# Understanding the Impacts of Stellar Companions on Planet Formation and
Evolution: A Survey of Stellar and Planetary Companions within 25 pc

Lea A. Hirsch  Kavli Center for Particle Astrophysics and Cosmology, Stanford
University, Stanford, CA 94305, USA  Lee Rosenthal  California Institute of
Technology, 1200 E California Blvd, Pasadena, CA 91125, USA  Benjamin J.
Fulton  NASA Exoplanet Science Institute, Caltech/IPAC-NExScI, 1200 East
California Boulevard, Pasadena, CA 91125, USA  Andrew W. Howard  California
Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA
David R. Ciardi  NASA Exoplanet Science Institute, Caltech/IPAC, 1200 East
California Boulevard, Pasadena, CA 91125, USA  Geoffrey W. Marcy  University
of California Berkeley, 501 Campbell Hall, Berkeley, CA 94709, USA  Eric L.
Nielsen  Department of Astronomy, New Mexico State University, P.O. Box 30001,
MSC 4500, Las Cruces, NM 88003, USA  Kavli Center for Particle Astrophysics
and Cosmology, Stanford University, Stanford, CA 94305, USA  Erik A. Petigura
Department of Physics and Astronomy, University of California, Los Angeles, CA
90095, USA  Robert J. de Rosa  European Southern Observatory, Alonso de
Córdova 3107, Vitacura, Santiago, Chile  Kavli Center for Particle
Astrophysics and Cosmology, Stanford University, Stanford, CA 94305, USA
Howard Isaacson  University of California Berkeley, 501 Campbell Hall,
Berkeley, CA 94709, USA  Lauren M. Weiss  Institute for Astronomy, 2680
Woodlawn Dr., Honolulu, HI 96822, USA  Evan Sinukoff  Institute for Astronomy,
2680 Woodlawn Dr., Honolulu, HI 96822, USA  Bruce Macintosh  Kavli Center for
Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305,
USA

(Accepted December 7, 2020)

###### Abstract

We explore the impact of outer stellar companions on the occurrence rate of
giant planets detected with radial velocities. We searched for stellar and
planetary companions to a volume-limited sample of solar-type stars within 25
pc. Using adaptive optics (AO) imaging observations from the Lick 3-m and
Palomar 200$\arcsec$ Telescopes, we characterized the multiplicity of our
sample stars, down to the bottom of the main sequence. With these data, we
confirm field star multiplicity statistics from previous surveys. We
additionally combined three decades of radial velocity (RV) data from the
California Planet Search with newly-collected RV data from Keck/HIRES and the
Automated Planet Finder/Levy Spectrometer to search for planetary companions
in these same systems. Using an updated catalog of both stellar and planetary
companions, as well as detailed injection/recovery tests to determine our
sensitivity and completeness, we measured the occurrence rate of planets among
the single and multiple star systems. We found that planets with masses in the
range of 0.1–10 $M_{\mathrm{J}}$ and with semi-major axes of 0.1–10 AU have an
occurrence rate of $0.18^{+0.04}_{-0.03}$ planets per star when they orbit
single stars, and an occurrence rate of $0.12\pm 0.04$ planets per star when
they orbit a star in a binary system. Breaking the sample down by the binary
separation, we found that only one planet-hosting binary system had a binary
separation $<100$ AU, and none had a separation $<50$ AU. These numbers
yielded planet occurrence rates of $0.20^{+0.07}_{-0.06}$ planets per star for
binaries with separation $a_{B}>100$ AU, and $0.04^{+0.04}_{-0.02}$ planets
per star for binaries with separation $a_{B}<100$ AU. The similarity in the
planet occurrence rate around single stars and wide primaries implies that
wide binary systems should actually host more planets than single star
systems, since they have more potential host stars. We estimated a system-wide
planet occurrence rate of 0.3 planets per wide binary system for binaries with
separations $a_{B}>100$ AU. Finally, we found evidence that giant planets in
binary systems have a different semi-major axis distribution than their
counterparts in single star systems. The planets in the single star sample had
a significantly higher occurrence rate outside of 1 AU than inside 1 AU by
nearly $4\sigma$, in line with expectations that giant planets are most common
near the snow line. However, the planets in the wide binary systems did not
follow this distribution, but rather had equivalent occurrence rates interior
and exterior to 1 AU. This may point to binary-mediated planet migration
acting on our sample, even in binaries wider than 100 AU.

††journal: AJ††facilities: Keck I/HIRES; Automated Planet Finder/Levy
Spectrometer; Lick Shane/ShaneAO; Palomar/PHARO††software: RadVel(Fulton et
al., 2016); RVSearch (Rosenthal et al. 2019, in prep); SpecMatch(Petigura et
al., 2017); isoclassify (Huber et al., 2017); PyKLIP (Wang et al., 2015);
photutils (Bradley et al., 2017); astropy (Astropy Collaboration et al., 2013;
Price-Whelan et al., 2018); matplotlib (Hunter, 2007)

##  1 Introduction

Nearly half of all solar-type stars have at least one stellar or brown dwarf
companion (Raghavan et al., 2010), and planets around G- and K-type stars
appear to be quite common (Batalha, 2014). Stellar multiplicity may impact
planet formation and evolution in many ways. Gravitational perturbations from
a stellar companion have been proposed to explain the inward migration and
spin-orbit misalignment of hot Jupiters (Wu et al., 2007; Batygin, 2013;
Storch et al., 2014), but observations of misaligned hot Jupiters failed to
reveal a correlation between orbital misalignment and the presence of an
imaged stellar companion (Ngo et al., 2015). Simulations of planets in orbits
around one member of a binary pair indicate that dynamically stable orbits can
exist at semi-major axes within a few tenths of the binary separation,
depending on the binary mass ratio and eccentricity (Holman & Wiegert, 1999;
Quintana et al., 2007). However, simulations also indicate that even very
widely spaced stellar companions can perturb the orbits of planets, causing
migration and possible ejection from the system (Kaib et al., 2013). Stellar
companions may also truncate protoplanetary disks, limiting planet formation
in binary systems (Jang-Condell et al., 2008).

Observational efforts aimed at characterizing these effects have primarily
focused on the Kepler and TESS samples, since transit-based surveys do not
have a selection bias against stellar multiplicity (rather, binaries are
likely to be over-represented in these flux-limited surveys). For the Kepler
field planet hosts, Kraus et al. (2016) found that stellar multiplicity is
lower at separations $<47^{+59}_{-23}$ AU, relative to stars in the solar
neighborhood. Other studies have reported similar suppression of close binary
companions to Kepler planet hosts (e.g. Wang et al., 2014), but the results of
multiplicity surveys of the Kepler field are not yet conclusive, with Horch et
al. (2014) and Matson et al. (2018) failing to find evidence for the reported
suppression.

More recently, Ziegler et al. (2019) have performed a similar search for
stellar companions to TESS objects of interest, using speckle interferometry
from HRCam on SOAR. They too report a deficit of close binary companions
within 100 AU of their sample stars.

By combining these disparate observational efforts to characterize the stellar
multiplicity of Kepler planet hosts, Moe & Kratter (2019) attempted to map out
the planet suppression as a function of binary separation. They found general
consistency between various surveys within their stated uncertainties and
sensitivity regimes, and were able to fit a piece-wise linear function to
planet suppression $S_{bin}$ vs. $\log{a_{bin}}$. They found that planet
suppression was negligible for binaries wider than 200 AU, and found total
suppression of planet formation for binaries within 1 AU.

Other studies have focused on giant planet hosts, and found that hot Jupiters
seem to form preferentially in binary or multiple systems. Wang et al. (2015)
and Ngo et al. (2015, 2016) found that stellar multiplicity is augmented by
nearly a factor of 3 around stars that host transiting hot Jupiters (a
$4.4\sigma$ effect), despite the fact that few of these binaries seem to have
configurations favorable for exciting Kozai-Lidov oscillations in the inner
planet.

Altogether, the distances to the Kepler field and the difficulty in defining a
planet-free control sample for comparison make interpreting this type of
multiplicity survey challenging. If the observational sensitivity to close
stellar companions were overestimated, it might cause these studies to
underestimate binary occurrence at small separations, thus producing the
inferred binary suppression.

On the other hand, the solar neighborhood stars from Raghavan et al. (2010) do
not represent an ideal planet-free control sample, since planets are common
around solar-type stars. This may cause any major difference between planet
hosts and a hypothetical planet-free control sample to be diluted when using
the solar neighborhood as a control, possibly leading to an underestimate of
the effect of stellar companions. The precise interplay between these two
competing effects is difficult to determine, and a measurement of the reported
effect using an alternative technique would help to confirm or reject these
conclusions.

One such alternative method for understanding the impacts of stellar
multiplicity on planet formation is to perform a dedicated survey for planets
around a sample of binary stars, and a control sample of single stars.
Dividing the sample by the presence of a stellar companion, rather than by the
presence of planets, allows for a less diluted comparison, since stellar
companions are much easier to find and rule out than planets. Focusing on
nearby stars also allows us to improve our sensitivity to stellar companions
at intermediate separations.

This type of survey can be carried out more easily using the radial velocity
(RV) technique, since RVs are sensitive to planets at a much wider range of
inclinations than transit surveys. However, the RV technique has its own
difficulties with binary systems. Specifically, double-lined spectroscopic
binaries are difficult to model precisely enough to measure the
$\mathrm{m}\,s^{-1}$-level variations caused by planets orbiting one stellar
component. Most binary systems have therefore been rejected from historical RV
planet surveys. Even binaries with very faint companions or easily-resolved
binaries with separations of several arcseconds were discarded early on to
avoid any risk of spectral contamination. Many binary systems that are
actually feasible RV targets therefore lack the decades-long RV time baselines
of their single star counterparts.

In this paper, we describe a uniform imaging and radial velocity survey
designed to detect both stellar and planetary companions orbiting sun-like
stars within 25 pc of the Sun, including both singles and binaries. In §2, we
detail our sample selection and summarize the properties of the stars we have
studied. §3 covers our adaptive optics imaging observations at Lick and
Palomar Observatories. In §4 we describe both the historic and new radial
velocity data we have used for this study. In §5 and §6 we determine the
binary and planet populations within 25 pc. Finally, in §7, we calculate the
occurrence rates of planets in both the single and binary stellar systems in
our sample, and discuss implications for planet formation and evolution.

##  2 The 25 pc Sample

We selected our sample of solar-type stars from the original Hipparcos catalog
(Perryman et al., 1997) in 2013, prior to the release of the first Gaia
astrometric catalog. We first selected all stars with parallax measurements
$\pi_{\mathrm{Hip}}\geq 40$ mas in the Northern hemisphere
($-10^{\circ}<DEC<65^{\circ}$). We required $0.55\leq B-V\leq 1.1$ mag.,
corresponding to a range in spectral type of approximately F9–K4 (Pecaut &
Mamajek, 2013).

Our cutoff at the blue end of this range excluded rapidly-rotating F type
stars. At the fainter red end of our $B-V$ criterion, we aimed to maximize the
number of nearby stars included in the survey while minimizing the number of
new RV survey stars requiring extensive follow-up. We omitted evolved stars
and sub-dwarfs by performing a fit to the main sequence in this color range
and excluding stars with absolute magnitudes more than 2 magnitudes brighter
or 1.5 magnitudes fainter than this fit to the main sequence. Since unresolved
binaries are over-luminous compared to single stars and therefore lie above
the main sequence, the brighter limit was intentionally more generous to avoid
excluding such systems from the sample.

A small number of sample stars were found to be stellar multiples with more
than one solar-type component falling within the selection regime of our
sample, but sharing a single Hipparcos number. When possible, we included both
solar-type components in our survey.

Our final sample contained 294 G- and early K-type dwarf stars. This sample
was generally very bright, with $V$ apparent magnitudes ranging from $V\approx
3$ to $V\approx 9$.

We summarize the stellar properties of our survey sample in Figures 1, 2, and
3. Figure 1 demonstrates our cuts on the Hipparcos sample to select our
targets. We derived stellar properties for the stars in our sample using the
spectral characterization package SpecMatch-syn (Petigura et al., 2017). Using
a high-resolution iodine-free spectrum taken with HIRES at Keck Observatory
for each star, $T_{\mathrm{e}ff}$, $\log{g}$, and [Fe/H] were determined by
comparing against model spectra. Finally, we carried out isochrone fitting
using isoclassify (Huber et al., 2017) to determine mass and radius. Stellar
properties from SpecMatchare detailed in Table LABEL:tab:specmatch.

For 36 double-lined spectroscopic binaries in the sample, the contamination of
the primary star’s spectrum by the secondary set of spectral lines ruled the
target out for our RV survey, and as a result, no template spectrum was
obtained for spectral characterization. For these stars, we estimated the mass
from the Hipparcos $B-V$, although due to the unresolved nature of these
binary targets, their mass determinations are likely biased. Since we did not
include these systems in the planet occurrence statistics, we did not attempt
to determine more precise stellar parameters for this subset of stars.

The median star in our sample was 0.86 $M_{\odot}$ and had a
$T_{\mathrm{e}ff}$ of 5296 K. Most were approximately solar in metallicity,
with a small tail to low metallicity stars. These stellar parameters are
plotted in Figure 2.

