800 6.47 F8V 29.3 6110 1.11 4.31 5.2 -4.87 N HIP 71251 6.48 F6V 29.3 6179 1.13 4.34 4.5 Y Gl 421.1 A 6.49 F6V 34.1 6363 1.16 4.27 6.3 Y Gl 511.1 6.49 G6V 21.2 5555 0.94 4.35 0.0 -5.02 Y GJ 9829 6.49 F7V 26.1 6153 1.09 4.42 4.5 -4.97 N GJ 429 A 6.50 G9IV-V 17.7 5438 0.99 4.53 2.3 -4.88 N HIP 3236 6.51 F8V 33.4 6270 1.21 4.31 2.0 -4.81 Y HIP 68184 6.52 K3V 10.1 4851 0.80 4.56 1.3 -5.11 Y

33 Table 2-2. Continued ′ Name Vmag Spec. Dist. Teff M∗ log g vsin i log RHK Pre-S. −2 −1 (pc) (K) (M⊙) (cm s )( km s ) GJ 59.1 6.53 G6.5V 20.7 5672 0.95 4.42 0.0 -4.99 N HIP 36152 6.54 F6V 30.4 6282 1.09 4.34 5.4 Y HIP 87382 6.55 F8V 33.6 6123 1.19 4.26 3.7 -5.05 N GJ 9648 A 6.57 G2V 24.4 5757 0.99 4.35 2.6 -4.92 N Gl 380 6.61 K6V 4.9 4085 0.67 4.51 2.5 -4.72 Y HIP 30067 6.62 F8V 27.5 6030 1.04 4.36 2.7 -4.96 N GJ 120.2 6.62 G8V 20.6 5621 0.99 4.45 0.9 -4.91 Y GJ 848.4 6.63 G8V 21.6 5484 0.99 4.39 1.3 -5.06 N GJ 4382 6.64 G4V 24.5 5726 1.01 4.39 1.8 Y HIP 3369 6.64 F8V 39.6 6254 1.22 4.21 4.5 Y GJ 3593 6.65 G4V 20.2 5637 0.94 4.47 0.0 -4.94 N Gl 614 6.67 K0V 17.6 5280 1.07 4.51 2.6 -5.06 N Gl 611 A 6.67 G8V 14.5 5249 0.79 4.54 1.8 -4.99 N GJ 3194 6.67 G1.5V 24.2 5647 0.91 4.42 1.8 -4.84 Y GJ 3896 A 6.68 G5V 27.2 5647 0.98 4.30 2.7 -5.07 N Gl 651 6.74 G8V 18.6 5488 0.91 4.51 1.8 -4.84 Y GJ 9648 B 6.75 G3V 24.4 5741 0.94 4.40 2.0 -4.91 N GJ 3387 6.84 G4V 23.5 5782 1.01 4.51 3.6 -5.00 N GJ 4052 6.92 G6V 37.4 5566 1.04 4.06 2.3 -5.08 N HIP 33537 6.94 G5V 24.6 5721 0.94 4.44 3.6 -4.83 N HIP 111274 6.95 G5V 36.4 5666 0.99 4.12 1.8 -5.10 N Gl 761.1 6.96 G5 35.5 5633 1.04 4.15 0.9 -5.07 N HIP 3979 6.97 G5V 21.5 5704 0.94 4.56 2.3 -4.84 N GJ 700.2 6.99 G9V 22.1 5341 0.93 4.46 3.3 -5.06 N HIP 114456 7.00 G8V 24.3 5505 0.94 4.45 2.2 -4.84 Y Gl 483 7.04 K3V 14.9 4968 0.81 4.52 3.0 -4.82 Y GJ 3781 A 7.06 G9V 15.4 5290 0.81 4.64 1.8 -5.01 N HIP 62536 7.07 G5V 37.8 5727 1.02 4.18 0.9 -5.08 Y HIP 75277 7.12 G9V 19.6 5379 0.89 4.56 1.1 -4.70 Y GJ 82.1 7.14 G9V 25.0 5412 0.98 4.51 3.2 -5.04 N GJ 3824 7.14 G7V 26.8 5389 0.91 4.30 3.1 -4.81 Y GJ 1229 7.19 G6V 26.0 5637 0.98 4.48 3.0 -4.89 N HIP 76114 7.21 G5IV-V 30.2 5745 0.99 4.41 1.8 -4.99 N GJ 634.1 7.22 G5V 29.6 5696 0.91 4.40 2.7 -4.95 Y GJ 4010 7.22 G9V 22.3 5380 0.84 4.47 2.9 -4.97 Y Gl 556 7.23 K3V 13.2 4794 0.76 4.60 3.6 -4.79 Y GJ 423.1 7.23 G8V 21.6 5426 0.89 4.56 3.5 -4.92 N GJ 9659 7.28 G3V 31.3 5824 1.04 4.46 2.7 -4.95 N HIP 52409 7.30 G6V 35.4 5717 1.04 4.32 3.5 -5.01 N Gl 775 7.48 K5V 12.9 4599 0.79 4.62 2.8 -4.84 Y

34 Table 2-2. Continued ′ Name Vmag Spec. Dist. Teff M∗ log g vsin i log RHK Pre-S. −2 −1 (pc) (K) (M⊙) (cm s )( km s ) Gl 411 7.52 M2V 2.5 3679 0.51 4.90 1.6 -5.45 Y GJ 591 7.56 K3V 22.4 5057 0.92 4.57 1.2 -5.04 Y Gl 204 7.64 K5V 13.0 4609 0.76 4.63 2.7 -4.92 Y Gl 653 7.71 K5V 10.7 4354 0.64 4.65 2.7 -4.82 N GJ 338 B 7.72 M0V 6.3 3839 0.58 4.76 2.5 -4.42 Y Gl 141 7.84 K4V 15.4 4665 0.76 4.62 3.6 -4.76 Y Gl 481 7.90 K4.5V 14.2 4429 0.73 4.63 3.6 -5.00 Y Gl 205 7.97 M1.5V 5.7 3895 0.60 4.82 0.9 -4.60 N GJ 715 8.00 K4V 18.6 4715 0.81 4.59 2.6 -4.82 N GJ 176.2 8.01 K5V 21.9 4994 0.79 4.58 4.0 -4.86 N Gl 15 A 8.13 M2V 3.6 3693 0.52 4.90 0.9 -5.27 Y GJ 365 8.16 K4V 18.2 4677 0.78 4.60 1.8 -4.87 Y GJ 169 8.30 M0.5V 11.4 4114 0.66 4.67 3.2 -4.81 Y GJ 488 8.47 M0V 10.6 4003 0.63 1.8 -4.47 Y GJ 526 8.50 M1.5V 5.4 3792 0.56 4.79 1.8 -4.96 N Gl 809 8.60 M1V 7.0 3772 0.55 4.81 3.9 -4.63 Y Gl 880 8.64 M1.5V 6.8 3786 0.56 0.4 -4.74 Y Gl 412 A 8.78 M1V 4.8 3743 0.54 4.90 1.6 -5.59 Y GJ 638 8.80 K7.5V 9.8 4337 0.68 4.57 3.2 -4.75 N GJ 172 8.86 M0.5V 10.1 3926 0.61 2.6 -4.70 Y GJ 414 A 8.86 K7V 11.9 3754 0.60 2.7 -4.86 Y Gl 617 A 8.90 M1V 10.7 4156 0.66 4.75 4.2 -4.71 Y GJ 908 8.99 M1V 6.0 3552 0.43 4.8 -5.34 N Gl 14 9.00 M0.5V 15.0 4335 0.69 4.7 -4.63 N Gl 514 9.03 M1V 7.7 3798 0.56 1.5 -4.88 N Gl 123 9.05 M0V 14.5 4039 0.69 0.9 -4.75 Y Gl 752 A 9.12 M3V 5.9 3680 0.51 0.8 -5.07 N Gl 846 9.15 M0.5V 10.2 3898 0.60 4.79 3.8 -4.52 N Gl 740 9.22 M1V 10.9 3570 0.62 4.50 1.9 -4.62 N Gl 382 9.26 M2V 7.9 3746 0.54 2.9 -4.66 N GJ 763 9.33 M0V 14.4 3697 0.63 5.00 2.1 -4.91 N GJ 96 9.35 M0V 11.9 4058 0.65 -4.53 Y Gl 678.1 A 9.43 M1V 10.0 3640 0.56 4.50 4.5 -4.84 N GJ 505 B 9.50 M0.5V 11.1 3831 0.54 2.8 -4.55 Y Gl 699 9.51 M4V 1.8 3247 0.15 5.20 3.3 -5.69 N GJ 49 9.56 M1.5V 10.1 3834 0.58 3.8 -4.67 N

35 Table 2-3. Minimum and maximum values for stellar parameters of the DPS targets in each spectral type. Stellar Parameter F G K M V mag 3.6, 6.6 4.1, 7.3 4.4, 8.9 7.5, 9.6 Distance (pc) 11, 43 11, 38 3, 22 2, 15 vsini ( km s−1 ) 2.0, 7.1 0.8, 4.8 0.6, 4.0 0.4, 4.8 ′ log RHK -5.50, -4.77 -5.11, -4.70 -5.11, -4.70 -5.69, -4.42

36 CHAPTER 3 SIMULATIONS OF OBSERVATIONS

The Dharma Planet Survey plans to monitor 150 stars from October 2016 to December

2020. Each star will be observed ∼100 times in ∼450 nights, including ∼30 consecutive observable nights. This high observing cadence is designed to detect close-in low-mass planets and potentially habitable planets around FGKM dwarf stars. An important question is whether we are able to complete observation of those stars given the observing cadence and time span.

Here we use simulations to find the answer to that question.

3.1 Simulation Method

We simulate ‘observations’ of 150 stars during October 2016-December 2020. The stars we use in the simulations are drawn from the DPS target list described in Chapter 2. They include all FGK stars and 20 brightest M dwarfs from the list. In the simulations, real observing conditions, such as weather, are taken into account. The simulations allow us to see how those stars are observed over the given time period and see results we can obtain at the end of observation, such as the number of observations each star receives.