The cumulative histogram of the Hipparcos distances to our sample stars is
plotted in Figure 3. Along with this distribution, we calculated the expected
cumulative distribution for a volume-complete sample of uniformly-distributed
stars, which was simply a measure of the fractional volume within each
distance. The distribution for our sample stars followed this expected
distribution closely, demonstrating that the sample was complete out to 25 pc.

Because these represented the nearest, brightest, and best-studied stars in
the northern sky, we did not see much change to the sample with the release of
the Gaia DR2 database. In fact, for stars at the bright end of this sample,
the Hipparcos astrometric constraints were typically more precise than those
from Gaia. We did find that eighteen of our sample stars had Gaia parallax
measurements $\pi\lesssim 40$ mas, pushing them slightly outside of our 25 pc
limit. These 18 stars have updated distances of 25.1–26.7 pc. Similarly, a
query of the Gaia DR2 catalog with identical constraints on parallax and
declination, and cuts in $G$ and $BP-RP$ corresponding to the same spectral
types we surveyed, results in a sample size of 308 stars, sixteen more than
are in our sample. Despite this, we proceeded with our sample as originally
defined.

We later determined that the re-reduction of the Hipparcos catalog by van
Leeuwen (2007) would have been a more appropriate source for selecting our
sample, but by that point it was too late to re-define the sample.
Nevertheless, the minimal sloshing of stars across the 25 pc boundary based on
the different Hipparcos reductions as well as the new Gaia astrometry made
little difference to the sample size, and we do not expect these small
differences to affect the outcomes of our survey.

Figure 1: Color-magnitude diagram showing the selection of our sample from the
Hipparcos catalog. We made vertical cuts at $0.55\leq B-V\leq 1.10$ to select
G and early K stars. We then performed a linear fit to the main sequence
within our color limits, and excluded stars brighter than 2 mag. above and
fainter than 1.5 mag. below this fit. Though the selection was made from the
original Hipparcos catalog (Perryman et al., 1997), we plot the colors and
absolute magnitudes derived from the new reduction (van Leeuwen, 2007). Figure
2: Histograms of the stellar properties of our sample stars, showing the
distributions of $T_{\mathrm{e}ff}$, $\log{g}$, [Fe/H], mass, radius, and
distance of stars followed in our survey. Parameters were derived with
SpecMatch and isoclassify using the iodine-free HIRES template spectrum of
each star in the sample. The median of each distribution is indicated with
black dashed lines, and the median value is printed at the top of the plot.
Stars without high resolution template spectra available are not included in
this plot. Figure 3: Cumulative distribution of the distances of stars in our
sample. The dashed line shows the curve expected assuming a volume-complete
sample of randomly distributed stars. The agreement between these cumulative
histograms demonstrates the volume-complete nature of our sample. Due to
slight differences in the original and re-reduction of the Hipparcos data, a
small fraction of our sample extends marginally beyond our nominal 25 pc limit
when plotting the van Leeuwen (2007) distances. These are represented by the
right-most bin in the cumulative distribution.

##  3 AO Imaging Observations

We undertook a uniform imaging survey of our stellar sample using the ShaneAO
adaptive optics (AO) system at the Lick Observatory 3-meter Shane Telescope
(Gavel et al., 2014). We obtained follow-up data for suspected companions
using ShaneAO as well as PHARO behind the adaptive optics system at the
$200\arcsec$ Hale Telescope at Palomar Observatory (Hayward et al., 2001). The
goal of this survey was to find or rule out new faint or close stellar
companions that might have been missed by previous imaging surveys (e.g.
Raghavan et al., 2010), and to determine stellar properties using photometry
and relative astrometry for any newly identified bound companions.

We were also able to use these data to find and rule out several non-common
proper motion background stars. Our observations were conducted over several
years between 2015 and 2018, so a baseline of 1–3 years between imaging
observations was typical. Because of our 25 pc distance limit, the stars in
our sample also had large proper motions. The median value of proper motion
for our sample stars was $0.33\arcsec/yr$, and 96% of the sample had
$>0.1\arcsec/yr$ proper motion. We were therefore able to distinguish between
bound and background companions by comparing the motion of the companion
relative to the primary star over time. We describe this process in more
detail in §5.

###  3.1 Lick/ShaneAO

We began our ShaneAO survey in September 2015, and were allocated 5–10 nights
per semester through August 2018. Accounting for weather losses, this resulted
in 27 partial or complete nights of observations on this survey. Typical
seeing measurements for our nights ranged from $1\farcs 5$ – $4\arcsec$. We
achieved Strehl ratios of approximately 30–40% on nights with good to average
seeing.

Our initial survey technique was designed to maximize survey sensitivity while
minimizing integration and readout time. We therefore obtained images of our
bright sample stars through the $K_{s}$ filter, allowing the primaries to
saturate in 13 integrations of 1.5 seconds each. We obtained these saturated
images in a 4-point dither pattern, with a preliminary, centered frame
included. Stars at the bright end of the sample, with $V<5$, typically
saturated out to the first Airy ring of the PSF, while stars at the fainter
end $V>8$ often did not saturate at all, depending on the observing
conditions.

The field of view of the ShaneAO detector has a radius of $\approx 15\arcsec$,
but with our dither algorithm, the effective FoV was $\approx 10\arcsec$,
since the outermost regions of the FoV were included in only a fraction of the
exposures taken. The ShARCS camera has a pixel scale of $32.6\pm 0.13$
mas/pixel, and a field rotation of $1.87\pm 0.1^{\circ}$ (G. Duchêne, private
communication).

For each set of saturated imaging observations we obtained an accompanying set
of 5 dithered images of a region of sky offset by $30\arcsec$ from the science
target, using the same exposure time and filter as the target observations. We
combined these images by median through the unaligned stack to serve as a sky
background measurement. We took flat field exposures in each science filter at
evening twilight on each night of observations, and accompanying dark images
were taken at identical exposure times. The flats were dark-subtracted and
combined, then each science and sky frame was divided by the flat field.
Median sky images were then subtracted from the science frames. We ran a bad-
pixel removal routine on each image, based on a map of bad pixels for the
ShARCS detector (Rosalie McGurk, private communication).

We used a rotational symmetry algorithm, adapted from Morzinski et al. (2015),
to register our saturated images. Each image was first roughly aligned by
moving the peak pixel of a median-filtered version of the image to the center
of the frame. The median filter had a size of 7 pixels to reject cosmic rays
or other single-pixel peaks. However, due to the heavy saturation of many of
the images, this algorithm was insufficient to align to better than $\approx
10$ pixels.

We next performed an iterative rotation algorithm to find the true centers of
each saturated star. At each prospective central pixel, the image was rotated
about the pixel by $90^{\circ}$, $180^{\circ}$, and $270^{\circ}$. For each
rotation, we subtracted the original image and summed the absolute value of
the residuals. We then summed over the residuals for each rotation. We
performed this process iteratively for central pixels in a box of size $\pm
10$ pixels about the center of the image. This map of central pixel versus
residuals was then interpolated onto a $0.1$ pixel scale, and the minimum was
found. We treated this location as the star center. In this way, we aligned
and coadded all science frames for each star. We assume a typical precision of
0.5 pixels for this alignment procedure, as well as for the center position of
the saturated star.

Using the PyKLIP package of Wang et al. (2015), we modeled and subtracted the
PSF of each image based on a reference differential imaging library composed
of the science frames of all other stars observed on the same night as the
target frame. We inspected both the PSF-subtracted and the original images by
eye for candidate companions.

When we detected a candidate companion, we used the DAOStarFinder algorithm
implemented in the photutils package (Bradley et al., 2017) to obtain
approximate astrometry. We adopted uncertainties of 0.1 pixel for unsaturated
companions, and an uncertainty of 0.5 pixel for the location of the saturated
primary based on our rotation algorithm. Flux ratio measurements could not be
obtained for the saturated images, so any stars with detected companions or
candidate companions were followed up with either the ShaneAO system in the
narrow-band $Br\gamma$ or $CH_{4}-1.2\mu m$ filters, or with the Palomar/PHARO
AO system in one of the available narrow-band filters. Candidate companions
were followed up if they were measured with SNR $\geq 5$ based on local image
noise statistics.

For unsaturated observations on the ShaneAO system, we calculated requisite
exposure times based on the SNR of the saturated image detection, and the
ratio of bandpass for the $Br\gamma$ vs. $K_{s}$ filters. We performed PSF
fits to determine astrometric offsets and relative photometry for the
companions, using a single star from the same night and imaged in the same
filter as our PSF reference. We injected similar-brightness synthetic
companions into the science images to determine the uncertainties on the
photometry and astrometry. We used the scatter in the offsets of the recovered
photometry and astrometry from the injected values as the characteristic
uncertainty on our measurements.

For the faintest detected companions, we used aperture photometry rather than
PSF fitting to determine the relative brightness. We used the DAOStarFinder
algorithm for astrometry with 0.2 pixel astrometric uncertainty assumed for
the faint companion. We chose an aperture radius of 1 FWHM of the primary PSF
for photometry.

###  3.2 Palomar/PHARO

We obtained follow-up observations on a case-by-case basis as a backup program
for spare time or sub-optimal conditions on PHARO (Hayward et al., 2001)
behind the adaptive optics system on the 200$\arcsec$ Hale telescope at
Palomar Observatory. Observations of our sample stars were carried out on 14
nights from 2016A through 2017B.

Targets were observed in a combination of wide and narrow-band filters
centered at $K$, selected to avoid saturation on the bright primaries. We
typically used either $H2$ ($\lambda=2.248\ \mu\mathrm{m},\
\Delta\lambda=0.02\ \mu\mathrm{m}$) or $Br\gamma$ ($\lambda=2.166\
\mu\mathrm{m},\ \Delta\lambda=0.02\ \mu\mathrm{m}$) combined with $K_{s}$, but
occasionally $H2$ combined with $Br\gamma$ for the brightest primary stars. If
J-band observations were unavailable from ShaneAO, they were also obtained
from PHARO using $J$ and neutral density filters since no narrow-band filters
centered at $J$ were available. The neutral density filter created a ghost
image close to the target star, so we took care to differentiate between this
and the candidate companion, and no new companion discoveries were based on
the Palomar $J$ images.

Images were obtained in sets of 15 using a 5-point dither pattern, with
exposure times optimized to avoid saturation on the bright primary star. When
a narrow-band filter ($H2$ or $Br\gamma$) and $K_{s}$ were combined, typical
integration times were only a few seconds; however, when the primary star was
sufficiently bright to require the combination of two narrow-band filters
($H2$ and $Br\gamma$), integration times were increased to several hundred
seconds per exposure. The field of view of the dithered observations was
$\approx 25\arcsec\times 25\arcsec$.

We estimated the sky background by taking a median through the dithered, un-
aligned science frames. We obtained flat field and corresponding dark
observations at evening twilight on each observing night. Flats were taken
through the wide-band filters $K_{s}$ and $J$ only, but were applied to
narrow-band images at the same central wavelength. Each science frame was
flat-fielded and sky-subtracted, then aligned using the same rotational
algorithm described in §3.1. We made astrometric and photometric measurements
following the same procedure as was used for the unsaturated follow-up ShaneAO
observations.

Astrometric and photometric measurements of sample stars with detected stellar
companions and nearby background stars are cataloged in Table
LABEL:tab:imaging in Appendix LABEL:section:appendixA. In columns 1-2 we list
the HD and HIP names of the target star. Column 3 indicates the component name
of the stellar companion, and is left blank for background or unassociated
stars. Column 4-6 provide the epoch, telescope, and filter of the observation.
The astrometric measurements of separation and position angle (PA) east of
north are provided in columns 7-8, and the relative photometry in the
indicated filter is provided in column 9. The method we used to derive the
photometric measurement is indicated in column 10. A flag (X) indicating that
the companion is an unassociated chance alignment of a background star is
provided in column 11.

##  4 Radial Velocity Observations

We carried out an RV monitoring campaign to search for planets around our
sample stars. We combined historic RV measurements made with the Lick/
Hamilton spectrograph (Fischer et al., 2014) and Keck/HIRES (Marcy et al.,
2008) with new RV observations from HIRES and the APF/Levy spectrometer for
each star.

Our survey goal was to ensure a uniform minimum radial velocity time baseline
of 3 years and a uniform minimum number of 30 observations for each star in
our sample. Among these 30 observations, we required that 8–10 be taken within
a 2 month period at high cadence to constrain short-period planets.

Of our 294 survey stars, 36 were known or found to be double-lined
spectroscopic binaries. These stars were not feasible radial velocity targets,
since RV precision is severely impacted by the presence of a second set of
bright spectral lines. We therefore excluded these stars from the RV survey,
leaving 258 stars in the RV sample. These excluded stars were binaries with
separations of $<1\arcsec$ and mass ratios of $q\gtrsim 0.3$. The exclusion of
these stars effectively decreased our statistical sensitivity to the effects
of very close binaries, since we only obtained RV observations for close
binaries with a faint, low-mass companion. Next generation AO-fed RV
instrumentation will help to alleviate the difficulties posed by close, equal-
mass binaries, but work to improve our techniques for deriving precise radial
velocities from double-lined spectra is also merited.