For each night of observation, two things are simulated: 1.) an observing schedule for the night and 2.) weather conditions. To generate an observing schedule, the following components are considered.

1. Location of the observatory. The Dharma Endowment Foundation Telescope used for the survey is located at Mt. Lemmon in Arizona, USA. Therefore, we use the location of Mt.

Lemmon, the latitude of 32.442◦N, longitude of 110.792◦W, and elevation of 2776 meters above the sea level, as the location of the observatory in our simulations.

2. Time to start and finish observation each night. Observation will be started 30 minutes after sunset and ended 30 minutes before sunrise. The period between those time limits is

‘dark’ time, or the time that is dark enough for observation.

3. Coordinate of the star. Right ascension and declination of the star tell us whether the star is observable that night. They also tell the position of the star on the sky at a given time.

37 4. Airmass of the star. The star will be observed only at airmass below 2 to reduce the atmospheric effects including atmospheric seeing and extinction. These effects can reduce the quality of observed spectra.

5. Angular distance between the star and the moon. To avoid moonlight contamination, we will not observe the star when the angular distance between the moon and the star less than 45 degree for the moon illumination greater than 50 percent and less than 30 degree for the moon illumination equal or less than 50 percent.

6. Exposure time. The exposure time for each star depends on its brightness. In our simulations, we use the same exposure time used in real observation.

• texp = 10 minutes for V < 5

• texp = 15 minutes for 5 ≤ V < 5.5

• texp = 30 minutes for 5.5 ≤ V < 7

• texp = 50 minutes for V > 7 7. Telescope slew time. We assume the telescope takes 5 minutes to move from one target to another target.

8. The number of observations the star obtains so far. This information is used to adjust the target priority. For example, initially, the star will receive a high priority for observation as we want to observe it every observable night. After the star receives 30 observations, its priority is reduced and the star will be managed to get ∼70 observations randomly spread over ∼420 nights. When the star obtains 100 observations, it will not be selected to observe.

9. Monsoon season. During monsoon season, the telescope will be closed and no observation takes place. In the simulations, we mask July and August, which are monsoon season in Arizona, as the months with no observation. An example of observing schedules created from our simulations is shown in Figure 3-1.

This is an observing schedule for the night of December 29th, 2018. In the schedule, information about the sun and moon in that night is given. The time to start and finish observation is provided. There are 20 objects scheduled to observe this night. For each object,

38 Figure 3-1. Simulated observing schedule for December 29th, 2018.

39 its coordinate, Vmag, time to start observing, exposure time, and priority flag are listed. The observatory will use this information for observation.

The other thing we simulate for each night is weather conditions. After we obtain an observing schedule for the night, whether each target can be observed or not depends on the weather condition at the time it is planned to be observed. We generate weather conditions for the night based on Kitt Peak weather statistics during 1999-20061 (Figure 3-2). Kitt

Figure 3-2. Kitt Peak weather statistics during 1999-2006.

Peak weather statistics provides the percentage of bad weather in each month of the year at

Kitt Peak National Observatory, Arizona. For example, in Figure 3-2, about 27 percent of the available time in January is lost to weather. Because the DEFT location is not very far from Kitt Peak National Observatory (∼100 miles), we assume their weather conditions are

1 http://www-kpno.kpno.noao.edu

40 similar. Therefore, in our simulations, we set 27 percent of the available time (or dark time) in January as bad weather (and 73 percent of the available time as good weather). To account for other factors that can make the telescope unable to operate, such as some technical issues, we assume only 90 percent of the available time with good weather can be used for observing targets. This results in only 66 percent of the dark time in January being able to use for observation, and the percentages for the other months are calculated in the same way and shown in Table 3-1. From the table, the percentage of zero is given for July and August due

Table 3-1. Percentage of available time that can be used for observation. Month Available Time (%) January 66 February 55 March 68 April 73 May 81 June 74 July 0 August 0 September 69 October 68 November 68 December 66 to the closure of the telescope during monsoon season. It is important to note that although we use the same percentage value for simulating weather conditions in the same month from different years, such as 66 percent for both January 2017 and January 2018, the simulated weather patterns in those months are different from each other, as seen in Figure 3-3. This result is typical in real weather conditions.

An example of simulated weather conditions for the night of December 29th, 2018 is shown in Figure 3-4. From the figure, we see that the weather is good (weather = 1) a few hours at the beginning of the night. Then the weather becomes bad (weather = -1) for 3 hours before being good again for the rest of the night. The weather in this night allows 15 stars out of the 20 scheduled stars to be observed (and the observing time is shown with triangle symbol). After weather conditions for the night are obtained, we can tell which stars

41 Figure 3-3. Comparison between simulated weather patterns of January 2017 and January 2018. in the schedule can be observed. This information will be recorded in the observing report for future usage. Figure 3-5 shows the observing report from the night of December 29th, 2018. The report tells observing status (Yes/No) for each star. It also tells what time the star was observed and the number of observations the star has received so far.

3.2 Results

We simulated ‘observations’ of the 150 stars during October 2016-December 2020.

This period (without July and August) includes 14386 hours of night time (or dark time).

Thirty-two percent of them (4608 hours) is the time when the weather is bad or the telescope is not available. Fifty-four percent (7715 hours) is used for observation. The remaining

14 percent (2063 hours) is the time when the telescope is idle (no target available for observation), is unable to complete the exposure due to the change in weather condition, and slews. The number of observations and the observing time span each star obtained at

42 Figure 3-4. Simulated weather in the night of December 29th, 2018. the end of the observing period are shown in Table 3-2. We find that 80 percent of the 150 stars can obtain 100 visits. Eight percent of the stars can get 91-99 observations, 7 percent can receive 81-90 observations, and the remaining 5 percent can receive 71-80 observations. A histogram of the numbers of observations for all 150 target stars is shown in Figure 3-6.

Although we want to obtain 100 observations in ∼450 days, the result from simulations shows that a number of stars have to take a longer time than that. As seen in Figure 3-7, the median value of the time spans to complete 100 observations for the 120 stars is around

490 days. Around 30 percent of the stars have to take around 500-600 days to get 100 observations, and around 5 percent has to take more than 600 days. From the figure, the stars that can be successfully completed 100 observations tend to have shorter observing time spans than the stars that cannot receive 100 observations (the median value of observing time spans about 580 days).

43 Figure 3-5. Simulated observing report for December 29th, 2018.

Several factors can prevent the star from receiving 100 observations within the desired time period (∼450 days). The important one is the observing season of the star itself.

Considering the observing time spans for the stars that can obtain 100 observations (black cross symbol in Figure 3-8), we see that the stars with the RAs around 200 to 360 tend to have longer observing time spans than the stars with the other RAs. At Mt. Lemmon, stars with the RAs around 200 to 360 rise around January to June, so they have monsoon season as a ‘gap’ in their observing seasons, such as the star GJ 761.1 shown in Figure 3-9. This gap makes the time for observing the star shorter. As seen in Figure 3-9, GJ 761.1 is observable around 10 months a year, mid-February to mid-December, but 2 months are lost because

44 Figure 3-6. The number of observations received by the target stars at the end of observing period.

Figure 3-7. Time span the target stars needed to obtained 100 observations.

45 of the monsoon season. Without those monsoon gaps, it could obtain 100 observations by MJD∼58330, giving the time span ∼480 days.

Figure 3-8. Time span the target stars needed to obtained 100 observations as a function of RA.

Figure 3-9. Simulated Observations of GJ 761.1.

The angular distance between the star and the moon is another factor that can increase the observing time span. As explained previously, we do not observe a star when it locates close to the moon. If the star spends several nights close to the moon, it will need a longer

46 time to obtain 100 visits. In addition, weather conditions can also affect the time span. It is possible that the weather is bad frequently at the time some particular stars are scheduled to be observed. Those ‘unlucky’ stars have to take a longer time to obtain 100 observations.

For the stars that cannot receive 100 observations (red triangle symbol in Figure 3-8), we find that they do not only have a monsoon ‘gap’ but also have the first observation at a relatively late time, around mid-2019. For example, in Figure 3-10, GJ 708.4 get the

first observation on April 15th, 2019, so it has 625 nights before the end of 2020, which are unfortunately not enough for completing 100 observations for this star.

Figure 3-10. Simulated Observations of GJ 708.4.

Considering the observing cadences of the stars, we manage each star to obtain 30 consecutive observable nights at the beginning of its observing period. However, we find that

30 consecutive observable nights take ∼70 actual nights. The factors that can prevent each star to be observed every night include the separation between the star and the moon, weather, and the number of stars waiting for observing each night, which is usually more than the number of stars that can fit into the observing time slot in that night. Figure 3-11 and Figure 3-9 show optimistic and pessimistic cases for the observing cadences of the 150 stars. The star in the prior case, GJ 892, takes 447 days to obtain 100 observations, which is close to our original plan. Only about 5 percent of stars in the sample can take a shorter time than this. From Figure 3-11, by excluding monsoon season and its off observing season, this star can receive 100 observations in around 220 nights. In other words, it receives 1 observation every other night. On the other hand, the star in the latter case, GJ

47 761.1, takes 605 days to obtain 100 observations where only around 5 percent of stars in the sample has to take longer time than this. Without the time during monsoon season and the

offseason, the star receives 100 observations in 380 days, or 1 observation every 4 nights.

Figure 3-11. Simulated Observations of GJ 892.

3.3 Conclusions

We created simulation codes for determining the possibility to complete observation of a set of stars in a given time period and with a desired observing cadence. We used them to find if 150 stars selected for the Dharma Planet Survey (see Chapter 2) can be observed and completed in 4.3 years, from October 2016 to December 2020, where each of those stars requires 100 observations in 450 days with 30 consecutive observable nights. We found that 80 percent of the stars could obtain 100 observations by the end of December 2020. The remaining 20 percent could not obtain 100 observations, but about three-fourths of it could receive at least 80 observations. Although we wanted the stars to be observed 100 times in

450 days, several factors caused them to take slightly longer time, around 490 days on average. For the consecutive observations, 30 observations in consecutive observable nights took about

70 nights, on average. Considering the observing cadences, the majority of stars (about 90 percent) received 1 observation every 2-4 days. This cadence is very high compared to the cadence seen in most RV surveys, e.g. 10-30 observations within 6-8 year (Marcy et al. 2005;

Cumming et al. 2008).

48 Table 3-2. Summary of observations for the target stars. Name Number of Observations First Day (JD) Last Day (JD) ∆t (days) Gl 449 100 2457709.542 2458166.270 457 Gl 124 100 2458000.346 2458518.075 518 Gl 904 100 2458492.072 2459129.351 637 Gl 166 A 100 2458000.433 2458512.147 512 Gl 602 100 2457755.496 2458240.187 485 Gl 764 86 2458729.122 2459215.044 486 Gl 197 100 2458004.488 2458467.319 463 Gl 407 100 2457679.494 2458157.251 478 Gl 368 100 2458125.231 2458576.217 451 Gl 303 100 2458737.517 2459191.255 454 Gl 768.1 A 100 2457819.520 2458372.177 553 Gl 820 A 100 2458364.160 2458841.040 477 GJ 1043 100 2458730.235 2459177.162 447 Gl 68 100 2458732.474 2459187.147 455 GJ 245 100 2458390.449 2458852.167 462 Gl 672 92 2458663.222 2459152.048 489 Gl 376 100 2457676.469 2458164.245 488 Gl 606.2 100 2457761.503 2458276.127 515 Gl 616 100 2457765.564 2458285.303 520 Gl 892 100 2458730.409 2459177.138 447 GJ 1095 100 2457672.406 2458124.408 452 GJ 3880 100 2457761.517 2458209.189 448 GJ 4324 100 2458623.415 2459140.282 517 GJ 75 100 2458367.258 2458815.349 448 GJ 4116 100 2458211.412 2458782.215 571 Gl 777 A 100 2458185.542 2458664.423 479 Gl 33 100 2458369.416 2458885.065 516 Gl 252 100 2457677.343 2458188.196 511 Gl 177.1 100 2457675.320 2458139.056 464 GJ 3115 100 2457998.299 2458483.140 485 Gl 779 100 2458385.102 2459014.265 629 GJ 596.1 100 2457761.541 2458296.267 535 Gl 27 100 2458729.410 2459183.147 454 HIP 30545 100 2458006.497 2458451.297 445 Gl 262 100 2458003.479 2458486.215 483 Gl 788 100 2457846.506 2458430.217 584 Gl 484 100 2458431.504 2458921.169 490 Gl 324 A 100 2458380.503 2458828.311 448 GJ 1085 100 2457672.382 2458141.119 469 HIP 102805 80 2458602.439 2459206.040 604

49 Table 3-2. Continued Name Number of Observations First Day (JD) Last Day (JD) ∆t (days) Gl 820 B 100 2458214.497 2458762.116 548 HIP 68030 100 2457763.448 2458283.216 520 Gl 226.3 100 2458734.423 2459190.154 456 HIP 57629 100 2457707.508 2458141.394 434 HIP 110341 95 2458645.462 2459215.068 570 HIP 92270 79 2458581.512 2459185.060 604 HIP 103682 74 2458607.490 2459171.063 564 GJ 3669 A 100 2458435.485 2458927.131 492 HIP 61053 100 2458427.499 2458874.246 447 Gl 183 100 2458000.467 2458505.199 505 Gl 547 100 2458477.561 2459205.540 728 HIP 85042 85 2458608.403 2459157.045 549 HIP 33719 100 2458040.449 2458453.383 413 Gl 708.4 87 2458589.335 2459172.090 583 HIP 67246 100 2458476.519 2458986.118 510 Gl 307.1 100 2458425.372 2458874.121 449 Gl 754.2 88 2458731.221 2459201.038 470 HIP 81580 100 2457759.519 2458283.254 524 HIP 73941 100 2457769.476 2458291.131 522 Gl 758 100 2457818.540 2458381.131 563 GJ 3852 100 2458202.199 2457755.506 447 HIP 69564 95 2458443.549 2458896.360 453 Gl 706 100 2458149.558 2458657.180 508 HIP 2832 100 2457672.184 2458145.060 473 Gl 675 100 2457848.309 2458383.080 535 Gl 797 A 95 2458657.409 2459212.042 555 GJ 451 100 2458426.538 2458873.259 447 HIP 50606 100 2457672.420 2458124.307 451 Gl 16.1 100 2457672.208 2458154.065 482 HIP 24332 100 2457676.319 2458143.069 467 HIP 50316 100 2457693.522 2458145.320 452 HIP 60353 95 2458583.172 2459214.502 631 HIP 81800 100 2458578.382 2459108.109 530 HIP 71251 100 2458485.395 2458945.122 460 Gl 421.1 A 100 2458432.503 2458898.233 466 Gl 511.1 100 2458492.340 2458939.161 447 GJ 9829 100 2457679.094 2458106.076 427 GJ 429 A 100 2458430.526 2458948.315 518 HIP 3236 100 2457672.139 2458131.051 459 HIP 68184 100 2457708.539 2458198.176 490

50 Table 3-2. Continued Name Number of Observations First Day (JD) Last Day (JD) ∆t (days) GJ 59.1 100 2458734.239 2459183.172 449 HIP 36152 100 2457673.450 2458152.283 479 HIP 87382 100 2458579.344 2459213.567 634 GJ 9648 A 93 2458729.309 2459205.040 476 Gl 380 100 2458432.396 2458879.159 447 HIP 30067 100 2458368.479 2458785.306 417 GJ 120.2 100 2458367.372 2458837.351 470 GJ 848.4 93 2458623.463 2459195.061 572 GJ 4382 100 2457891.449 2458443.264 552 HIP 3369 100 2457999.323 2458504.056 505 GJ 3593 100 2458431.528 2458875.201 444 Gl 614 100 2458126.481 2458665.131 539 Gl 611 A 92 2458576.367 2459215.537 639 GJ 3194 100 2458000.492 2458551.083 551 GJ 3896 A 86 2459211.535 2458498.499 713 Gl 651 89 2458527.542 2459208.555 681 GJ 9648 B 76 2458591.385 2459202.039 611 GJ 3387 100 2457673.474 2458135.192 462 GJ 4052 83 2458572.493 2459147.065 575 HIP 33537 100 2457673.450 2458152.283 479 HIP 111274 74 2458376.121 2458996.380 620 Gl 761.1 100 2457851.394 2458456.036 605 HIP 3979 100 2458000.370 2458522.115 522 GJ 700.2 100 2458138.558 2458662.329 524 HIP 114456 100 2457858.508 2458498.076 640 Gl 483 100 2458494.393 2458942.097 448 GJ 3781 A 100 2457758.560 2458251.116 493 HIP 62536 100 2458486.478 2459017.130 531 HIP 75277 98 2458610.235 2459215.513 605 GJ 82.1 100 2457672.247 2458166.072 494 GJ 3824 100 2457755.458 2458256.258 501 GJ 1229 100 2457794.541 2458294.194 500 HIP 76114 100 2458479.476 2458967.133 488 GJ 634.1 100 2457789.558 2458294.291 505 GJ 4010 100 2458143.531 2458665.416 522 Gl 556 100 2458497.398 2458961.265 464 GJ 423.1 100 2458447.533 2459003.164 556 GJ 9659 84 2458607.490 2459171.063 564 HIP 52409 100 2457694.532 2458187.195 493 Gl 775 75 2458633.447 2459153.096 520

51 Table 3-2. Continued Name Number of Observations First Day (JD) Last Day (JD) ∆t (days) Gl 411 100 2457675.522 2458181.307 506 GJ 591 100 2458135.504 2458657.253 522 Gl 204 100 2458365.510 2458807.163 442 Gl 653 72 2458593.490 2459168.040 575 GJ 338 B 100 2457674.526 2458164.356 490 Gl 141 100 2457672.483 2458164.134 492 Gl 481 78 2458467.555 2458467.555 741 Gl 205 100 2458365.510 2458807.163 442 GJ 715 100 2458600.417 2459105.150 505 GJ 176.2 100 2457673.249 2458165.097 492 Gl 15 A 100 2458365.246 2458830.162 465 GJ 365 100 2457676.483 2458123.233 447 GJ 169 100 2457672.247 2458166.072 494 GJ 488 91 2458450.509 2459006.127 556 GJ 526 88 2458485.419 2459212.449 727 Gl 809 100 2458586.490 2459152.173 566 Gl 880 100 2458729.434 2459162.143 433 Gl 412 A 100 2457672.458 2458121.156 449 GJ 638 100 2458135.504 2458657.253 522 GJ 172 100 2457677.478 2458116.289 439 GJ 414 A 100 2457686.513 2458173.118 487 Gl 617 A 100 2458124.470 2458620.118 496 GJ 908 100 2458363.397 2458871.056 508 Gl 14 100 2457672.063 2458130.074 458 Gl 514 100 2458121.426 2458658.194 514 Gl 123 100 2457672.101 2458127.072 455 Gl 752 A 91 2458636.385 2459170.063 534 Gl 846 84 2457675.098 2458268.426 593 Gl 740 82 2458548.516 2459199.038 651 Gl 382 100 2458518.488 2458989.349 471

52 CHAPTER 4 SIMULATIONS OF PLANET DETECTIONS

The Dharma Planet Survey (DPS) aims to discover low-mass planets, including the

potentially habitable planets, around nearby bright stars. Between October 2016 to December 2020, 150 selected nearby bright stars will be monitored. In this chapter, we use a simulation

method to estimate the number of planets that could be detected from those stars.

4.1 Simulation Method

4.1.1 Precision of Radial Velocity Measurements

RV measurement precision is a factor that determines planet detectability of RV

measurements. The better the RV precision, the smaller detectable RV signals. To simulate planet detections around the 150 stars, we assume each star has two corresponding RV

measurement precisions, one for a baseline case (δVrms,bl ) and one for a pessimistic case

(δVrms,pm). These cases provide the upper and lower limits of planet detections around the star. For the baseline case, the RV precision is calculated from √ 2 2 2 2 δVrms,bl = (1.5 × δVrms,photon) + 0.5 + 0.3 + 0.6 , and for the pessimistic case, it is calculated from √ 2 2 2 2 δVrms,pm = (1.5 × δVrms,photon) + 0.5 + 0.6 + 2.0 .