Of the remaining 258 stars, 128 had historic radial velocity time series
surpassing the survey minimum requirement, due to their inclusion in the
California Planet Survey (e.g. Marcy et al., 2008; Wright et al., 2012; Howard
et al., 2012, 2014; Marcy et al., 2014), which was carried out at Lick and
Keck Observatories starting in the late 1980s and continues today. These stars
were not targeted for additional follow-up under this program, but many
continue to be observed regularly. These are the best-studied members of our
survey sample, and the majority are single stars, since most known binaries
were excluded from the initial CPS target lists. These stars are also among
the least magnetically active of our sample stars, since RV surveys often
select for magnetically quiet stars which display less RV jitter. For this
survey, we did not exclude magnetically active stars, but are less sensitive
to planets around these noisier targets. The detailed star-by-star sensitivity
was determined by injection/recovery, as described in §6.3.

The remainder of the stars in the sample were divided into HIRES and APF sub-
samples, and additional radial velocity observations were obtained to bring
them up to the survey minimum requirement of 30 observations over 3 years
including a high-cadence set.

We achieved this survey goal for 242 of the 258 RV target stars. The new
radial velocity measurements are described below. Of the remaining sample
stars, 13 were observed at least 20 times. We chose to include these stars in
the radial velocity analysis as well. The final three stars had fewer than 20
observations, and were therefore excluded from our analysis. The distributions
of the number and baseline of observations of these stars are displayed in
Figure 4, divided between the single star and binary star samples. We describe
in §5 how we divided our sample based on the presence of companion stars.

For both the single and binary stars in our sample, two main categories can be
observed in the distribution of observing baseline: those that have had many
years of previous RV observations, and those that were only added to the RV
survey at the beginning of this project. A larger fraction of binaries than
singles fell into this latter category, as shown in Fig. 4. Of these newly-
added binaries, most were close, with separations $<100$ AU.

Figure 4: Cumulative distributions of the number of RV observations (left) and
RV time baseline (right) for our sample. The left panel demonstrates that a
smaller fraction of the binary systems have $>100$ observations, as compared
to the single stars. In particular, the median number of observations for the
close (separation $<100$ AU) binaries included in the sample is 53.5, and for
the wide (separation $>100$ AU) binaries, the median is 79.5. For the single
star systems, the median is 90.5 observations. The right panel demonstrates
that a greater fraction of the binary stars have RV data sets spanning $<10$
years, compared to the single star systems. Nearly half of the close binary
systems were never observed prior to the beginning of this survey in 2013, as
compared with 20% of the wide binaries and only 10% of the singles.

###  4.1 Keck/HIRES

We obtained new observations with Keck/HIRES (Vogt et al., 1994) for this
program for 59 sample stars. These stars were chosen from the sub-sample of
stars with incomplete RV data sets based on our minimum survey requirements.
We observed stars using HIRES rather than the APF if they satisfied one of two
conditions: (1) $V>8.5$ making them too time consuming to observe with the
APF, or (2) 5–10 historic HIRES observations existed for that star, creating
complications in fitting zero-point offsets and long-period RV trends if the
star was switched to a new instrument. We followed up the remainder of the
stars with the APF/Levy system.

For all stars observed with HIRES, we followed the typical California Planet
Survey observing methods and reductions to extract radial velocities (e.g.
Marcy & Butler, 1992; Howard et al., 2010). Stellar light was passed through
the iodine cell, a glass cell filled with gaseous molecular iodine heated to
$50^{\circ}$ C, imprinting a dense spectrum of absorption lines onto the
stellar spectrum. These lines were used to calibrate the wavelength solution
and as a reference for the instrumental PSF.

For this survey, we obtained spectra through the C2 and B5 deckers, which have
sizes of $14\arcsec\times 0\farcs 87$ and $3.5\arcsec\times 0\farcs 87$
yielding a spectral resolution of $R\approx 55,000$ for each star. We timed
our exposures to ensure a signal-to-noise ratio of $S/N=140$ per pixel at 550
nm. We avoided known close binaries by rotating the slit on sky. We also
obtained an iodine-free spectrum for each of our target stars to serve as a
template in the forward modeling process, and for stellar classification of
the primary star with SpecMatch.

Each spectrum was divided into approximately 700 chunks, and for each chunk,
the template spectrum and the known spectrum of iodine were used to forward
model the Doppler shift as well as the instrumental PSF. The final radial
velocities were then calculated by weighted average among these chunks, such
that spectral regions with high velocity dispersion carried less weight. The
statistical uncertainty for each RV measurement was also calculated from the
dispersion of the chunks.

###  4.2 APF/Levy

We targeted an additional 70 stars using the Automated Planet Finder (APF) and
Levy Spectrograph (Radovan et al., 2014). The APF is a 2.4-meter dedicated
radial velocity telescope at Lick Observatory. It is fully automated,
including target list creation, observations, and raw reductions.

The APF sample were typically bright stars ($V<8.5$) that had nevertheless
been excluded from previous radial velocity survey samples, so did not have
historic HIRES radial velocity data. Some were younger and more magnetically
active than typical RV targets, and some had known binary companions.

The procedure for obtaining RV measurements of the APF stars was very similar
to that of our HIRES sub-sample, including the iodine calibration method and
the RV reduction pipeline (Fulton et al., 2015).

We observed the APF target stars through the W decker, with a size of
$3\arcsec\times 1\arcsec$ and a spectral resolution of $R\approx 100,000$.
Exposures were timed to achieve $SNR=140$ per pixel at 550 nm. We obtained an
iodine-free template spectrum through the W decker for each target. The
template was again used to forward model the Doppler shift at each epoch, in
narrow spectral chunks. The chunks were combined with a weighted average to
calculate the radial velocity and statistical uncertainty at each epoch.

##  5 Stellar Companions

Using the combination of literature review, the Gaia DR2 catalog (Gaia
Collaboration et al., 2018), our high-resolution imaging observations, and our
radial velocity data sets, we compiled a catalog of the stellar companions to
stars in our sample, along with estimates for their physical separations and
masses. In total, we compiled a list of 207 stellar, white dwarf, and brown
dwarf companions orbiting 145 sun-like stars within 25 pc in Table
LABEL:tab:binaries in Appendix LABEL:section:appendixB. Of these, 95 were re-
detected or newly discovered by our survey. The remainder were either too wide
for the $\approx 12\arcsec$ outer working angle of our imaging observations,
or were known SB2 binaries, which were too close to resolve in our imaging
observations and were excluded from our radial velocity survey due to their
complicated double-lined spectra.

Information from Raghavan et al. (2010), the Washington Double Star catalog
(Mason et al., 2001), and the 9th Catalog of spectroscopic binaries (Pourbaix
et al., 2004) served as the starting point for our literature review on each
target, and information from these sources are incorporated throughout the
table. Soederhjelm1999; Makarov et al. (2008); Shaya & Olling (2011); Malkov
et al. (2012); Tokovinin (2014); Halbwachs et al. (2018) were all also of
great help in compiling and characterizing the list of companions previously
published in the literature, as well as all of the references mentioned in the
table notes.

We additionally queried the Gaia DR2 catalog for any stars within 1 degree of
each target star with parallax $\pi>40$ mas. We then identified companions
with equivalent parallax and common proper motions from this larger subset of
Gaia stars. The vast majority of the companions identified in this way had
been previously discovered and published in the literature, but this search
allowed us to incorporate the Gaia photometry to independently calculate
stellar masses for these companions. A small number of previously published
wide companions were ruled out by this method, as they had very different
astrometric solutions compared to the primary stars. We also found that a
handful of previously published stellar companions did not appear in our
query, typically because the companions did not have parallax measurements
listed in the DR2 catalog.

From our imaging survey, we re-detected 86 previously-published companions and
an additional 9 new companions to stars in our sample. Three of these new
companions (HD 25893 Bb, HD 34673 Bb, and HIP 91605 Bb) were tertiary stars in
a previously-known binary star system, where we have newly resolved the
companion to itself be a double star. One new stellar companion, HD 159062 B,
was a faint white dwarf, whose orbit and photometry were modeled and reported
in Hirsch et al. (2019). Another two new companions, HD 165401 B and HD 190771
B, will be characterized in more detail in future work (Tejada et al. in
prep).

28 visual companions from our ShaneAO survey were determined to be
unassociated background sources, based on their observed motion over multi-
epoch imaging observations, typically spanning 1–2 years. Since the nearby
stars in our sample all had significant proper motions, a 1–2 year time
baseline was sufficient to identify background sources. We compared the
relative motion of each new companion to the expected motion of a background
star based on the proper motion and parallax of the primary. We classified
stars whose motion was in the same direction as expected for a stationary
background star, and whose magnitude of motion was consistent with the
expectation within 50%, as background stars. These classifications were
typically unambiguous, since the newly-detected companions were at separations
of 2–10″(corresponding to tens or hundreds of AU) from the primary star, so
their expected orbital motion over 1–3 years was negligible. Therefore, bound
companions had almost no measured motion over our imaging epochs. These
background sources are listed as such in column 11 of Table LABEL:tab:imaging,
and are not included in Table LABEL:tab:binaries.

The total number of stars with one or more detected binary companion,
including SB2 systems excluded from our RV analysis, was 145. This yielded a
raw multiplicity fraction for our sample stars of $0.486\pm 0.030$, consistent
with the stellar multiplicity fraction reported in Raghavan et al. (2010).
Breaking down these multiple systems by multiplicity, we found that the raw
fractions of binary, triple, and higher-order systems were $0.316\pm 0.027$,
$0.138\pm 0.020$, and $0.032\pm 0.010$ respectively. Again, these values
agreed well with the results of Raghavan et al. (2010).

In Table LABEL:tab:binaries, we list the HD and HIP names of the primary star
(columns 1-2) and the component name of the binary companion (column 3). The
mass of the primary target star and the method we used to derive it are listed
in columns 4–5. This was by default the most massive star in the system
falling within our color and brightness limits. The majority of the sample had
stellar masses derived from spectroscopic analysis combined with isochrone
models using the SpecMatch-syn (Petigura et al., 2017) and isoclassify (Huber
et al., 2017) python packages. These stars have the designation code “Spec”
listed in column 5. For known double-lined spectroscopic binaries, we did not
obtain HIRES template spectra to calculate stellar properties, as these stars
were not targeted with our RV survey. Instead, we calculated somewhat less
precise masses by interpolating the stellar properties table of Pecaut &
Mamajek (2013) using Hipparcos $B-V$ color and assuming a mass uncertainty of
$\pm 1$ spectral type from the table. These stars are coded “BV” in column 5
of the table.

Companion masses (columns 6-8) were mostly sourced from the literature. The
methods used to determine companion masses, and the shorthand codes we used to
describe them in the paper are as follows: Companions included in our RV
survey had spectroscopic mass determinations using SpecMatch-syn, equivalent
to the masses determined for the primary stars. Only secondaries of solar-
type, within the color and brightness limits of our survey, were included as
RV targets. These masses are coded “Spec” in column 7. Dynamical masses and
mass ratios based on orbit fits were the preferred source of companion masses
for those companions without HIRES template spectra. We incorporated primary
star masses to convert from mass ratios. These masses are coded “Dyn”.
Dynamical mass lower limits ($m\sin{i}$) from radial velocities alone are
coded “Dyn-”. Many stars had photometric mass estimates from the literature,
and these are coded ’Phot’. For companions with photometry available from GAIA
DR2, we interpolated the stellar properties table of Pecaut & Mamajek (2013)
to determine their masses, first using the primary star’s mass to determine
absolute Gaia magnitude, then shifting to the companion star’s absolute Gaia
magnitude using the measured $\Delta G$, and finally interpolating to the
companion’s mass. This method worked well down to the bottom of the main
sequence, but required extrapolation for stars with masses $<0.075M_{\odot}$
since these masses were not included in the table. This affected only one
stellar companion assessed using this method, HD 79555 B, whose mass was
determined to be $0.063\pm 0.002M_{\odot}$. Other low-mass companions had
photometric estimates of the mass available from other literature sources. We
used photometric measurements from our own ShaneAO and Palomar data for the
newly-detected companion stars. All of these secondary masses are coded “Phot”
in the table as well. Finally for mass estimates based on spectral type we
used the code “Type”. We note that the photometric and spectral type methods
of estimating companion masses assumed the companions were on the main
sequence, so could result in inaccurate or overestimated masses for any
unknown very young stars in the sample.

Columns 9-11 contain our estimate for the physical separation of the companion
from the target star, the method used to determine the separation, and the
literature reference for this measurement. The methods employed to determine
separation were spectroscopic orbit fits to determine the semi-major axis for
binaries with short periods (coded as “Dyn” in column 10), or measured
projected separation for wider binaries (coded as “Proj”). We note that the
dynamical semi-major axis estimates provided here do depend on the accuracy of
the derived total mass of the system.

We plot the separation and mass ratios of the detected or literature
companions in Figure 5. They span separations from $<1$ to $>10^{4}$ AU, and
mass ratios from 0.02 to $>1$ for the few stars in our sample that are not
themselves the primary star in their system. Companions detected in our
survey, regardless of whether they were previously known, are indicated with
filled markers, while companions that we added in from the literature are
plotted with open markers. Newly discovered companions are indicated with red
stars. Companions whose primary stars were excluded from our RV planet search
are plotted as triangles. Some of these companions are located at separations
wider than 100 AU, as they are tertiary companions in triple systems where the
closer inner companion compelled the exclusion of the primary star. We note
that a few very close and fairly bright binaries were not excluded, typically
due to a legacy of RV data predating this survey, while a few possibly
feasible targets were nevertheless excluded due to a faint or fairly wide
companion. Notes on individual systems can be found in Appendix
LABEL:section:appendix_binary.

Figure 5: Mass ratio versus physical separation for all binary companions in
our sample. Our estimates of mass and separation are detailed in Table
LABEL:tab:binaries. Companions detected or re-detected by our imaging survey
are plotted as filled symbols, while companions incorporated only from the
literature are plotted as open symbols. Companions whose primary star was
included in our RV planet search are plotted as circles, while those whose
primary was excluded from the RV search are plotted as triangles. Most
companions inward of 10 AU excluded their primaries from our RV survey, with
the exception of those companions with mass ratios below approximately 0.3.
The companions plotted as triangles outward of 100 AU all represent tertiary
companions to primaries excluded from our RV survey due to a closer inner
stellar companion. The small number of companions with $q>1$ represent the
systems in which our Sun-like target star was itself the secondary component,
as well as systems in which two or more Sun-like stars were included in the
sample.

###  5.1 Sensitivity to Stellar Companions

The combination of our radial velocity and imaging observations provided
improved sensitivity to stellar companions to our sample stars, down to the
bottom of the main sequence at most separations. This sensitivity was
essential, since our knowledge of stellar companions was used to guide our
division of the complete sample into “single” and “multiple” sub-samples.
Contamination of the single sample by unknown binary systems would bias the
results of our comparison of planet occurrence rates between these samples.

Judging our sensitivity to stellar companions from our saturated adaptive
optics survey was more complicated than simply creating a standard contrast
curve, since the saturated cores of our survey stars made calculating precise
relative photometry more difficult. Instead, we estimated our sensitivity on a
sample-wide level, by determining the outer envelope of the angular
separations and $\Delta K$ measurements of all companions detected in the
saturated survey, then re-detected in unsaturated follow-up images.

In Figure 6, we plot the angular separation and contrast of each $Br\gamma$
detection of a companion discovered in the saturated AO survey at SNR$>5$. For
this plot, we treated $\Delta Br\gamma$ as equivalent to $\Delta K$. For the
faintest companions, consecutive saturated $K_{s}$ and unsaturated $Br\gamma$
observations were carried out. This allowed us to locate the position of the
companion using the deeper saturated $K_{s}$ image, but perform the aperture
photometry on the unsaturated $Br\gamma$ image. Multi-epoch observations of
the same companions were all included, as were companions determined to be
background stars.

Using this catalog of companion detections, we calculated the 95th percentile
of the measured $\Delta K$ values at each position, using a running histogram
box containing 30 surrounding detections. Due to the lower density of
detections at wider separations, the box size was larger at wide separations
than at small separations. From these 95th percentile measurements at each
separation, we fit a 4th order polynomial, which we overplot in Figure 6.

To determine the physical limits on companions, we converted the relative
photometry into companion masses and angular separations into projected
separations for each star’s mass and distance. We assumed sensitivity was
unity above the contrast curve and zero below the curve for each star. We then
summed over the contrast curves for each star to estimate the sample-wide
sensitivity to binary companions in our imaging observations.

For the radial velocity observations, we estimated detectability of stellar
companions as a function of companion mass and semi-major axis by using the
analytic RV sensitivity curve from Howard & Fulton (2016), which was based on
the number, precision, and time baseline of our RV observations. We performed
injection/recovery tests in the planetary regime to more accurately assess our
sensitivity to planetary companions, but for computational efficiency, we did
not inject companions more massive than $\approx 30M_{\mathrm{J}}$. Thus we
relied on the analytic approximation in the stellar companion regime.

Since our RV observations were aimed at detecting giant planets, our
sensitivity to stellar companions (with masses 2 or more orders of magnitude
larger than typical giant planets) was complete out to approximately twice the
observing time baseline. For each star, we determined the number and baseline
of observations, and used the rms of the RV residuals to a best-fitting orbit,
trend, or flat line as an estimate of the typical precision. See §6 for a
description of the process used to determine the best-fit model for each
system. We again assumed sensitivity was unity above the threshold and zero
below it. We converted the RV sensitivity curve from units of orbital period
and RV semi-amplitude to semi-major axis and companion $m\sin{i}$ using the
primary’s stellar mass. Together with the imaging limits, our sample-wide
completeness to stellar companions is plotted in Figure 7.

Figure 6: ShaneAO companion detections are plotted, comparing angular
separations to $K_{s}$ contrast in magnitudes. We calculated our saturated
imaging survey sensitivity by fitting the envelope of these detections using a
4th order polynomial. This fit is plotted as the cyan dashed line. Bound
companions, typically at smaller separations and smaller contrasts, are
indicated with filled circles. Companions determined to be chance alignments
of background stars are indicated with open circles. These background stars
are typically more widely separated and higher-contrast than the bound
companions.

The combination of imaging and radial velocities provided essentially complete
sensitivity to stellar companions with masses $>0.09M_{\odot}$ at all
separations out to the outer working angle of our images, except for a small
wedge of parameter space around 10 AU where completeness was 70–80%. For
companions below 0.09$M_{\odot}$ and at separations $>20$ AU, completeness
dropped significantly. However, because of the shape of the AO contrast curve,
this transition from near completeness to incompleteness was below the bottom
of the main sequence for companions outside of 30 AU.

Based on the log-normal period distribution and flat mass ratio distribution
of Raghavan et al. (2010), we have determined that our sensitivity to stellar
companions is $\approx 98$% complete within 100 AU. In combination with the
$\approx 50\%$ binary rate, we therefore expect that for our 290 star sample,
we may have missed no more than 3 stellar companions due to lack of
sensitivity in this regime.

For the remainder of this study, we divided our sample based on these results
into single star and binary star samples. Among the stars surveyed with radial
velocities (excluding the double-lined spectroscopic binaries), there were a
total of 146 single stars, and 109 stars in binary or multiple systems. Ten of
the lower-mass binary companions to our sample stars fell into our survey
color and luminosity limits and were included as RV targets, and the remainder
of our RV observations were limited to only one of the stars in each multiple
system. The secondary stars included in our RV survey were typically nearly
equal-mass with their primaries, and were therefore included because their
planet formation and dynamical environment should be nearly identical to the
primary star. Many of these companions also had a history of RV observations
predating this survey, so were essentially free to include. Three sample stars
had a more massive stellar companion outside of our survey limits, so were
themselves the secondaries in their respective systems.

Figure 7: This plot demonstrates our survey sensitivity to stellar companions
as a function of companion mass and physical/projected separation, for the
combination of ShaneAO imaging and HIRES or APF radial velocities. The imaging
observations allow nearly complete sensitivity down to the bottom of the main
sequence at projected separations of 20 AU or more. Within $\approx 20$ AU,
our 20 year median RV baseline allows us to fit orbits for all stellar
companions. For stars with shorter RV baselines, the gap between coverage from
RVs and imaging grows larger. The overall color scaling demonstrates nearly
complete sensitivity to companions with masses $\geq 0.09M_{\odot}$, except in
a triangular window around 10 AU where sensitivity is 70–80%. Below
0.09$M_{\odot}$and at separations $>20$ AU, sensitivity drops down to
essentially zero as the companions drop below the imaging contrast curve.
Outside of 30 AU, we are sensitive to companions down to the bottom of the
main sequence (taken as 0.075$M_{\odot}$). We note that this plot does not
include sensitivity to double-lined spectroscopic binary systems, which were
mostly excluded from the sample before this data was collected.

##  6 Planetary Companions

###  6.1 Blind Planet Search

We carried out a blind search for planetary companions to our sample stars
using the RVSearch pipeline, based on the infrastructure of the publicly
available RV analysis package RadVel(Fulton et al., 2018). The complete
algorithm is described in detail in a forthcoming paper, Rosenthal et al. (in
prep), but a short description of our procedure follows.

First, we selected a nominal model for each stellar system, based on its
radial velocities. In the case of an obvious high-amplitude radial velocity
curve due to a stellar companion, the nominal model was a fit to this single
Keplerian orbit, performed in advance of the planet search pipeline.
Otherwise, the nominal model was chosen to be either flat, or the best-fit
linear trend with curvature. These three nominal model options allowed the
flexibility to capture all types of potential influence from a binary
companion: none (flat), acceleration due to a long-period binary
(trend+curvature), or a full Keplerian orbit due to a closer binary companion
(single Keplerian).

Once the nominal model was chosen, we performed a grid search for an
additional planet to our model, with eccentricity fixed to zero. The RV semi-
amplitude and time of conjunction of the additional planet, as well as the RV
jitter and zero-point offsets of each instrument were allowed to float. If a
nominal model with a trend and curvature was chosen, the slope and curvature
were also allowed to vary for these fits.

We constructed a grid of periods ranging from 3 to $10^{4}$ days. We
calculated the optimal grid spacing by requiring that each subsequent period
resulted in a difference of less than one quarter phase over the time baseline
of the data. This resulted in a grid typically containing several thousand
periods, depending on the time span of each sample star’s RV data.

At each point in this grid, we fixed the model planet period to the test
period and performed a maximum a posteriori fit using the likelihood function
(Fulton et al., 2018):

$\log{\mathcal{P}}=\\\
-\sum_{i}{\left[\frac{(v_{i}-v_{m}(t_{i}))^{2}}{2(\sigma_{i}^{2}+\sigma_{\mathrm{jit}}^{2})}+\log{\sqrt{2\pi(\sigma_{i}^{2}+\sigma_{\mathrm{jit}}^{2})}}\right]}$
(1)

where $v_{i}$, $\sigma_{i}$ and $t_{i}$ refer to the offset-subtracted radial
velocity measurements and their uncertainties and time stamps, $v_{m}$ is the
n-planet model velocity, and $\sigma_{\mathrm{jit}}$ is the white noise jitter
term added in quadrature to the measurement uncertainty.We imposed a physical
prior of $K_{n}>0.0$ $\mathrm{m}\,s^{-1}$ on the RV semi-amplitude $K_{n}$ for
each planet we fit.

At each test period, we calculated the BIC value of the best-fit orbit
solution, and compared it to the BIC of the nominal model to obtain
$\Delta\mathrm{BIC}=\mathrm{BIC}_{\mathrm{n-1}}-\mathrm{BIC}_{\mathrm{n}}$. We
then constructed a periodogram by plotting test period vs.
$\Delta\mathrm{BIC}$, and searched for significant peaks.

To determine the requisite significance of a peak in order to consider it a
planet detection, we used the methodology of Howard & Fulton (2016) to
determine an empirical false alarm probability threshold: we selected the
50th–95th percentile of the $\Delta\mathrm{BIC}$ values for each system, and
fit an exponential function to the distribution of $\Delta\mathrm{BIC}$
values. We then extrapolated this fit to a relative occurrence of 0.1% for an
eFAP $=0.1$% threshold. If the peak value of the $\Delta\mathrm{BIC}$
periodogram exceeded this eFAP $=0.1$% threshold, it was considered a
significant planet detection.

If a planet was detected via a significant peak, we added an additional planet
to the model and repeated the same procedure, this time comparing against the
1-planet model. We continued this process, including one more additional
planet on each iteration and comparing to the preferred n-1 planet solution
with the BIC criterion, until the periodogram failed to show any additional
peaks that surpassed the $\Delta\mathrm{BIC}$ threshold. For these subsequent
iterations, only the nth planet’s eccentricity was fixed to zero. We freed the
eccentricities of the n-1 previously-detected significant periodicities to
allow a more accurate fit for these signals. A prior of $e<0.99$ was used for
computational efficiency for these fits. This process is demonstrated in
Figure 8 for the known 2-planet system, HD 114783 (Bryan et al., 2016).

Figure 8: An example of our iterative periodogram search for planets around
our sample stars, for HD 114783, a 2-planet system. Panels a and b show the
final 2-planet fit to the RV time series data and residuals to this fit.
Panels c and d show the first (1-planet) fit and the associated periodogram in
which the first periodicity is identified. Likewise, panels e and f show the
second planet fit and periodogram identifying the second periodicity. Panel g
shows a running periodogram for each planet, demonstrating how the
significance of each grows with increasing number of observations. Finally,
panel h shows the 3-planet periodogram, which exhibits no additional
significant peaks. In each periodogram panel, the 0.1% FAP line, plotted as a
horizontal dotted line, is calculated based on the distribution of periodogram
peaks, and differs for each panel due to the differing periodogram statistics.

We chose to perform this search blind with respect to known planets, in order
to avoid biasing our planet occurrence rate statistics. We therefore did not
input information on known planetary companions to our sample stars, but
instead allowed our algorithm to search for and detect these planets when it
was able to do so. All known planets in the survey were recovered with this
algorithm. For the sake of computational time, this survey was limited to
searching for planets with periods of $\geq 3$ days.

Upon discovery of a potential planet, we performed a detailed MCMC analysis of
the system using RadVel (Fulton et al., 2018) to assess its properties and
significance further. In total, we detected 135 significant periodicities in
our data set. Of these, we classified 53 as false positives, of which 35 were
found to be due to stellar activity or rotation by a corresponding periodicity
in the $S_{\mathrm{H}K}$ time series. This category also included annual
aliases of long-period stellar activity cycles seen in the $S_{\mathrm{H}K}$
time series. Fifteen false positives were found to be yearly harmonics or
aliases of the same. Two false positives in the HD 123 system were caused by
contamination of the spectrum by a pair of stellar companions (see Appendix
LABEL:section:appendix_planets).