These formulae are obtained by approximating the error budget involving RV measurements of the star. The errors can come from several sources such as photon noise, astrophysical noise, the data reduction process, and wavelength calibrations. In the equation, 1.5 × δVrms,photon represents the error in data reduction pipeline, 0.5 represents the error in wavelength calibration, 0.3 or 0.6 represents instrumental drifts, and 0.6 or 2.0 in the last terms represents stellar noises. The δVrms,photon in the equations is the photon-noise RV measurement precision. It relates to the fluctuation in the photon counts due to the discrete nature of light. We calculate δVrms,photon using simulated stellar spectra and the equations given by Bouchy et al.

53 (2001): c δVrms,photon = √ , Q Ne− where c is the speed of light, Q is the quality factor indicating the richness of RV information

that can be provided by the spectra, and Ne− is the number of photons in the spectra. The Q

factor can be calculated from √∑ W (i) Q = √∑ A0(i) and (∂A (i)/∂ν(i))2ν(i)2 W (i) = 0 , A0(i) where W (i) is the optimum weight of pixel i on the spectra, ν(i) is the frequency at pixel ∑ i, A0(i) is the photon flux at pixel i, and A0(i)=Ne− . The simulated stellar spectra are generated using high-resolution synthetic stellar spectra from Husser et al. (2013). Details

about the synthetic spectra are explained in Chapter 5. We take into account the stellar

properties and instrumental specifications including stellar rotational velocity, stellar spectral type, SNR, spectral resolution (R=100000), wavelength coverage (3800-9000 A˚), and the

detection efficiency of TOU spectrograph (Figure 4-1). The SNRs of the stars are estimated

from: √ 2 SNR0 = 998.8 × 0.75 × π × (63.5) × texp × (5500/100000/3.3) × 0.083, and

SNR0 SNRV = √ , (2.512V ) where SNR0 is the SNR of a star with V magnitude equal to 0, 998.8 is the photon flux of the zero magnitude star, 0.75 × π × (63.5)2 is the light collecting area of the Dharma Endowment

Foundation Telescope, texp is the exposure time of star to calculate the photon-noise RV precision (see Chapter 3 for details about the exposure time for stars with different brightness),

5500 is the wavelength (in Angstrom) reference point for normalization, 100000 is the spectral

resolution of TOU spectrograph, 3.3 is the pixel sampling rate, and 0.083 is the CCD efficiency

54 Figure 4-1. The overall detection efficiency of the TOU spectrograph as a function of wavelength.

at the reference point (5500 A˚), as shown in Figure 4-1. The estimated photon-noise RV

measurement precisions for the 150 stars are shown with green symbols in Figure 4-2. We

see that most stars have the photon-noise RV precisions below 0.5 m s−1 . After obtaining

δVrms,photon for the stars, their δVrms,bl and δVrms,pm values can be calculated, and these values are plotted with cyan and red symbols in Figure 4-2. From the figure, we see that the

majority of stars have RV measurement precisions around 1.0 m s−1 for the baseline case and

2.2 m s−1 for the pessimistic case. 4.1.2 Simulated Radial Velocity Data

After obtaining the precisions of RV measurements for each star, we simulate RV data

of the star based on those RV precision values. The RV data are simulated by assuming one

planet orbits the star. The planet causes the star’s radial velocity changing as: ( ) ∆V (t) = K × cos(ω0 + T ) + e × cos(ω0) ,

55 Figure 4-2. Estimated RV measurement precisions of the DPS target stars.

where (√ ) 1 + e (ξ ) T = 2 × tan−1 × tan , 1 − e 2 1 1 ( ) ξ = Ω + e × sin(Ω) + × e2 × sin(2 × Ω) + × e3 × 3 × sin(3 × Ω) − sin(Ω) , 2 8 t Ω = 2 × π × , P and ( ) ( ) −2 ( ) −1 m × sin(i) M 3 P 3 1 × × × × √ K = 28.435 ⊙ . mJupiter M 1year 1 − e2 From the equations, K is the RV semi-amplitude of the star in m s−1 , m is the planet mass,

M is the stellar mass, P is the orbital period of the planet, e is the orbital eccentricity of the

−1 planet, ∆V (t) is the change in stellar radial velocity in m s at time t, and ω0 is the phase of the planet at time t = 0. When the RV measurement uncertainty is taken into account, the

56 change in the stellar radial velocity can be rewritten as:

∆Vm(t) = ∆V (t) + δV , where δV is a random number drawn from a Gaussian distribution with the mean of zero and the standard deviation of δVrms,bl or δVrms,pm, which is estimated in the previous section.

The time t for which ∆Vm(t) is simulated is not selected purely randomly, but it is constrained by conditions faced in real observation such as good and bad weather, day and night time, the observing season of star, and monsoon season, and by the conditions required for the survey such as consecutive observations (10 consecutive nights), observation duration (450 days), and the number of observations (100). Details about those conditions are described in Chapter 3. Figure 4-3 and Figure 4-4 show two examples of simulated RV data sets. For the data

⊙ set in Figure 4-3, the RVs are simulated based on M = 1 M , m = 100 M⊕ (∼0.3 MJupiter ),

−1 P = 100 days, e =0.02, and δVrms = 1 m s . The values of stellar and planet parameters give the RV semi-amplitude of 13.8 m s−1 , which is an order of magnitude larger than the

RV measurement precision. From the figure, 100 RV data points are spread within 450 days. Although we cannot tell by looking at the plot, those data points do fall within the night time

with good weather. The region between the two vertical dash lines represents the monsoon

season where no observation takes place. The region between day 200 to day 300 represents

the time when the star is not visible in the sky, which is set to around 3 months for this data set. For the data set shown in Figure 4-4, the RVs are generated according to M = 0.5

⊙ −1 M , m = 10 M⊕ (∼0.03 MJupiter ), P = 100 days, e =0.13, and δVrms = 1 m s . The RV semi-amplitude of the star is 2.2 m s−1 , which is in the same order of magnitude of the

RV measurement precision. A hundred RV data points are also distributed within the 450-day duration. For this data set, the length of time that the star is not visible is set to around 5 months (day 280 to 430). (No observations during day 230 to 280 are just a coincidence.)

57 Figure 4-3. Simulated 100 RV measurements for 1-M⊙ star having a planet with m = 100 M⊕, P = 100 days, e =0.02 orbits around. The RV measurement precision is assumed to be 1 m s−1 .

4.1.3 Planet Detectability

To estimate the planet detectability of the survey, we generate 100 RV data sets using the same values of stellar mass, RV measurement precision, planet mass, and planet orbital period.

Those data sets are allowed to have different planet eccentricities, where the eccentricities are drawn from the planet eccentricity distribution given by Wang & Ford (2011) (Figure 4-5).

The time ts are also allowed to be different for different datasets. Figure 4-6 shows a set of

RV data simulated using the same stellar mass, RV measurement precision, planet mass, and planet orbital period as those in Figure 4-4, but different e (e = 0.0 for this data set) and t values. Then each data set is analyzed to identify the underlying planet signals. There are two main steps in this process. The first step is performing the Periodogram analysis

(Lomb-Scargle Periodogram). The Periodogram analysis fits sinusoidal functions with different periods to the data and gives ‘power’ and false-alarm probability values corresponding to each

58 Figure 4-4. Simulated 100 RV measurements for 0.5-M⊙ star having a planet with m = 10 M⊕, P = 100 days, e =0.1 orbits around. The RV measurement precision is assumed to be 1 m s−1 . sinusoidal function. The Periodogram power indicates how good a sinusoidal function fits to the data. The higher the power, the better the fitting. For the false-alarm probability (FAP), it indicates the tendency that the sinusoidal function fits to the data by coincidence. The lower

FAP indicates the lower probability. The period with the highest power and FAP less than

0.001 is used in the second step. In the second step, a number of Keplerian orbits are created based on the period received from the previous step and are fit to the data. The Keplerian fitting gives the values of planet orbital parameters that fit best to the data by means of the lowest reduced χ2 and 1−σ errors of those values. The FAP corresponding to the best fit period is also calculated. If the difference between the best fit period and the input period less than 3−σ of the period error and the FAP less than 0.001, we mask that the planet signal in this dataset can be detected. After all 100 datasets are analyzed, the detection fraction can be calculated. Figure 4-7 shows an example of planet detection probability for star with 0.5 M⊙,

59 Figure 4-5. Distribution of planet eccentricities given by Wang & Ford (2011) and used in simulating RV data.

assuming the RV measurement precision for this is 1 m s−1 . The plot includes the detection

probabilities from planets with masses 1-128 M⊕ and orbital periods from 1-450 days.

4.2 Results

4.2.1 Planet Detectability of the Dharma Planet Survey

From the method explained in the previous section, we can calculate the probability to

detect planets around the survey stars. The probabilities for the baseline case and pessimistic

case are shown in Figure 4-8 and Figure 4-9, respectively. 4.2.2 Planet Yields

To calculate the number of planets that could be detected in the survey, we use planet

occurrence rates given by Mulders et al. (2015). The occurrence rates tell how many planets present around stars. When combining the planet occurrence rates to the planet detectability

60 Figure 4-6. Simulated 100 RV measurements for 0.5-M⊙ star having a planet with m = 10 M⊕, P = 100 days, e =0.00 orbits around. The RV measurement precision is assumed to be 1 m s−1 . of the survey, the number of planets to be discovered from the survey can be estimated. The number of planets that could be detected around the 150 stars are shown in Table 4-1. 4.3 Conclusion

We use a simulation method to estimate planet detections of 150 target stars in the

Dharma Planet Survey. We found that 1 Earth, 4-25 Super-Earths, 44-55 Neptunes, 26

Sub-Jovians, and 1-8 habitable Super-Earths could be detected by the survey.