The final false positive, detected in the data on HD 217014 (51 Peg), was
caused by a zero-point offset in the Lick data at the epoch of the final
upgrade of the spectrograph in 2001 (Fischer et al., 2014). This jump in the
median RV was interpreted as a long-period, highly-eccentric planet by our
algorithm. We have undertaken a visual inspection of the RV data for other
long-period signals, and have found no other systems with false positives of
this nature. Although we originally suspected HD 95128 d (Gregory & Fischer,
2010) might be a false positive due to this effect, we have determined that a
drastically different pattern of $\gamma$ values for the Lick instrument
configurations were needed to remove the long-period power in this system.
More details on this false positive source can be found in Appendix
LABEL:section:appendix_planets.

Twenty-eight periodicities were removed because they were caused by stellar
companions, rather than planets. Of these single-lined spectroscopic binaries,
two had additional periodicities detected, both of which were classified as
false positives. No planetary signals were detected in these close
spectroscopic binary systems. All of these binaries are included in the list
of binary companions in Table LABEL:tab:binaries, and these sample stars were
included in the occurrence statistics as part of the binary sub-sample. More
details on these systems can also be found in Appendix
LABEL:section:appendix_planets.

The remaining 54 periodicities were classified as planets and candidate
planets, orbiting 31 of our RV sample stars. Of these, 44 were previously
published and were included in the Exoplanet
Archive111https://exoplanetarchive.ipac.caltech.edu/. Ten additional signals
were classified as planet candidates.

All significant periodicity detections are detailed in Table LABEL:tab:planets
in Appendix LABEL:section:appendixC, along with a note indicating whether the
signal is due to a star, published planet, candidate planet, or caused by
stellar activity. Notes on all planet candidates and false positives can be
found in Appendix LABEL:section:appendix_planets.

###  6.2 New Planet Detections

We included ten “planet candidates” in our analysis. These candidates
represented significant periodic signals detected in our blind planet search,
that had not previously been published. None could be ruled out as stellar
activity or rotation based on the $S_{\mathrm{H}K}$ time series data available
from HIRES or the APF. These candidates are denoted “PC” in Table
LABEL:tab:planets.

For each system, we performed an MCMC analysis of the orbit using RadVel and
report the median and 68% confidence intervals for each orbital parameter. We
included our best-fit RadVel orbits in our occurrence rate analysis. More
detailed vetting and characterization of these new planets are forthcoming in
Rosenthal et al. (in prep).

###  6.3 Sensitivity to Planets

To assess our sensitivity to planets of various masses and semi-major axes, we
performed injection/recovery tests using our planet search pipeline. We
injected 3000 synthetic planetary orbits into each star’s individual radial
velocity data set. Injected planet orbits were selected to have periods from 3
to $10^{6}$ days, and RV semi-amplitudes of 0.1 to 1000 $\mathrm{m}\,s^{-1}$.
We chose injected planet eccentricities at random from a beta distribution
according to the observed eccentricity distribution of exoplanets described in
Kipping (2013). For each orbit, the argument of periastron $\omega$ and the
time of conjunction $t_{c}$ were chosen from uniform distributions spanning
$2\pi$ for $\omega$ and the planet’s orbital period for $t_{c}$.

We then used a truncated version of our planet search pipeline to determine
whether an injected planet was recovered. Due to computational constrains,
constructing a full periodogram for each injection was not feasible for this
project. Instead, we performed a least squares minimization over $\omega$,
$t_{c}$, and RV semi-amplitude $K$ at the period grid point closest to the
injected planet’s period. If the measured $\Delta$BIC of this fit exceeded the
0.1% FAP limit defined by the final iteration of the original planet search
periodogram, then we considered the injected planet to be recovered.
Otherwise, we considered the injection to be missed.

To ensure that this truncated procedure produced reasonable completeness
contours, we compared this approach to the creation of a full search
periodogram for one star, HD 114613. We found that the completeness results
were nearly identical from each approach, except for injected planets with
periods at or below the 3 day search lower-limit, which were unrealistically
“recovered” using the truncated methodology, causing an overestimate of our
sensitivity to planets in this regime. However, this should not significantly
affect our occurrence statistics since we do not attempt to calculate planet
occurrence rates at or below 3 days (except in §8.4, where we discuss hot
Jupiter occurrence). Additionally, the computational time for the full
periodogram method was more than 100 times longer than for the truncated
method. We therefore proceeded with the truncated injection-recovery method to
assess our sensitivity to planets.

In the case of stars with previously-detected planets or other Keplerian
signals, we performed our BIC comparison against the best n-planet model, and
fit for the additional planetary parameters as well as the injected planetary
parameters during the recovery phase. This method mimics the behavior of the
search pipeline when searching for multiple Keplerian signals.

We did not include a step in the sensitivity analysis to mimic the false
positive rejection procedure for our planet search. While we do not expect any
false positives among the injected planets, our procedure was not able to
estimate the false negative rate (e.g. real planets that we spuriously
rejected from our planet catalog, possibly due to a chance similarity with a
stellar activity or rotation period).

From our injection/recovery tests, we were able to construct a map of our
sample-wide planet sensitivity as a function of planet mass and semi-major
axis. In Figure 9, we plot the sample-wide injected planet recovery results
for the single and binary sub-samples, with points color-coded based on
whether the injected planet was recovered (blue) or not (red). These maps
could be used to determine the fractional sensitivity to planets by dividing
the number of recovered injections by the total number of injections in a bin
surrounding a given location in planet mass vs. semi-major axis space.

We overplotted the 25, 50, and 75% completeness contours on the
injection/recovery results in Figure 9. We note that our overall completeness
was better for the single stars than the binaries, but this discrepancy was
measured and accounted for using the injection/recovery tests.

Figure 9: Results of the injection/recovery tests for the single and binary
star systems in our sample. Blue injections were recovered and red injections
were missed. A total of 3000 injections were performed for each star in both
the single and binary sub-samples. The black dashed lines show the 25, 50, and
75% completeness contours for each sample.

##  7 Planet Occurrence in Single and Binary Systems

We calculated the planet occurrence rate in bins of semi-major axis and planet
mass for both the single and binary star samples. For all calculations of
occurrence, we included both known planets and our new planet candidates. We
followed the methodology described by Bowler et al. (2015).

For a given range of planetary parameter space, we first counted the number of
detected planets, $n_{pl}$. We next determined the effective number of stars
with sensitivity to planets in that parameter space, $n_{\star,eff}$. This
second value was calculated by multiplying the fraction of recovered
injections within the region of interest by the number of stars in the sample.
Injections were distributed uniformly in log space, and we assumed that the
planet occurrence rate density was also uniform in $\log{m\sin{i}}$ and
$\log{a}$. We calculated both $n_{pl}$ and $n_{\star,eff}$ separately for the
single and binary samples.

We then used binomial statistics to estimate occurrence. Given $n_{pl}$ and
$n_{\star,eff}$, we calculated the posterior probability for occurrence $f$
using the generalized binomial likelihood taken from equation 7 in Bowler et
al. (2015):

$P(k|f,n)=\frac{\Gamma(n+1)}{\Gamma(k+1)\Gamma(n-k+1)}f^{k}(1-f)^{n-k}(n+1)$
(2)

where $n=n_{\star,eff}$ is the number of trials, $k=n_{pl}$ is the number of
successes, and $f$ is the occurrence rate in number of planets per star
(NPPS).

We adopted a Jeffreys prior on occurrence, which for Bernoulli trials takes
the form $p(f)=\left[f(1-f)\right]^{-1/2}$. Our final posterior for occurrence
then took the form

$P(f|n,k)\propto P(k|f,n)*p(f)$ (3)

which we normalized to integrate to unity.

For our single and binary samples, we divided parameter space into sparse
cells, covering the range of 0.1 to 10 AU in planetary semi-major axis and
0.03 to 10 $M_{\mathrm{J}}$ in planet mass and spanning 0.5 dex per cell. This
yielded a total of 20 cells. Due to the relatively small number of planet
detections, we chose not to divide the sample up more finely. These 20 cells
covered the majority of the parameter space in which we were sensitive to
planetary companions, as well as a significant portion of parameter space in
which our sensitivity was low. The lower semi-major axis limit of 0.1 AU was
chosen to be well within the RV search space of $>3$ days, which corresponds
to 0.03–0.05 AU depending on the host star mass. For our binned occurrence
rates, we calculated statistics only in cells with at least 20% fractional
sensitivity at the center of the cell. This excluded the regime of mass
between 0.03 and 0.1 $M_{\mathrm{J}}$at semi-major axis greater than 0.3 AU,
as well as the next mass bin up (0.1-0.3 $M_{\mathrm{J}}$) in the outermost
separation bin ($>3$ AU). This left us with 16 cells over which we were able
to determine planet occurrence, in which the fractional sensitivity was
$>20\%$.

The detected planets and binned occurrence rates are displayed in Figure 10.
Many of the cells contained zero planets, so we display the 95% upper limit in
those cells. For ease of interpretation, we print the number of planets per
100 stars. The values for occurrence ranged from 0 – 8.7 planets per 100 stars
per cell.

The binned occurrence rates were generally consistent between the single and
binary samples. We saw the most significant differences ($>1\sigma$) near the
regions of lowest sensitivity. In particular, in the lower right corner of our
selected parameter space, where $M_{p}<1M_{\mathrm{J}}$ and $a>3$ AU, the
occurrence rate of planets in single star systems was higher than in binaries
at a 94% confidence level. In contrast, in the lower left corner with
$0.1<M_{p}<0.3M_{\mathrm{J}}$ and $a<0.3$ AU, the occurrence rate was higher
in the binary systems than the single systems at the 93% confidence level. We
hypothesize that this might be due to inward planet migration caused by a
binary companion, although this trend does not seem to hold at higher or lower
masses. More detailed analysis and modeling of the planet distribution in
parameter space in each sub-sample may help to confirm or refute this more
qualitative observation.

Figure 10: Binned planet occurrence rates calculated for each cell based on
the number of detected planets and the number of stars with sensitivity in a
particular cell. Uncertainties reported here are calculated based on the
Jeffreys interval, and results represent the average number of planets per
star per cell.

Using the same methodology, we computed the total occurrence rate over a much
larger region of parameter space, for planets with masses between 0.1 and 10
$M_{\mathrm{J}}$ and semi-major axes between 0.1 and 10 AU. Because lower-mass
($<0.1M_{\mathrm{J}}$) planets were detectable only at the smallest
separations, we omitted this region of parameter space from our summation.

This method yielded an estimate for the occurrence rate of planets in our mass
and semi-major axis range of $0.18^{+0.04}_{-0.03}$ planets per star in the
single star systems, and an occurrence of $0.12\pm 0.04$ planets per star in
the binary systems. These rates are documented in Table 1. These values differ
at the 84% confidence level, with a higher occurrence around singles than the
binary sample as a whole for planets with masses between 0.1 and 10
$M_{\mathrm{J}}$ and semi-major axes of 0.1–10 AU.

Finally, we calculated the overall planet occurrence rate for all stars in our
sample in the same region of planet parameter space. For planets with masses
of 0.1–10$M_{\mathrm{J}}$ and semi-major axes of 0.1–10 AU, the overall planet
occurrence rate around sun-like stars within 25 pc was $0.16\pm 0.03$ planets
per star.

###  7.1 Effects of Binary Separation

Up to this point, we have treated the binary and multiple stars as one sub-
sample, comparing them against the single stars to determine the overall
effect of a binary companion on planet formation and evolution. However,
previous theoretical and observational work has shown that the particular
configuration of the binary companion makes a difference to this question. We
therefore explored how breaking up the binary sample based on binary
separation affected our results.

The distribution of binary separation for our sample binary stars spanned the
range of $1-10^{4}$ AU. It did not follow the full binary separation
distribution of Raghavan et al. (2010), since the closer binaries were under-
represented in our RV sample, due to the continued exclusion of double lined
spectroscopic binaries. Figure 11 shows the RV sample binary separation
distribution in the black histogram. Here we plot only the minimum binary
separation, choosing the closest stellar companion in cases of triple or
higher-order multiples.

Figure 11: The distribution of binary separation for all binary systems
included in our RV planet survey, compared with the distribution of binary
separation for those systems found to host planets. Only a single planet
hosting binary has a binary separation $<100$ AU: HD 19994, which has a
hierarchical pair of companions at 50 AU and a $\approx 1M_{\mathrm{J}}$
planet orbiting at 457 days.

Only eight binary star systems in our sample were found to host planets,
including one planet falling outside our 20% completeness limit and three
additional planets not included in our 0.1–10$M_{\mathrm{J}}$, 0.1–10 AU total
occurrence statistics (see white planet detections plotted in Figure 10). The
distribution of the minimum binary separations of all eight of these planet-
hosting stellar multiples are plotted in the magenta histogram in Figure 11.
Only a single planet-hosting binary star, HD 19994 A, had a binary separation
of $<100$ AU. Its binary separation was approximately 50 AU, near the peak of
the overall binary separation distribution of Raghavan et al. (2010). The
remainder of the planet hosting binary systems had binary separations $>100$
AU.

Dividing the binary sample at 100 AU, we repeated the summed planet occurrence
calculation for planets with masses of 0.1–10 $M_{\mathrm{J}}$ and semi-major-
axes of 0.1–10 AU on each sub-sample and found a statistically significant
difference in the planet occurrence rate for close versus wide binaries. We
found an occurrence rate of $0.20^{+0.07}_{-0.06}$ planets per star for binary
hosts with $a_{B}>100$ AU, and $0.04^{+0.04}_{-0.02}$ planets per star for
binaries with companions closer than 100 AU.

For the wide ($a_{B}>100$ AU) binary hosts, the planet occurrence rate was
similar to that of the single stars. For closer binary systems ($a_{B}<100$
AU), the planet occurrence rate differed from the wide binaries at a 94%
confidence level and from the single stars at a confidence level of 99%. We
plot the posterior distributions of the occurrence in the single, binary,
close binary, and wide binary sub-samples in Figure 12.

Figure 12: Normalized posterior distributions for the planet occurrence rates
(number of planets per star) in different sub-samples. Here, we consider
planets with masses of 0.1–10 $M_{\mathrm{J}}$ and semi-major axes spanning
0.1–10 AU. These PDFs are based on the likelihood function from Bowler et al.
(2015) and include a Jeffreys prior on occurrence. In addition to the
posterior distributions, we show the occurrence and 68% confidence intervals
for each sub-sample to demonstrate the overlap between planet occurrence
estimates in each sub-sample.

To test the sensitivity of this calculation on the threshold we used to divide
the sample into “close” and “wide” binaries, we calculated the planet
occurrence rate in binary systems closer and wider than a given separation for
various values of $a_{B}$ from 50–200 AU, using the same method. We found that
these occurrence rates were only weakly dependent upon the separation chosen
to divide the sample, varying by less than $1\sigma$ as the threshold was
varied from 50 to 200 AU. Figure 13 demonstrates the slightly increasing “wide
binary” planet occurrence rate and slightly declining “close binary”
occurrence rate as the binary separation threshold was increased. For
separation thresholds $>50$ AU, no planets moved between the “close” and
“wide” binary samples, so the changes only reflect slight variations in
$n_{\star,eff}$ for each slightly modified sample. The number of planets,
stars, and the occurrence rates are provided in Table 1.

$a_{p}$ (AU) |  $m\sin{i}$ ($M_{\mathrm{J}}$) | Sub-Sample | $N_{pl}$ | $N_{\star,eff}$ |  $f_{occ}$ (NPPS)
---|---|---|---|---|---
Overall Planet Occurrence
0.1–10 | 0.1–10 | Singles | 18 | 101.8 | ${0.18}^{+0.04}_{-0.03}$
0.1–10 | 0.1–10 | All Binaries | 8 | 66.6 | ${0.12}^{+0.04}_{-0.04}$
0.1–10 | 0.1–10 | Wide Binaries ($a_{B}>100AU$) | 7 | 34.9 | ${0.20}^{+0.07}_{-0.06}$
0.1–10 | 0.1–10 | Close Binaries ($a_{B}<100AU$) | 1 | 31.7 | ${0.04}^{+0.04}_{-0.02}$
0.1–10 | 0.1–10 | All Stars | 26 | 168.4 | ${0.16}^{+0.03}_{-0.03}$
Planet Occurrence vs. Binary Separation
0.1–10 | 0.1–10 | Wide Binaries ($a_{B}>50AU$) | 8 | 44.1 | ${0.18}^{+0.06}_{-0.05}$
0.1–10 | 0.1–10 | Close Binaries ($a_{B}<50AU$) | 0 | 22.5 | ${0.01}^{+0.03}_{-0.01}$
0.1–10 | 0.1–10 | Wide Binaries ($a_{B}>75AU$) | 7 | 37.1 | ${0.19}^{+0.07}_{-0.06}$
0.1–10 | 0.1–10 | Close Binaries ($a_{B}<75AU$) | 1 | 29.5 | ${0.04}^{+0.04}_{-0.03}$
0.1–10 | 0.1–10 | Wide Binaries ($a_{B}>100AU$) | 7 | 34.9 | ${0.20}^{+0.07}_{-0.06}$
0.1–10 | 0.1–10 | Close Binaries ($a_{B}<100AU$) | 1 | 31.7 | ${0.04}^{+0.04}_{-0.02}$
0.1–10 | 0.1–10 | Wide Binaries ($a_{B}>125AU$) | 7 | 32.9 | ${0.22}^{+0.08}_{-0.06}$
0.1–10 | 0.1–10 | Close Binaries ($a_{B}<125AU$) | 1 | 33.7 | ${0.04}^{+0.04}_{-0.02}$
0.1–10 | 0.1–10 | Wide Binaries ($a_{B}>150AU$) | 7 | 30.5 | ${0.23}^{+0.08}_{-0.07}$
0.1–10 | 0.1–10 | Close Binaries ($a_{B}<150AU$) | 1 | 36.1 | ${0.03}^{+0.04}_{-0.02}$
0.1–10 | 0.1–10 | Wide Binaries ($a_{B}>175AU$) | 7 | 29.6 | ${0.24}^{+0.08}_{-0.07}$
0.1–10 | 0.1–10 | Close Binaries ($a_{B}<175AU$) | 1 | 37.0 | ${0.03}^{+0.04}_{-0.02}$
0.1–10 | 0.1–10 | Wide Binaries ($a_{B}>200AU$) | 7 | 28.9 | ${0.25}^{+0.08}_{-0.07}$
0.1–10 | 0.1–10 | Close Binaries ($a_{B}<200AU$) | 1 | 37.6 | ${0.03}^{+0.04}_{-0.02}$
Planet Occurrence vs. Planet SMA
0.1–1 | 0.1–10 | Singles | 3 | 121.4 | ${0.03}^{+0.02}_{-0.01}$
1–10 | 0.1–10 | Singles | 15 | 82.2 | ${0.18}^{+0.04}_{-0.04}$
0.1–1 | 0.1–10 | All Binaries | 4 | 82.9 | ${0.05}^{+0.03}_{-0.02}$
1–10 | 0.1–10 | All Binaries | 4 | 50.2 | ${0.08}^{+0.04}_{-0.03}$
0.1–1 | 0.1–10 | Wide Binaries ($a_{B}>100AU$) | 4 | 41.7 | ${0.10}^{+0.05}_{-0.04}$
1–10 | 0.1–10 | Wide Binaries ($a_{B}>100AU$) | 3 | 28.1 | ${0.11}^{+0.07}_{-0.05}$
0.1–1 | 0.1–10 | Close Binaries ($a_{B}<100AU$) | 0 | 41.2 | $<0.05$
1–10 | 0.1–10 | Close Binaries ($a_{B}<100AU$) | 1 | 22.1 | ${0.05}^{+0.06}_{-0.03}$
Hot Jupiter Occurrence
P (days) |  $m\sin{i}$ ($M_{\mathrm{J}}$) | Sub-Sample | $N_{pl}$ | $N_{\star,eff}$ |  $f_{occ}$ (NPPS)
3–10 | 0.1–10 | Singles | 2 | 134.4 | ${0.02}^{+0.01}_{-0.01}$
3–10 | 0.1–10 | Binaries | 0 | 94.5 | $<0.02$
Table 1: Occurrence rates calculated for different ranges of planetary
parameter space and for different subsets of the full 25 pc sample. Each row
represents a planet occurrence calculation for planets between the given lower
and upper values of planetary semi-major axis $a_{p}$ and mass $m\sin{i}$.
Figure 13: Planet occurrence rate in “wide” and “close” binaries as a function
of the binary separation threshold used to divide the binary sample. Planet
occurrence is only weakly dependent on the binary separation threshold between
50–200 AU, with an overall variation of $\lesssim 1\sigma$ as the separation
threshold is varied for both the wide and close binary sub-samples.

##  8 Discussion

###  8.1 System-Wide Planet Occurrence in Wide Binary Systems

Based on our observations and the occurrence rates we have determined, we have
found evidence that planet formation proceeds in the same way around each
member of a wide binary ($a_{B}>100$ AU) as it does around a single star.
Thus, interactions between binary companions and their respective disks appear
not to be significant to giant planet formation interior to 10 AU for binaries
at separations of 100 AU or more. In other words, planet occurrence around
each star in a wide binary system appears to be independent of the effect of
the other stellar components.

For this study, we primarily targeted only a single star in each binary
system, with the exception of a handful of binary systems with multiple sun-
like components. Thus our occurrence rates as reported here pertained to the
number of planets per star, not per system. For binary or multiple stellar
systems, multiple host stars should then increase the system-wide planet
occurrence rate. We expect that the occurrence rate of planets in each binary
system should be greater than the 20% measured here, given the addition of a
second (or third, etc.) host star.

We note that our sample consisted of only sun-like stars, within a specified
range of colors and masses. Since the mass ratio distribution of binary
systems is fairly flat, many binaries including a sun-like star also include a
significantly lower-mass companion star. In single systems, low-mass stars
have intrinsically lower giant planet occurrence rates, so the total
occurrence in binary systems is not expected to be a simple factor of two
higher.

Previous studies of giant planet occurrence rates in single star systems have
shown an approximately linear dependence of planet occurrence on stellar host
mass (Johnson et al., 2010). For the lower-mass companion stars, we therefore
extrapolated our wide binary occurrence rate to lower host star masses as
$f=0.20\frac{M_{\star}}{0.87M_{\odot}}$, using the median stellar mass of
$0.87M_{\odot}$ for our sample as the denominator to scale the calculated
occurrence rate.

For the binaries in our sample with $a_{B}>100$ AU, we estimated the system-
wide planet occurrence rate by summing over the number of planets per star in
each system (again in the range of 0.1–10 $M_{\mathrm{J}}$ in mass and 0.1–10
AU in semi-major axis), and dividing by the number of systems. We excluded
hierarchical triples from this analysis, since in these systems, although the
solar-type primary was considered a “wide” binary with its companions more
than 100 AU away, the companions were typically within 100 AU of one another
in their hierarchical configuration. This limited our estimate to a total of
29 true wide binary systems, and yielded a system-wide occurrence rate of
$\approx 0.3$ planets per wide binary system.

This estimate is very rough, and more observations will be needed to probe the
effect of host star mass on the conclusions in this work. Dedicated RV
observations of all components in a sample of multiple stellar systems would
provide insight on the relative likelihood of each component to host planets,
based on the mass ratios between the stars as well as their orbits.

In addition, radial velocity observations of a larger sample of binary systems
with binary separations in the range of 10–100 AU would help to resolve the
transition in planet occurrence rate as a function of binary separation.
Because of the number and distribution of the close and wide binaries in our
survey sample, it was not possible to determine the precise binary separation
at which binary companions begin to have an inhibiting effect on planet
occurrence.

However, our results are in good qualitative agreement with previous
multiplicity studies of the Kepler field planet host stars. In particular,
Kraus et al. (2016) identified a binary separation of
$a_{crit}=47^{+59}_{-23}$ AU as the critical separation, within which binary
companions significantly inhibit planet formation. In this study, we found a
corresponding fall-off in the giant planet occurrence rate for binaries with
separations $\lesssim 100$ AU, and we found no planets (and an occurrence rate
of only $0.01^{+0.03}_{-0.01}$ planets per star) around components of binaries
with separations $<50$ AU.

Although we were able to estimate the system-wide planet occurrence rate for
wide binary systems, we did not attempt to do so for all binary systems. The
total system-wide planet occurrence rate for all binaries will depend on the
details of planet suppression as a function of binary separation and mass
ratio, as well as the overall binary separation and mass ratio distributions
(Raghavan et al., 2010).

In the future, the Gaia catalog will allow for the creation of a larger
volume-complete sample of stars for similar observations and analysis, and
30-meter class telescopes will provide access to fainter stars, allowing us to
extend this survey to lower-mass stars and more distant targets. However, this
type of survey will take many years, since very few low-mass stars or stars
beyond 25 pc have the long history of RV observations that many stars in our
25 pc Sun-like sample had.

###  8.2 Planet Properties in Single and Binary Systems

A comparison between the binned planet occurrence rates in single vs. binary
systems in Figure 10 shows a noticeable discrepancy in the locations of the
detected planets in the mass vs. semi-major axis plane. In particular, the
large majority of planets detected in single star systems were located between
1 and 10 AU from their host stars. Contemporaneous studies of giant planet
occurrence have found a peak in the separation distribution of RV-detected
planets at approximately 3 AU, consistent with this behavior (Cumming et al.,
2008; Fernandes et al., 2019, Fulton et al. in prep).

However, the distribution of planets in the binary systems in this survey did
not appear to follow this trend. Instead, more than half of the planets in
binary systems were located within 1 AU of their host stars.

We calculated the total occurrence rates interior and exterior to 1 AU (but
still between 0.1 and 10 $M_{\mathrm{J}}$ in mass) in both the single star
sample and the wide binary sample. We found that for the single stars, the
occurrence rate was higher in the 1–10 AU bin than in the 0.1–1 AU bin with
$>99$% confidence. In the wide binaries, by contrast, we found that the
occurrence rate was approximately equivalent in the 0.1–1 AU and the 1–10 AU
bins.

Table 1 provides occurrence rates interior and exterior to 1 AU for each sub-
sample. Only the single star sample has such a pronounced difference in
occurrence for planets interior and exterior to 1 AU. Although small number
statistics may be at work here with only 12 total planet detections around
binary components, this qualitative trend may also be indicative of planet
migration from wide to small separations, mediated by interaction with a
(wide) binary companion.