61 Figure 4-7. Planet detection probability of a 0.5-M⊙ star with RV measurement precision of 1 m s−1 .

Table 4-1. Planet yields of 150 target stars observed in the Dharma Planet Survey Case Earths Super-Earths Neptunes Sub-Jovians Habitable Super-Earths (0.7-1.4 M⊕) (1.4-5.7 M⊕) (5.7-36 M⊕) (36-128 M⊕) (1.4-5.7 M⊕) (0.8-1.2 R⊕) (1.2-2.3 R⊕) (2.3-5.7 R⊕) (5.7-11 R⊕) (1.2-2.3 R⊕) Baseline (all) 1 25 55 26 8 Pessimistic (all) 0 4 44 26 1 Baseline (F) 0 2 5 2 0 Pessimistic (F) 0 0 4 2 0 Baseline (G) 0 6 18 6 0 Pessimistic (G) 0 1 14 6 0 Baseline (K) 0 6 9 2 0 Pessimistic (K) 0 1 7 2 0 Baseline (M) 1 11 23 16 8 Pessimistic (M) 0 2 19 16 1

62 Figure 4-8. Planet detectability of the Dharma Planet Survey in the baseline case.

63 Figure 4-9. Planet detectability of the Dharma Planet Survey in the pessimistic case.

64 CHAPTER 5 EFFECT OF TELLURIC LINES ON RADIAL VELOCITY MEASUREMENTS

Observed stellar spectra obtained from ground-based RV instruments are contaminated

with atmospheric absorption lines, or telluric lines. These telluric lines are produced by

rotational-vibrational transitions of molecules in the Earth’s atmosphere, such as H2O, O2,O3,

CO, CO2, and CH4 (e.g. Smette et al. 2015). Depths and centroids of telluric lines on the spectra vary from one observation to another observation due to the change in the amounts of

atmospheric molecules, pressure, temperature, and wind speed at the time and in the direction

of observation. In addition, telluric lines temporally move with respect to the rest frame of stellar lines due to the Earth’s Barycentric motion. These behaviors of telluric lines make

it difficult to completely remove them from the stellar spectra, and their removal residuals

cause uncertainty in the RV measurements, which may not be negligible when a very high

measurement precision is desired. The study of Cunha et al. (2014) shows that telluric line residuals in HARPS spectra can

produce up to ∼1 m s−1 error in the RV measurements. This result implies that to achieve

0.1 m s−1 RV precision in the optical waveband, some treatments of telluric contamination

is needed. In redder wavelengths, the effect of telluric lines is more prominent. Bean et al.

(2010) show that telluric line residuals in CRIRES K-band spectra limit the long-term RV measurement precision to above several m s−1 .

While improvement in the RV measurement precision in both optical and NIR bands is desired and telluric contamination becomes a non-negligible source of RV error, none of the previous studies show clearly how much telluric lines and different levels of telluric residuals affect the RV measurement precision in these wavebands. Here, we do a comprehensive study on the effect of telluric lines and their residuals on RV measurements. Specifically, we use a simulation method to quantitatively estimate the RV measurement precision that can be achieved from optical and NIR stellar spectra contaminated with telluric lines and residuals.

These spectra represent observed stellar spectra obtained from ground-based RV instruments.

65 The results from our study will provide some ideas on how much telluric lines and their residuals affect RV measurement precision ways to reduce the effect and can be used as a

guideline for designing and optimizing current and upcoming RV surveys.

5.1 Simulation Method

To quantitatively estimate the effect of telluric lines on RV measurements, we simulate

observed spectra, which are contaminated with telluric lines, and measure their RV precisions. 5.1.1 Simulated Observed Spectra

We use high-resolution synthetic stellar spectra for generating observed spectra. The

synthetic spectra have advantages over empirical spectra, including higher quality and

versatility. Although synthetic spectra cannot reproduce every feature presenting in real stellar

spectra, especially in the NIR band, they are still relatively accurate and widely used in RV precision estimation (e.g. Bouchy et al. 2001; Reiners et al. 2010; Figueira et al. 2016). The

spectra are drawn from a library of synthetic stellar spectra based on the most recent stellar

atmospheric model PHOENIX-ACES (Husser et al., 2013), which can provide unprecedented

accuracy in the reproduction of stellar line features. Our work estimates the telluric effect in

the spectra of M9V to F5V stars as these stellar types are usually selected for high-precision RV surveys due to their intrinsic spectral quality allowing high-precision RV measurements.

Therefore, synthetic spectra with effective temperatures (Teff) from 2700 to 6700 K and log g = 4.5 cm s−2, which are typical values for those spectral types, are selected from the library.

The spectra we obtain cover the three wavelength regions we want to investigate the telluric impact. Those are 1) Optical, ∆λ = 0.38 − 0.62 µm, 2) Broad-Optical,

∆λ = 0.38 − 0.9 µm, and 3) Near-Infrared, ∆λ = 0.9 − 2.4 µm. Most current and

upcoming high-resolution spectrographs operate within these regions. The Optical represents

an operating wavelength range that is typical for high-resolution optical spectrographs, such

as HARPS (Pepe et al., 2000), CORALIE (Queloz et al., 1999), SOPHIE (Perruchot et al., 2008), and ESPRESSO (Pepe et al., 2010). It is only lightly contaminated by telluric lines, and the majority of these lines are shallow micro-telluric lines. The Broad-Optical expands

66 the wavelength coverage of the Optical to include redder wavelengths, R and I bands. It is useful for observing late-type (KM) stars, which emit the majority of their light at redder

wavelengths. The TOU spectrograph used in the Dharma Planet Survey (DPS; Ge et al. 2016)

is an example of a spectrograph operating in this region. The Broad-Optical has moderate

atmospheric contamination. The majority of absorption lines comes from O2-A (∼0.76 µm),

O2-B (∼0.69 µm), O2-γ (∼0.63 µm), and H2O bands. The NIR is another preferable region for observing late-type dwarfs. It contains strong telluric lines from various molecular species

including H2O, O2, CO2 and CH4. Some examples of spectrographs operating in the NIR are CARMENES (Quirrenbach et al., 2014), FIRST (Ge et al., 2014a), and SPIRou (Artigau et al.,

2014). The synthetic stellar spectra are convolved with a rotational broadening profile given by

Gray (2008) to account for intrinsic stellar rotation, which broadens stellar absorption lines.

The broadening profile is chosen to responses to the projected stellar rotational velocity (vsini) of 2 km s−1 . This value represents a low projected stellar rotational velocity, which is preferred for target stars selected for high-precision RV measurements. Next, the spectra are Doppler shifted to account for the systemic radial velocity of the star with respect to the Earth. The systemic RV can be considered as the sum of two components: 1) the stellar radial velocity with respect to the solar system’s barycenter, and 2) the Earth’s barycentric radial velocity.

Unless we specify explicitly, we use zero for the value of the first component in order to reduce the number of parameters that can affect our results. The second component, the barycentric radial velocity, causes stellar lines to move back and forth through telluric lines during the year.

We shift the spectra to response the barycentric velocities from -30 to 30 km s−1 with 0.5 km s−1 step size. The number 30 km s−1 is the amplitude of the Earth’s barycentric velocity, and we use 0.5 km s−1 step size in order to obtain a sufficient amount of spectra (121) with different telluric line features for reasonable estimation of RV precision.

After applying the rotational broadening profile and RV shifts, the spectra are multiplied with an atmospheric transmission function displayed in Figure 5-1. The atmospheric

67 BVRIZ 1.0

H2O 0.8 O2 CO2 CH 0.6 4 N2O

0.4

0.2 Transmission Fraction 0.0 0.4 0.5 0.6 0.7 0.8 0.9 Wavelength (µm)

YJHK 1.0

0.8

0.6

0.4

0.2 Transmission Fraction 0.0 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength (µm)

Figure 5-1. Atmospheric transmission function simulated by TAPAS (Bertaux et al., 2014) and used in the RV precision calculation here. Absorption lines produced from different kinds of atmospheric molecules are shown in different colors. For references, the central wavelengths of the traditional photometric bands are also shown. transmission function we use is generated from a web-based service named TAPAS (Transmissions of the AtmosPhere for AStromomical data, Bertaux et al. 2014). TAPAS generates atmospheric models using LBLRTM code (Clough & Iacono, 1995), HITRAN molecular lines (Rothman et al., 2013) and atmospheric profiles stored in the ETHER database, a French Atmospheric

Chemistry Data Center. For our RV precision calculation, we use the atmospheric profile called Standard US 1976 with the elevation of observatory = 2,120 m (Kitt Peak Observatory) and airmass = 1.2. Five absorbing molecules, H2O, O2, CO2, CH4, and N2O, are taken into account in generating the transmission function as they are the most abundant absorbing molecules and hence produce the most prominent absorption lines in the wavelength region we consider in our study (0.38-2.4 µm). In fact, to mimic variation of weather condition as

68 happening in real observation, we should use different atmospheric transmission function for different RV measurement. However, weather condition can change in various ways, depending on observing time, location, season, etc. Therefore, we choose to use only one atmospheric model for our RV precision calculation in order to see clearly the effect from a single component of telluric contamination, i.e. their movements relative to stellar lines due to the Earth’s barycentric velocity.

Next, we convolve the spectra with an instrument line spread function (LSF) to obtain the spectral resolution (R) of 100000 and then rebin them to 3 pixels per resolution. Finally, the photon noises are added to the spectra to create the signal-to-noise ratio (SNR) per pixel at

1.25 µm = 200 for the spectra. An example of simulated spectra we create is shown in Figure 5-2.

Figure 5-2. Simulated stellar spectra with (red) and without (black) telluric lines. Most absorption lines seen in the picture are from O2-B band.

69 Table 5-1. Signal to noise ratio of the simulated spectra with different effective temperatures (or spectral types) at 1.25 µm and 0.55 µm. Effective Temperature 1.25 µm 0.55 µm 2700 200 18 3200 200 44 3700 200 72 4000 200 89 4800 200 130 5200 200 146 5400 200 153 5700 200 164 6000 200 175 6300 200 185 6700 200 198

5.1.2 Precision of Radial Velocity Measurements

When doing RV measurements, a star is observed for many times and its many spectra are collected. Several factors can produce variation in the RVs measured from those spectra including real planet signals, astrophysical noises, and telluric contamination. Since we focus on the telluric effect, in our simulations we assume no planet orbits the star and no other noise sources besides the photon noise, which is the fundamental noise of RV measurements, and telluric contamination.

To calculate the value of RV precision for one star, we simulate a set of 121 observed spectra using the same input parameters except for the barycentric RV, which we assign different values for different spectra. We assign those spectra the barycentric RVs ranging from -30 to 30 km s−1 with 0.5 km s−1 step size, one spectrum for one barycentric RV value. Therefore, those spectra are different from each other only in the locations of telluric lines relative to the locations of stellar lines. These spectra mimic 121 spectra of the star observed at different barycentric velocities (or different time of the year). Next, these spectra are corrected for the given barycentric velocities, and their RVs are measured using the Least-Square Matching method (Anglada-Escud´e& Butler, 2012), as done in RV measurements of real observed spectra. Generally, in the Least-Square Matching method, a high SNR spectra from the same star to be measured RVs is usually used as a template in

70 the RV extraction process. For our work, we use a spectrum of ‘the same star’ but without telluric contamination as the template. An example of radial velocities obtained from a set of spectra simulated using the same input parameters (except the barycentric RVs) is shown in Figure 5-3. Finally, after we obtain 121 RV values from the 121 spectra, we calculate the standard deviation of these RVs. The value of the standard deviation represents the precision of RV measurements from observed spectra of one star, the RV error caused by telluric lines is calculated from: √ 2 − 2 Errortelluric = RVmeasured RVphoton,

Figure 5-3. Measured radial velocity values of 121 observed spectra (Teff=5700) simulated using the same set of input parameter values, except the barycentric RV, which is assigned different values for different spectra. The black line shows the results when considering telluric contamination while the red line shows the results when there is no telluric contamination (only photon noise).

We investigate whether there are systematics in our simulations. First, we simulate a set of observed spectra in a similar way we do above, except we do not add the photon-noises and telluric contamination. The measured RV precision we obtain from this dataset is very close

71 to zero, much less than 0.1 m s−1 . Secondly, we simulate another set of observed spectra where the photon-noises are included but no telluric lines are added. The RV precision we obtain this time is consistent with the photon-limited RV precision calculated from the formula given by Bouchy et al. (2001). These tests indicate that any systematics in our simulations are negligible in the RV precision values we obtained. 5.1.3 Telluric Line Correction

5.1.3.1 The masking method

In RV measurements, there are two methods generally used to correct telluric contamination on observed spectra and reduce the RV measurement errors. The first method is masking or discarding some spectral regions that are contaminated with relatively strong telluric lines from RV extraction (e.g. Pepe et al. 2002). This method is quite suitable when telluric lines spread just within small spectral regions. However, if telluric lines are widespread and a large fraction of the spectrum is masked out, the increased photon-noise uncertainty will outweigh the precision gained from masking the telluric lines.

In our simulations, the spectral regions where telluric lines are stronger than the desired level, e.g. 10 percent, and their 30 km s−1 wings are identified and neglected from the RV precision calculation, as shown in blue symbol in Figure 5-4. The 30 km s−1 is for accounting the RV shifts of stellar lines relative to the identified telluric lines during the year due to the

Earth’s barycentric motion. The percentages of the remaining spectral region that can be used in the RV precision calculation after masking different levels of telluric lines are shown in Table 5-2.

5.1.3.2 The modeling method

The other method to mitigate the effect of telluric lines is to use a telluric line model to subtract out the telluric contamination in the observed spectrum, hereafter the modeling method. There are two techniques to generate the telluric model: the empirical and synthetic techniques. For the empirical technique, the telluric model is derived from the spectrum of a telluric standard star. A telluric standard star is a fast-rotating early-type (usually B or A type)

72 Figure 5-4. Spectral regions that are masked out from RV precision calculation due to strong (>10 percent) telluric lines.

star of which intrinsic spectra are almost featureless. Hence, most absorption lines present in

its spectra originate from the Earth’s atmosphere. To obtain an accurate telluric model for the correction, observation of the telluric standard star should be done around the same time and

airmass of the science target to be corrected. Implementation of this technique was presented

in Vacca et al. (2003). The second technique to create the telluric model is the synthesis technique. In this technique, a telluric model is generated using three main components: 1) a radiative transfer code, e.g. LBLRTM (Clough et al., 2005), 2) molecular line data giving line information including position, strength, and broadening, e.g. HITRAN (Rothman et al., 2013), and 3) an atmospheric profile describing temperature, pressure, and molecular abundance as a function of height from the observing location. There are a number of tools for generating telluric lines models based on the synthetic technique, e.g. Telfit (Gullikson et al., 2014),

TAPAS (Bertaux et al., 2014), and Molecfit (Smette et al., 2015). Some examples of telluric

73 Table 5-2. The fraction of stellar spectrum remaining for the RV precision calculation after contaminating telluric lines with different strengths are masked. Spectral Region Masking Level Remaining Spectral Fraction (%) (%) Optical 1 89 2 94 5 98 10 99 20 100 50 100 90 100 Broad-Optical 1 65 2 74 5 82 10 87 20 92 50 95 90 98 NIR 1 10 2 17 5 27 10 36 20 45 50 61 90 73

correction using synthetic models are presented in Bailey et al. (2007), Seifahrt et al. (2010), and Kausch et al. (2015). Although the modeling method is shown to be very efficient, it still cannot fully remove

telluric lines from science spectra. This is due to some inaccuracy of the telluric model, which

is caused by several factors. For example, the model from the empirical technique can be

contaminated by intrinsic features of the telluric standard star, e.g. Hydrogen line series. In

addition, the standard star cannot be observed at the same time and direction of the science target, so the obtained telluric model may not represent the true telluric contamination. As

for the synthetic technique, accuracy of the model can be degraded by assumptions and

74 simplifications made in the radiative transfer code, the incompleteness of the molecular line database, and the absence of a real-time atmospheric profile. Telluric residuals from this

method can also cause a non-negligible error to the RV measurements.

As our work does not focus on the technical details of the modeling method, we simply

assume that we can eventually reduce the strengths of telluric lines to a fraction of the original strengths, such as 20 percent. These remaining lines are defined as telluric residuals in the

simulated observed spectra. Therefore, in practice to obtained 20 percent residuals, or 80

percent removal, we multiply the telluric transmission function with 0.2 before multiplying it

with the stellar spectra. Since it is unlikely to recover true stellar flux in spectral regions with

saturated or nearly saturated telluric lines using the modeling method, we mask out the regions where telluric lines (before being corrected) are deeper than 90 percent, as well as their 30

km s−1 vicinity, from RV calculation. The percentages of the remaining regions after masking

these saturated lines are listed in Table 5-2. In other words, for the modeling method here,

we mask out saturated telluric lines (depth > 90 percent) and reduce the strength of the remaining lines by multiplying them with a constant number. An example of simulated spectra

contaminated with telluric residuals (20 percent) is shown in Figure 5-5.

5.2 Results

5.2.1 The Effect of Telluric Lines in Optical, Broad-Optical, and NIR Regions

Figure 5-6 shows the estimated RV error caused by telluric lines in the Optical,

Broad-Optical and NIR spectra. Depending on the stellar spectral type, telluric lines in the Optical add around 0.1-0.4 m s−1 error to the measured RV precision. The value is largest

in late-K and early-M dwarfs and decreases in both ends of spectral types. Assuming that

optical spectra with SNR∼200 (normalized at 1.25 µm) are obtained, in this case, the effect

of telluric lines is not prominent and the measured RV precision is dominated by photon noise

(Figure 5-7). On the other hand, if the SNR is increased to ∼1000, which pushes down the photon-noise to below the RV amplitudes of one Earth-mass planets in HZs, the effect of

telluric lines become important and can prevent the detection of those planets, except in mid-

75 Figure 5-5. (Red) Simulated observed spectra after telluric lines are modeled to 80 percent, giving 20 percent residuals. Locations where original telluric lines stronger than 90 percent are masked out from the RV calculation and shown in the blue symbol. (Black) The observed spectra simulated using the same parameters but without telluric contamination.

to late-M dwarfs where the RV signals of one Earth-mass planet in HZs are relatively large

and the effect of telluric lines is small (Figure 5-8). For the Broad-Optical and NIR bands, the effect of telluric lines is so strong that these bands cannot be used to detect planets below

∼5 M⊕. The RV errors caused by telluric lines in the Broad-Optical and NIR spectra are ∼4 m s−1 and ∼40 m s−1 , respectively.

5.2.2 Treatment of Telluric Contamination

5.2.2.1 Optical wavelength region

From Figure 5-8, we clearly see that telluric lines in the optical band can prevent the detection of one Earth-mass planet in HZs around solar-type stars and early-M dwarfs.

However, when some strong telluric lines are masked out, improvement in the RV measurement

precision can be seen. In Figure 5-9, masking telluric lines stronger than ∼5-10 percent is

sufficient for detecting one Earth-mass planet around early-M dwarfs, but masking weaker lines

76 Figure 5-6. Radial velocity measurement error caused by telluric lines in the Optical, Broad-Optical, and NIR regions.

is needed for detecting those planets around more massive stars. The result indicates that to

detect an Earth analog, or one Earth-mass planet in HZs around Sun-like stars, all telluric lines

stronger than ∼1 percent need to be masked out.