A more detailed quantitative comparison of the orbital period distribution of
planets in single vs. binary systems was beyond the scope of this paper, but
will be forthcoming in a follow-up paper using the same sample and sensitivity
analysis.

###  8.3 Double-lined Spectroscopic Binaries

In our stellar sample, 36 close binary systems were excluded from our RV
planet search due to the risk of stellar spectral contamination by the
companion star. These systems effectively factored into our planet occurrence
statistics as having zero planet detections and zero sensitivity to planets
(since we did not collect any RV data on these stars). All 36 of these systems
had binary separations smaller than 50 AU, and so would have been included in
our “close binary” sample if observed.

To probe the potential effect of excluding these systems, we explored how our
planet occurrence rates for close binary systems would change in the scenario
that these stars had been surveyed with equivalent sensitivity to the rest of
the close binary sample, and no planets had been detected. We therefore used
the injection-recovery results from the existing close binary sample, but
applied them to a total number of stars increased by 36 to obtain an updated
$N_{\star,eff}=51.3$. The number of planets, $N_{pl}=1$, was unchanged. This
yielded a planet occurrence rate of ${0.02}^{+0.03}_{-0.01}$ planets per close
binary star. Compared to the number we calculated without these SB2 systems
(${0.04}^{+0.04}_{-0.02}$ planets per star), this value is smaller by $\approx
1\sigma$.

This occurrence calculation assumed that the double-lined spectroscopic
binaries we excluded could have been surveyed with the same sensitivity as the
close binaries that were included in our RV survey sample, which is not
currently possible. To address this gap in our understanding, we are currently
working to understand and mitigate the effects of double-lined spectroscopic
binaries on our radial velocity pipeline, in the hopes of performing a
sensitive planet survey on these systems in the future. RV instruments that do
not use the iodine cell method for wavelength calibration may also have a
better chance of fitting for spectral contamination and obtaining more
accurate RV data for these SB2 systems.

###  8.4 Hot Jupiter Occurrence Rates

For the analysis described above, we chose to limit our occurrence
calculations to $>0.1$ AU to ensure we remained well within the parameter
space explored by our injection-recovery tests. However, previous work has
indicated a difference in the stellar multiplicity of Hot Jupiter host stars
(Wang et al., 2015; Ngo et al., 2015, 2016), so here we attempt to estimate
planet occurrence rates at planet periods of 3–10 days in our single and
binary star samples for comparison.

In our sample, there were seven detected planets with periods between 3–10
days. All orbited single star hosts. Of these, only two (HD 217014 b and HD
217107 b) fell within the 0.1–10 $M_{\mathrm{J}}$ giant planet regime, while
the remainder had $m\sin{i}\approx 0.01-0.02M_{\mathrm{J}}$. There were also
two previously-known hot Jupiters with periods shorter than 3 days in our
sample (HD 189733 b and 55 Cnc e), both of which were in wide binary systems.
We were unable to probe to these shorter periods without significantly
increasing the computational time required for this calculation. However, we
did not see evidence for a significant population of shorter-period planets
based on the RV scatter of our sample stars.

We caution that our abbreviated injection-recovery approach, described in
§6.3, was found to be unreliable very close to the 3 day planet search cutoff,
so our sensitivity may have been overestimated in this regime. Nevertheless,
we used our injection-recovery results to calculate the hot Jupiter occurrence
rates in our single and binary systems. These occurrence rates should be
treated with some skepticism until more rigorous injection-recovery tests are
run at short periods.

We found that for giant planets ($0.1<m\sin{i}<10M_{\mathrm{J}}$) with orbits
of 3–10 days, the occurrence rate was ${0.02}^{+0.01}_{-0.01}$ planets per
single star and $<0.02$ planets per star in a binary system. These rates are
statistically indistinguishable, mainly due to the small number of detected
planets in this regime. These occurrence rates are also broadly consistent
with previous hot Jupiter statistics from the literature. Due to the low
occurrence of hot Jupiter planets, a larger sample would help to probe the
possible differences between the single and binary star samples.

##  9 Conclusions

We have undertaken a joint radial velocity and imaging survey of the nearest
sun-like stars, to assess the impacts of stellar multiplicity on planet
occurrence rates. We have compiled an extensive list of stellar binary
systems, based on our imaging and RV observations as well as previous
multiplicity surveys published in the literature over the past decade. We have
determined that $48.4\pm 2.9$% of our nearby stars have binary companions, a
figure in good agreement with the results of Raghavan et al. (2010).

Of our 290 sample stars, $\approx 260$ have been followed up with radial
velocity observations to attempt to detect planetary companions. Of these, 255
had sufficient follow-up data to meet our survey requirements, 146 of which
were single stars and 109 of which had binary companions. We have assessed our
sensitivity to planets around each of these 255 stars, and reported on the
planet and planet candidate detections resulting from our data. In total, we
have detected 44 planets and 10 candidates, and we have reported their best-
fit orbits and masses.

Based on the planets detected and our sensitivity assessment, we have
calculated total planet occurrence rates for planets with masses in the range
of 0.1–10 $M_{\mathrm{J}}$ and semi-major axes in the range of 0.1–10 AU, for
our single and binary samples respectively. We have found that the single star
systems had a planet occurrence rate of $0.18^{+0.04}_{-0.03}$ planets per
star at these masses and separations, while the binary stars had a planet
occurrence rate of $0.12\pm 0.04$ planets per star.

Dividing the binary sample by binary separation at 100 AU, we have determined
that the occurrence rate for wide ($a_{B}>100$AU) binaries was
$0.20^{+0.07}_{-0.06}$ planets per star, and for close ($a_{B}<100$ AU)
binaries was $0.04^{+0.04}_{-0.02}$ planets per star. We therefore concluded
that wide binaries had an equivalent giant planet occurrence rate to single
stars, while close binaries had a suppressed planet occurrence rate relative
to the single stars, detected at the 99% confidence level.

We have also calculated binned planet occurrence rates over the range of
parameter space to which we had more than 20% fractional sensitivity. We have
found evidence for a difference in the semi-major axis distribution of planets
in single versus binary star systems.

Finally, we have estimated that for wide binary systems with a typical
distribution of companion masses, the total system-wide giant planet
occurrence rate should be approximately 0.3 planets per system.

The authors thank the anonymous referee for their extremely useful suggestions
and comments that helped to improve this manuscript. LAH thanks James Graham
for useful discussions and advice on this survey and statistical analysis. The
authors thank Ken and Gloria Levy, who supported the construction of the Levy
Spectrometer on the Automated Planet Finder, which was used heavily for this
research. We thank the University of California and Google for supporting Lick
Observatory, and the UCO staff as well as UCO director Claire Max for their
dedicated work scheduling and operating the telescopes of Lick Observatory.
LAH acknowledges funding support from the National Science Foundation and NASA
during the completion of much of this project. The authors wish to recognize
and acknowledge the very significant cultural role and reverence that the
summit of Mauna Kea has always had within the indigenous Hawaiian community.
We are most fortunate to have the opportunity to conduct observations from
this mountain.