Modeling method is another method to correct telluric contamination. Figure 5-10 shows that one Earth-mass planet in HZs of M dwarfs can be detected if telluric lines on observed

spectra can be modeled and removed to ∼50 percent. To detect those planets around more

massive stars, better telluric modeling and removal are required. To detect Earth analogs,

telluric lines must be modeled and removed to ∼80 percent. 5.2.2.2 Broad-Optical and NIR wavelength regions

For late-type stars like M dwarfs, they are faint in the optical waveband and hard to

obtain a very high SNR, resulting in high photon-noise. Considering the result in Figure 5-7, the broad-optical and NIR bands can provide RV precision advantage over the optical band in

M dwarfs when there is no telluric contamination on observed spectra. However, telluric lines

77 Figure 5-7. Radial velocity precision of the Optical (0.38-0.62 µm), Broad-Optical (0.38-0.9 µm), and NIR (0.9-2.4 µm) spectra with different stellar spectral types. The solid lines show the results when telluric lines are included in the simulations, and the dot lines show the results when considering only the photon-noise (no telluric contamination in the simulated spectra), assuming SNR=200. are always imprinted in observed spectra from ground-based instruments and those lines create large RV measurement error. Using the masking technique to correct telluric contamination in the Broad-Optical band can significantly reduce the RV error and allow it to have better RV precision than the

Optical band. As shown in Figure 5-11, late M dwarfs need ∼10 percent masking and ∼5 percent masking is required for early-type M dwarfs. For the modeling method, it also gives the Broad-Optical band better RV precision than the Optical band when ∼90 percent telluric lines are removed from observed spectra (Figure 5-12).

In the NIR band, telluric lines are ubiquitous, so the masking method cannot make it gains better RV precision than the Optical band, except in ∼M9V where a marginal advantage can be seen (Figure 5-13). On the other hand, the modeling technique can give the RV precision

78 Figure 5-8. Radial velocity precision of the Optical (0.38-0.62 µm), Broad-Optical (0.38-0.9 µm), and NIR (0.9-2.4 µm) spectra with different stellar spectral types. The solid lines show the results when telluric lines are included in the simulations, and the dot lines show the results when considering only the photon-noise (no telluric contamination in the simulated spectra), assuming SNR=1000.

advantage. As shown in Figure 5-14, ∼95 percent telluric modeling is required to make the

NIR band achieves better RV precision than the Optical band for observing late M dwarfs, and for early to mid-M dwarfs ∼98-99 percent telluric modeling is needed.

It is important to note that if high SNR spectra can be obtained, it is difficult to gain the

advantage from broad-optical and NIR regions. As shown in Figure 5-15 to Figure 5-16, we need better telluric correction than their low SNR counterparts.

5.3 Conclusions

We use simulations to study the effect of telluric lines on RV measurements. Several questions regarding the telluric effect can be answered by our results. First, telluric lines contribute ∼0.2, 4, and 40 m s−1 error to RV measurements in the Optical, Broad-Optical, and NIR bands, respectively. The error in the Optical band is large enough to prevent the detection

79 Figure 5-9. (Red) Radial velocity precision of the Optical region when some strong telluric lines are masked out from the spectra, assuming SNR=1000. The solid, dash, dot, and dash-dot red lines indicate 10, 5, 2, and 1 percent masking, respectively. (Black) Radial velocity precision of the Optical region for no telluric correction case (solid), and the photon-noise (dot) case. of Earth analogs, which are aimed for planet surveys using next-generation RV instruments such as ESPRESSO (Pepe et al., 2010). Masking telluric lines stronger than ∼1 percent or modeling them to ∼80 percent can substantially reduce the RV error and provide the Optical band the RV precision for detecting the Earth analogs. When a high SNR cannot be obtained with the optical waveband, using red wavelengths can give RV precision advantage.

80 Figure 5-10. (Red) Radial velocity precision of the Optical region when telluric lines are subtracted out from the spectra, assuming SNR=1000. The solid, dash, dot, and dash-dot red lines indicate 50, 20, 10, and 5 percent modeling, respectively. (Black) Radial velocity precision of the Optical region for no telluric correction case (solid), and the photon-noise (dot) case.

81 Figure 5-11. Radial velocity precision of the Broad-Optical region when strong telluric lines are masked out, assuming SNR=200.

82 Figure 5-12. Radial velocity precision of the Broad-Optical region when telluric lines are subtracted out from the spectra, assuming SNR=200.

83 Figure 5-13. Radial velocity precision of the NIR region when strong telluric lines are masked out, assuming SNR=200.

84 Figure 5-14. Radial velocity precision of the NIR region when telluric lines are subtracted out from the spectra, assuming SNR=200.

85 Figure 5-15. Radial velocity precision of the Broad-Optical region when strong telluric lines are masked out, assuming SNR=1000.

86 Figure 5-16. Radial velocity precision of the Broad-Optical region when telluric lines are subtracted out from the spectra, assuming SNR=1000.

87 CHAPTER 6 CURRENT STATUS OF THE DHARMA PLANET SURVEY AND EARLY SCIENCE RESULTS 6.1 Instrument and Pipeline Performance

TOU spectrograph used in the DPS is placed in a vacuum chamber and thermal

enclosure, giving the pressure and temperature variations below 0.01 mTorr and 1 mK.

These small variations provide the long-term RV stability of ∼0.73 m s−1 (RMS) without

calibration (Ge et al., 2016), which is about 1.5 times better than that achieved by HARPS

(Lovis & Pepe, 2007). Although the TOU spectrograph is very stable, some small drifts due to external environment perturbations are still presented. These instrumental drifts are

tracked and corrected using two ThAr lamps: the ‘reference lamp’ and the ‘operation lamp’.

The ‘reference lamp’ is used to correct the long-term (nightly) instrumental drifts, while the

‘operation lamp’ is used to correct intra-night instrumental drifts. Each of them has its own correction error, and the total error is calculated from the quadratic summation of the errors

from the two lamps. The total instrumental drift correction error measured over two months is

0.36 m s−1 as shown in Figure 6-1, which is comparable to that achieved by HARPS (Lovis &

Pepe, 2007).

To demonstrate on-sky RV performance, an RV stable star, HD 10700 (Tau Ceti), was monitored in 2015. This star is known as one of the most RV-stable bright G dwarfs (Pepe

et al., 2011), making it an ideal star for testing and developing the DPS data processing

pipeline. In each night, the star received three consecutive 10-minute exposures, which can

help to minimize the high-frequency stellar oscillation noise (Dumusque et al., 2011), and those exposures were combined to calculate the RV for that night. Figure 6-2 shows 35 RVs of the

star obtained in two months. The RVs have the RMS of 0.9 m s−1 , which is comparable to that achieved by HARPS (Pepe et al., 2011).

The ability of the DPS to detect low-mass planets is demonstrated by observation of know low-mass planet host stars. Figure 6-3 and Figure 6-4 display RV measurements of HD 1461

and HD 190360 from our survey. The stars are known to host a 7-M⊕ planet (Rivera et al.,

88 Figure 6-1. Correction residuals after small instrumental drifts are corrected with ThAr measurements over 60 days.

Figure 6-2. Radial velocity measurements of the RV stable star HD 10700 (Tau Ceti) obtained from TOU spectrograph and our data reduction pipeline (Ma & Ge 2018, submitted).

89 2010) and an 18-M⊕ planet (Vogt et al., 2005), respectively. The RV curve of each star shows a clear feature of the orbiting planet. The best fit planet models to our RV data give the planet mass of 7 M⊕ for HD 1461 and 18 M⊕ for HD 190360, which agree with the published

values. These results indicate that our survey has the potential to detect low-mass planets.

Figure 6-3. Phased radial velocities of the known planet host star HD 1461 obtained from TOU spectrograph and our data reduction pipeline (Ma & Ge 2018, submitted). The best fit planet model (solid line) gives the planet minimum mass (Msini) of 7 M⊕, the orbital period of 5.8 days, and the eccentricity of 0.1.

6.2 Observation

We have been tracking the observation of the survey stars since the DPS was started on

October 10th, 2016. Until now (April 3rd , 2018), 93 stars have been monitored so far. Fourteen of those stars were found or suspected to be spectroscopic binaries, so they were dropped from the survey. Only 79 stars remain as the active survey targets. All observed stars, both the active and dropped ones, are listed in Table 6-1. From the 79 active targets, 23 percent have received more than 100 visits, 28 percent have received 50-100 observations, and the remaining

49 percent have received less than 50 observations.

90 Figure 6-4. Radial velocities of the known planet host star HD 190360 obtained from TOU spectrograph and our data reduction pipeline (Ma & Ge 2018, submitted). The best fit planet model (solid line) gives the planet minimum mass (Msini) of 18 M⊕, the orbital period of 18 days, and the eccentricity of 0.0.

Figure 6-5 displays the cumulative exposure time since the beginning of the survey. The blue line shows the result from the simulations in Chapter 3 while the magenta shows the

result of the real survey. The real survey has already used about 2500 hours for observation,

which are about 80 percent of the value predicted from the simulations. The difference between the prediction and the real observation is due to engineering works and technical

issues at the observatory. Otherwise, according to the slopes from both cases, the real

observation mostly agrees with the simulations.

91 Figure 6-5. The cumulative exposure time of the DPS since October 10th, 2016. The blue line shows the result from the simulations in Chapter 3. The magenta line shows the result from the real observation.

92 Table 6-1. The DPS target stars observed between October 2016 - April 2018. Star Name V Spec. Exposure Time Observations Median SNR Status (Minutes) GJ 1043 5.3 F5V 5 3 56 active HD 100563 5.8 F5.5V 15 22 65 active HD 102870 3.6 F9V 10 7 101 active HD 104304 5.5 G8V 30 44 73 active HD 10700 3.5 G8V 10 207 137 active HD 10780 5.6 K0V 30 107 92 active HD 11007 5.8 F8V 76 30 85 active HD 110833 7.0 K3V 50 44 67 active HD 110897 6.0 F9V 30 75 81 active HD 111631 8.5 M0V 50 21 24 active HD 111998 6.1 F6V 30 13 70 dropped, SB2 HD 114710 4.3 F9.5V 10 118 114 active HD 115043 6.8 G1V 30 129 56 active HD 117043 6.5 G6V 30 65 63 active HD 119850 8.5 M2V 50 29 26 active HD122676 7.1 G7V 50 25 60 dropped, SB2 HD 122742 6.3 G6V 30 17 80 dropped, SB2 HD 126053 6.3 G1.5V 30 41 58 active HD 127334 6.3 G5V 30 63 72 active HD 128165 7.2 K3V 50 53 68 active HD 132254 5.6 F8V 30 97 100 active HD 1326 8.1 M2V 50 40 33 active HD 134323 6.2 G6V 30 24 87 dropped, SB2 HD 13579 7.1 K2V 50 109 56 active HD 142373 4.6 F8V 10 149 102 active HD 147379 A 8.6 M1V 50 30 28 active HD 15335 5.9 G0V 30 105 82 active HD 153597 4.9 F8V 10 13 93 dropped, SB2 HD 154633 6.1 G5V 122 30 78 active HD 157214 5.4 G0V 15 69 86 active HD 158633 6.4 K0V 30 73 69 active HD 160346 6.5 K3V 30 44 65 dropped, SB2 HD 16673 5.8 F8V 30 81 65 dropped, SB2 HD 168009 6.3 G1V 30 74 81 active HD 168151 5.0 F5V 15 70 96 active HD 178428 6.1 G4.5V 30 17 94 dropped, SB2 HD 183650 7.0 G5V 30 13 49 active HD 184960 5.7 F7V 30 28 98 active HD 185144 4.7 K0V 10 177 91 active HD 18757 6.7 G1.5V 30 7 61 active

93 Table 6-1. Continued Star Name V Spec. Exposure Time Observations Median SNR Status (Minutes) HD 190007 7.5 K5V 50 8 51 active HD 190404 7.3 K1V 50 31 50 active HD 191862 5.8 F7V 30 29 68 active HD 193664 5.9 G3V 30 54 82 active HD 194012 6.2 F7V 30 36 77 active HD 195838 6.1 F9V 30 35 69 active HD 195987 7.1 G9V 50 20 67 dropped, SB2 HD 199305 8.6 M1V 50 6 22 active HD 210027 3.8 F5V 10 28 147 dropped, SB2 HD 211476 7.0 G2V 50 40 60 active HD 21197 7.8 K4V 50 2 28 active HD 217014 5.5 G2V 15 98 87 dropped, SB2 HD 219134 5.6 K3V 15 176 98 active HD 219623 5.5 F7V 30 89 96 dropped, SB2 HD 224465 6.6 G4V 30 83 60 active HD 22484 4.3 F9V 15 199 89 active HD 26965 4.4 K0V 10 123 84 active HD 29883 8.0 K5V 50 2 37 active HD 30562 5.8 G2V 30 17 63 active HD 31966 6.8 G2V 30 87 47 active HD 33093 6.0 G0V 15 37 47 dropped, SB2 HD 33632 6.5 F8V 30 11 67 active HD 36395 8.0 M1.5V 50 2 29 active HD 38230 7.4 K0V 50 87 55 active HD41330 6.1 G0V 30 129 80 active HD 43947 6.6 F8V 30 10 49 active HD 45067 5.9 F9V 30 27 56 active HD 4813 5.2 F7V 15 52 70 active HD 48682 5.2 F9V 15 22 75 active HD 5015 4.8 F9V 10 58 90 active HD 55575 5.6 F9V 30 39 98 active HD 58551 6.5 F6V 30 6 64 active HD 58855 5.4 F6V 15 148 83 active HD 59380 5.9 F6V 15 61 58 dropped, SB2 HD 65583 7.0 K0V 50 56 60 active HD 69830 6.0 G8V 15 183 56 active HD 69897 5.1 F6V 15 72 87 active HD 70110 6.2 G0V 30 67 61 active HD 71148 6.3 G1V 30 126 76 active HD 73344 6.9 F6V 30 30 35 active

94 Table 6-1. Continued Star Name V Spec. Exposure Time Observations Median SNR Status (Minutes) HD 76151 6.0 G2V 30 57 63 active HD 82106 7.2 K3V 50 56 43 active HD 84737 5.1 G0.5V 15 145 96 active HD 86728 5.4 G3V 15 64 85 active HD 88230 6.6 K6V 30 76 70 active HD 88446 7.8 G1V 50 10 36 active HD 89269 6.7 G4V 30 16 53 active HD 90839 4.8 F8V 10 23 73 active HD 91889 5.7 F8V 30 62 61 active HD 9407 6.5 G6.5V 30 179 68 active HD 94765 7.3 K0V 50 49 47 active HD 95735 7.5 M2V 50 119 62 active HIP 65859 9.1 M1V 50 1 14 active

95 6.3 Planet Detection

After monitoring the 79 stars for about one and a half year, the DPS can discover one

super-Earth (8.5 M⊕) orbiting a K dwarf star, HD 26965, (Ma et al., 2018) and identify

additional 15 planet candidates around 9 stars. Most of them have the masses less than

the mass of Saturn (95 M⊕) and the orbital periods shorter than the orbital period of

Mercury (88 days). Properties of those planets are listed in Table 6-2, and the phased RV measurements of their host stars are displayed in Figure 6-6 to Figure 6-21. In addition,

Figure 6-22 and Figure 6-23 show the locations of the planets on the mass-period detectability

planes calculated in Chapter 4. We see that most of the planets (except HD 26965 b) have the

masses and periods falling in the region of high detection probability (≳ 0.7). We consider the planet detection rates of F, G, and K dwarfs, which currently receive sufficient RV data points. (M dwarfs currently receive only a few observations.) For F dwarfs, the detection rate is 25 percent. That is we have observed 32 F dwarfs and detected 8 planets. For G and K dwarfs, the detection rates are 14 percent and 17 percent, respectively.

Comparing these rates to the detection rates estimated from the simulations in Chapter 4, which are 17-25 percent for F dwarfs, 32-46 percent for G dwarfs, and 33-57 percent for K

dwarfs, we see that the detection rates from the real observation for G and K dwarfs are lower

than predicted. This is because when we selected the target stars, we avoided choosing known

planet host stars, which mostly are G and K types. Bright nearby G and K dwarfs have been

observed extensively from previous RV surveys, such as the Eta-Earth survey (Howard et al., 2010a), and a number of planets were discovered around those stars. On the other hand,

F dwarfs have not been observed as much as G and K dwarfs due to their large RV jitters.

In other words, our G and K type target stars are biased toward the stars with no planet

detection.

96 Table 6-2. List of the planet and planet candidates from the Dharma Planet Survey. Name Mass Period Eccentricity RV Semi-Amplitude Host Star Type −1 (M⊕) (days) ( m s ) HD 26965 b 8.5 42.38 0.04 1.8 K Planet Candidate 1 b 34.5 52 0.1 5.2 F Planet Candidate 2 b 128 88.4 0.3 17.1 F Planet Candidate 2 c 11.9 2.24 0.08 5.2 F Planet Candidate 3 b 26.3 15.63 0.0 (fixed) 6.1 F Planet Candidate 4 b 17.9 39.88 0.1 3.2 F Planet Candidate 5 b 41.4 11.8 0.0 13 K Planet Candidate 5 c 28.9 5.46 0.4 12.8 K Planet Candidate 6 b 41.7 1.5 0.0 23 G Planet Candidate 6 c 49.0 6 0.0 17 G Planet Candidate 6 d 81.4 46.3 0.3 15 G Planet Candidate 6 e 114 151 0.0 13.5 G Planet Candidate 7 b 55.6 109.8 0.2 6.5 F Planet Candidate 7 c 13.2 2.29 0.0 (fixed) 3.5 F Planet Candidate 8 b 37.4 49 0.3 6.2 F Planet Candidate 9 b 31.3 14.8 0.3 8.6 G

Figure 6-6. Phased radial velocities of the star HD 26965 (Ma et al., 2018). The best-fit Keplerian planet model (black solid line) corresponds to a planet with the properties listed in Table 6-2.

97 Figure 6-7. Phased radial velocities of the star with the planet candidate 1 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

98 Figure 6-8. Phased radial velocities of the star with the planet candidate 2 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

99 Figure 6-9. Phased radial velocities of the star with the planet candidate 2 c. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

100 Figure 6-10. Phased radial velocities of the star with the planet candidate 3 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

101 Figure 6-11. Phased radial velocities of the star with the planet candidate 4 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

102 Figure 6-12. Phased radial velocities of the star with the planet candidate 5 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

103 Figure 6-13. Phased radial velocities of the star with the planet candidate 5 c. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

104 Figure 6-14. Phased radial velocities of the star with the planet candidate 6 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

105 Figure 6-15. Phased radial velocities of the star with the planet candidate 6 c. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

106 Figure 6-16. Phased radial velocities of the star with the planet candidate 6 d. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

107 Figure 6-17. Phased radial velocities of the star with the planet candidate 6 e. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

108 Figure 6-18. Phased radial velocities of the star with the planet candidate 7 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

109 Figure 6-19. Phased radial velocities of the star with the planet candidate 7 c. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

110 Figure 6-20. Phased radial velocities of the star with the planet candidate 8 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

111 Figure 6-21. Phased radial velocities of the star with the planet candidate 9 b. The best-fit Keplerian planet model (red solid line) corresponds to a planet with the properties listed in Table 6-2.

112 Figure 6-22. The big circles show the locations of the discovered planet and planet candidates from the DPS on the mass-period detectability plane (baseline case) calculated in Chapter 4. The small orange dots are the Kepler planet candidates.

113 Figure 6-23. The big circles show the locations of the discovered planet and planet candidates from the DPS on the mass-period detectability plane (pessimistic case) calculated in Chapter 4. The small orange dots are the Kepler planet candidates.

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119 BIOGRAPHICAL SKETCH Sirinrat Sithajan was born in Bangkok, the capital city of Thailand. She went to middle-school and high-school in her hometown. She became interested in sciences and performed very well in sciences classes. In the last year of high-school, she was awarded a

4-year full scholarship for studying at Faculty of Science, Mahidol University, one of the top universities in Thailand. As she had a particular interest in the nature of celestial objects and universe, she attended physics major, which offered some astronomy classes. During her college study, her interest in astronomy was considerably developed, and she wanted to pursue advanced education in the US, which offers various courses and research opportunities in this field. She graduated from Mahidol University with first class honors and was granted a full scholarship from National Astronomical Research Institute of Thailand (NARIT) for her doctoral degree study. She got accepted from the Department of Astronomy, University of

Florida and joined Dr. Jian Ge’s research group for exoplanet hunting. After getting PhD, she will go back to Thailand to work at NARIT as an astronomer.

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