##  Appendix A Stellar Properties from SpecMatch

Table 2: Stellar properties from SpecMatchfor all stars in sample with a HIRES template spectrum. HD | HIP |  $T_{\mathrm{e}ff}$($\pm 100$ K) |  $\log{g}$($\pm 0.1$ dex) | [Fe/H]($\pm 0.06$ dex) | Mass ($M_{\odot}$) | Radius ($R_{\odot}$)
---|---|---|---|---|---|---
224930 | 171 | 5390 | 4.6 | -0.67 | $0.71\pm 0.03$ | $0.69\pm 0.03$
123 | 518 | 5700 | 4.5 | 0.13 | $1.07\pm 0.05$ | $1.33\pm 0.03$
166 | 544 | 5580 | 4.6 | 0.19 | $0.98\pm 0.03$ | $0.91\pm 0.02$
1461 | 1499 | 5720 | 4.2 | 0.21 | $1.03\pm 0.05$ | $1.15\pm 0.03$
1562 | 1598 | 5720 | 4.5 | -0.22 | $0.88\pm 0.04$ | $0.94\pm 0.02$
3651 | 3093 | 5240 | 4.4 | 0.19 | $0.89\pm 0.04$ | $0.87\pm 0.02$
3765 | 3206 | 5040 | 4.5 | 0.19 | $0.85\pm 0.03$ | $0.83\pm 0.02$
4256 | 3535 | 4950 | 4.4 | 0.28 | $0.85\pm 0.03$ | $0.81\pm 0.01$
4628 | 3765 | 4960 | 4.7 | -0.23 | $0.74\pm 0.03$ | $0.71\pm 0.01$
4614 | 3821 | 5880 | 4.4 | -0.23 | $0.91\pm 0.04$ | $1.09\pm 0.02$
4915 | 3979 | 5640 | 4.6 | -0.14 | $0.90\pm 0.05$ | $0.90\pm 0.02$
6101 | 4849 | 4770 | 4.6 | -0.07 | $0.79\pm 0.02$ | $0.80\pm 0.02$
6582 | 5336 | 5280 | 4.7 | -0.71 | $0.71\pm 0.02$ | $0.72\pm 0.01$
7590 | 5944 | 5960 | 4.5 | -0.00 | $1.04\pm 0.04$ | $1.01\pm 0.06$
10008 | 7576 | 5390 | 4.7 | 0.07 | $0.90\pm 0.03$ | $0.83\pm 0.01$
10086 | 7734 | 5720 | 4.5 | 0.15 | $1.02\pm 0.05$ | $1.00\pm 0.07$
10307 | 7918 | 5860 | 4.3 | 0.07 | $1.03\pm 0.05$ | $1.17\pm 0.03$
10476 | 7981 | 5200 | 4.5 | 0.02 | $0.84\pm 0.03$ | $0.84\pm 0.02$
10853 | 8275 | 4720 | 4.7 | -0.08 | $0.72\pm 0.02$ | $0.69\pm 0.01$
10780 | 8362 | 5400 | 4.6 | 0.10 | $0.90\pm 0.03$ | $0.84\pm 0.01$
12051 | 9269 | 5470 | 4.3 | 0.25 | $0.97\pm 0.05$ | $1.02\pm 0.02$
12846 | 9829 | 5660 | 4.4 | -0.19 | $0.87\pm 0.04$ | $0.97\pm 0.02$
13789 | 10416 | 4780 | 4.6 | 0.11 | $0.78\pm 0.03$ | $0.74\pm 0.01$
14214 | 10723 | 5990 | 4.0 | 0.16 | $1.17\pm 0.05$ | $1.47\pm 0.04$
16160 | 12114 | 4740 | 4.4 | -0.09 | $0.74\pm 0.02$ | $0.73\pm 0.01$
16909 | 12709 | 4720 | 4.6 | 0.03 | $0.77\pm 0.03$ | $0.74\pm 0.01$
17382 | 13081 | 5350 | 4.6 | 0.14 | $0.91\pm 0.03$ | $0.85\pm 0.01$
18143 | 13642 | 5170 | 4.4 | 0.30 | $0.83\pm 0.02$ | $0.76\pm 0.01$
18632 | 13976 | 5140 | 4.6 | 0.28 | $0.89\pm 0.03$ | $0.82\pm 0.01$
18803 | 14150 | 5630 | 4.4 | 0.15 | $0.98\pm 0.05$ | $1.02\pm 0.02$
18757 | 14286 | 5610 | 4.3 | -0.23 | $0.86\pm 0.02$ | $1.08\pm 0.02$
19373 | 14632 | 5940 | 4.1 | 0.14 | $1.14\pm 0.05$ | $1.43\pm 0.03$
19994 | 14954 | 6140 | 4.1 | 0.24 | $1.39\pm 0.03$ | $1.79\pm 0.04$
20165 | 15099 | 5140 | 4.6 | 0.04 | $0.84\pm 0.03$ | $0.81\pm 0.02$
20619 | 15442 | 5700 | 4.6 | -0.16 | $0.91\pm 0.04$ | $0.90\pm 0.02$
20630 | 15457 | 5780 | 4.6 | 0.14 | $1.00\pm 0.03$ | $0.93\pm 0.02$
232781 | 15673 | 4640 | 4.7 | -0.41 | $0.66\pm 0.02$ | $0.63\pm 0.01$
22049 | 16537 | 5120 | 4.7 | 0.01 | $0.81\pm 0.02$ | $0.76\pm 0.01$
22484 | 16852 | 5960 | 4.1 | -0.01 | $1.13\pm 0.07$ | $1.66\pm 0.04$
22879 | 17147 | 5750 | 4.5 | -0.77 | $0.79\pm 0.01$ | $1.09\pm 0.02$
23439 | 17666 | 5000 | 4.7 | -0.88 | $0.67\pm 0.01$ | $0.67\pm 0.01$
24496 | 18267 | 5540 | 4.5 | 0.04 | $0.93\pm 0.05$ | $0.97\pm 0.02$
24238 | 18324 | 4850 | 4.4 | -0.44 | $0.72\pm 0.01$ | $0.74\pm 0.01$
24409 | 18413 | 5680 | 4.4 | -0.01 | $0.94\pm 0.04$ | $1.15\pm 0.03$
25329 | 18915 | 4650 | 5.2 | -1.57 | $0.51\pm 0.02$ | $0.46\pm 0.02$
25680 | 19076 | 5890 | 4.5 | 0.12 | $1.07\pm 0.04$ | $1.03\pm 0.02$
25893 | 19255 | 5440 | 4.6 | 0.25 | $0.97\pm 0.04$ | $0.92\pm 0.05$
26965 | 19849 | 5030 | 4.4 | -0.25 | $0.77\pm 0.02$ | $0.80\pm 0.01$
26913 | 19855 | 5670 | 4.6 | 0.05 | $0.98\pm 0.03$ | $0.92\pm 0.02$
26923 | 19859 | 5960 | 4.4 | -0.00 | $1.04\pm 0.05$ | $1.05\pm 0.02$
29697 | 21818 | 4660 | 4.6 | -0.07 | $0.69\pm 0.02$ | $0.67\pm 0.01$
29883 | 21988 | 4950 | 4.6 | -0.11 | $0.78\pm 0.03$ | $0.77\pm 0.01$
32147 | 23311 | 4800 | 4.4 | 0.28 | $0.82\pm 0.03$ | $0.79\pm 0.01$
32850 | 23786 | 5300 | 4.7 | -0.07 | $0.85\pm 0.03$ | $0.90\pm 0.02$
32923 | 23835 | 5660 | 4.0 | -0.13 | $1.00\pm 0.03$ | $1.70\pm 0.04$
34411 | 24813 | 5830 | 4.1 | 0.10 | $1.04\pm 0.05$ | $1.28\pm 0.03$
34673 | 24819 | 4730 | 4.6 | -0.12 | $0.76\pm 0.02$ | $0.75\pm 0.01$
35112 | 25119 | 4950 | 4.6 | -0.05 | $0.81\pm 0.02$ | $0.84\pm 0.01$
37008 | 26505 | 4940 | 4.5 | -0.37 | $0.73\pm 0.02$ | $0.75\pm 0.01$
37394 | 26779 | 5360 | 4.6 | 0.21 | $0.92\pm 0.03$ | $0.85\pm 0.01$
38230 | 27207 | 5140 | 4.3 | -0.01 | $0.83\pm 0.02$ | $0.87\pm 0.02$
38858 | 27435 | 5710 | 4.5 | -0.16 | $0.90\pm 0.05$ | $0.93\pm 0.02$
39587 | 27913 | 5980 | 4.6 | 0.03 | $1.05\pm 0.03$ | $1.00\pm 0.02$
40397 | 28267 | 5480 | 4.3 | -0.05 | $0.88\pm 0.02$ | $1.05\pm 0.02$
41593 | 28954 | 5370 | 4.7 | 0.13 | $0.91\pm 0.03$ | $0.84\pm 0.01$
42618 | 29432 | 5710 | 4.4 | -0.07 | $0.92\pm 0.05$ | $0.99\pm 0.02$
42807 | 29525 | 5730 | 4.6 | 0.03 | $0.98\pm 0.04$ | $0.93\pm 0.02$
263175 | 32423 | 4780 | 4.7 | -0.24 | $0.71\pm 0.02$ | $0.69\pm 0.01$
48682 | 32480 | 6070 | 4.3 | 0.12 | $1.12\pm 0.05$ | $1.13\pm 0.02$
50281 | 32984 | 4760 | 4.6 | 0.03 | $0.74\pm 0.02$ | $0.70\pm 0.01$
50692 | 33277 | 5810 | 4.3 | -0.16 | $0.91\pm 0.04$ | $1.12\pm 0.03$
51419 | 33537 | 5660 | 4.5 | -0.31 | $0.84\pm 0.03$ | $1.00\pm 0.02$
51866 | 33852 | 4900 | 4.5 | 0.11 | $0.81\pm 0.03$ | $0.78\pm 0.01$
52919 | 33955 | 4670 | 4.5 | 0.01 | $0.74\pm 0.02$ | $0.71\pm 0.01$
52711 | 34017 | 5810 | 4.3 | -0.10 | $0.93\pm 0.04$ | $1.09\pm 0.03$
53927 | 34414 | 4910 | 4.7 | -0.27 | $0.73\pm 0.03$ | $0.71\pm 0.01$
55575 | 35136 | 5800 | 4.3 | -0.31 | $0.87\pm 0.03$ | $1.17\pm 0.03$
$\cdots$ | 36357 | 5000 | 4.7 | -0.03 | $0.78\pm 0.03$ | $0.74\pm 0.01$
59747 | 36704 | 5160 | 4.7 | 0.09 | $0.85\pm 0.03$ | $0.79\pm 0.01$
60491 | 36827 | 5110 | 4.8 | -0.11 | $0.79\pm 0.03$ | $0.74\pm 0.01$
61606 | 37349 | 4980 | 4.7 | 0.07 | $0.81\pm 0.03$ | $0.76\pm 0.01$
63433 | 38228 | 5700 | 4.6 | 0.08 | $0.99\pm 0.03$ | $0.93\pm 0.02$
64606 | 38625 | 5200 | 4.6 | -0.71 | $0.70\pm 0.02$ | $0.69\pm 0.01$
65277 | 38931 | 4690 | 4.5 | -0.15 | $0.73\pm 0.02$ | $0.71\pm 0.01$
65430 | 39064 | 5140 | 4.4 | -0.05 | $0.82\pm 0.02$ | $0.85\pm 0.02$
65583 | 39157 | 5160 | 4.6 | -0.62 | $0.73\pm 0.01$ | $0.77\pm 0.01$
67228 | 39780 | 5830 | 3.9 | 0.21 | $1.25\pm 0.11$ | $1.88\pm 0.26$
68017 | 40118 | 5490 | 4.4 | -0.36 | $0.82\pm 0.01$ | $0.96\pm 0.02$
$\cdots$ | 40774 | 4970 | 4.8 | -0.24 | $0.73\pm 0.02$ | $0.69\pm 0.01$
72760 | 42074 | 5400 | 4.7 | 0.16 | $0.92\pm 0.03$ | $0.85\pm 0.01$
72946 | 42173 | 5670 | 4.5 | 0.16 | $1.02\pm 0.04$ | $0.97\pm 0.02$
73350 | 42333 | 5820 | 4.5 | 0.17 | $1.07\pm 0.05$ | $1.06\pm 0.08$
73667 | 42499 | 4840 | 4.5 | -0.51 | $0.71\pm 0.01$ | $0.73\pm 0.00$
75732 | 43587 | 5360 | 4.3 | 0.42 | $0.97\pm 0.05$ | $0.96\pm 0.02$
76151 | 43726 | 5780 | 4.4 | 0.15 | $1.04\pm 0.04$ | $1.01\pm 0.02$
78366 | 44897 | 5980 | 4.5 | 0.08 | $1.08\pm 0.04$ | $1.07\pm 0.02$
82106 | 46580 | 4870 | 4.7 | 0.05 | $0.78\pm 0.03$ | $0.75\pm 0.03$
82443 | 46843 | 5390 | 4.7 | 0.07 | $0.90\pm 0.03$ | $0.84\pm 0.01$
82885 | 47080 | 5590 | 4.4 | 0.38 | $1.04\pm 0.03$ | $0.98\pm 0.02$
84737 | 48113 | 5880 | 4.0 | 0.16 | $1.18\pm 0.07$ | $1.65\pm 0.04$
86728 | 49081 | 5760 | 4.2 | 0.25 | $1.07\pm 0.05$ | $1.20\pm 0.03$
87883 | 49699 | 4980 | 4.5 | 0.13 | $0.83\pm 0.03$ | $0.80\pm 0.01$
89269 | 50505 | 5590 | 4.4 | -0.13 | $0.87\pm 0.04$ | $0.95\pm 0.02$
90508 | 51248 | 5720 | 4.3 | -0.27 | $0.86\pm 0.03$ | $1.12\pm 0.03$
94765 | 53486 | 5090 | 4.6 | 0.13 | $0.85\pm 0.03$ | $0.81\pm 0.03$
95128 | 53721 | 5850 | 4.2 | 0.05 | $1.01\pm 0.05$ | $1.14\pm 0.03$
96064 | 54155 | 5490 | 4.6 | 0.12 | $0.94\pm 0.03$ | $0.87\pm 0.02$
96612 | 54426 | 4910 | 4.7 | -0.09 | $0.76\pm 0.03$ | $0.73\pm 0.01$
97334 | 54745 | 5960 | 4.6 | 0.16 | $1.11\pm 0.03$ | $1.06\pm 0.02$
97658 | 54906 | 5120 | 4.6 | -0.23 | $0.77\pm 0.03$ | $0.75\pm 0.01$
98281 | 55210 | 5390 | 4.5 | -0.16 | $0.83\pm 0.03$ | $0.86\pm 0.02$
99491 | 55846 | 5520 | 4.3 | 0.37 | $1.02\pm 0.04$ | $0.98\pm 0.02$
99492 | 55848 | 4950 | 4.4 | 0.31 | $0.86\pm 0.03$ | $0.82\pm 0.02$
100180 | 56242 | 5990 | 4.3 | 0.03 | $1.06\pm 0.05$ | $1.12\pm 0.03$
101177 | 56809 | 5870 | 4.3 | -0.16 | $0.93\pm 0.05$ | $1.02\pm 0.10$
101501 | 56997 | 5530 | 4.6 | 0.02 | $0.92\pm 0.03$ | $0.88\pm 0.02$
103095 | 57939 | 4970 | 5.0 | -1.18 | $0.63\pm 0.02$ | $0.60\pm 0.00$
105631 | 59280 | 5470 | 4.5 | 0.24 | $0.98\pm 0.04$ | $0.92\pm 0.02$
108954 | 61053 | 6010 | 4.4 | -0.05 | $1.04\pm 0.05$ | $1.10\pm 0.03$
109358 | 61317 | 5820 | 4.3 | -0.19 | $0.91\pm 0.04$ | $1.11\pm 0.03$
110463 | 61946 | 4970 | 4.7 | 0.04 | $0.79\pm 0.03$ | $0.74\pm 0.01$
110833 | 62145 | 5080 | 4.6 | 0.20 | $0.87\pm 0.03$ | $0.83\pm 0.02$
110897 | 62207 | 5780 | 4.4 | -0.47 | $0.82\pm 0.02$ | $1.03\pm 0.02$
111395 | 62523 | 5670 | 4.5 | 0.16 | $1.00\pm 0.04$ | $0.95\pm 0.02$
112758 | 63366 | 5040 | 4.4 | -0.41 | $0.76\pm 0.01$ | $0.80\pm 0.01$
112914 | 63406 | 4840 | 4.7 | -0.24 | $0.71\pm 0.02$ | $0.68\pm 0.01$
114710 | 64394 | 6020 | 4.4 | 0.08 | $1.10\pm 0.04$ | $1.10\pm 0.03$
114783 | 64457 | 5140 | 4.5 | 0.17 | $0.87\pm 0.04$ | $0.84\pm 0.02$
115383 | 64792 | 6080 | 4.3 | 0.21 | $1.20\pm 0.05$ | $1.23\pm 0.03$
115404 | 64797 | 5030 | 4.7 | -0.07 | $0.78\pm 0.03$ | $0.75\pm 0.03$
116442 | 65352 | 5150 | 4.6 | -0.33 | $0.76\pm 0.03$ | $0.77\pm 0.01$
116443 | 65355 | 5000 | 4.6 | -0.31 | $0.74\pm 0.02$ | $0.73\pm 0.01$
116956 | 65515 | 5450 | 4.6 | 0.22 | $0.96\pm 0.03$ | $0.88\pm 0.01$
117043 | 65530 | 5580 | 4.2 | 0.23 | $1.00\pm 0.05$ | $1.18\pm 0.14$
117936 | 66147 | 4820 | 4.5 | 0.12 | $0.79\pm 0.03$ | $0.75\pm 0.01$
118096 | 66193 | 4550 | 4.7 | -0.40 | $0.63\pm 0.02$ | $0.61\pm 0.01$
119332 | 66781 | 5210 | 4.6 | 0.01 | $0.84\pm 0.03$ | $0.82\pm 0.02$
119802 | 67105 | 4780 | 4.6 | 0.09 | $0.77\pm 0.03$ | $0.74\pm 0.01$
122064 | 68184 | 4820 | 4.4 | 0.21 | $0.81\pm 0.03$ | $0.79\pm 0.01$
122742 | 68682 | 5540 | 4.4 | 0.06 | $0.93\pm 0.04$ | $0.99\pm 0.02$
124292 | 69414 | 5460 | 4.5 | -0.03 | $0.88\pm 0.04$ | $0.90\pm 0.02$
124642 | 69526 | 4770 | 4.6 | 0.12 | $0.77\pm 0.02$ | $0.73\pm 0.01$
125455 | 70016 | 5120 | 4.6 | -0.08 | $0.81\pm 0.03$ | $0.79\pm 0.02$
126053 | 70319 | 5650 | 4.5 | -0.29 | $0.84\pm 0.03$ | $0.95\pm 0.02$
127334 | 70873 | 5690 | 4.2 | 0.25 | $1.05\pm 0.05$ | $1.24\pm 0.03$
127506 | 70950 | 4730 | 4.6 | -0.08 | $0.73\pm 0.02$ | $0.70\pm 0.01$
128165 | 71181 | 4800 | 4.5 | 0.03 | $0.77\pm 0.03$ | $0.75\pm 0.01$
128311 | 71395 | 5000 | 4.6 | 0.17 | $0.84\pm 0.03$ | $0.78\pm 0.01$
130004 | 72146 | 4940 | 4.7 | -0.20 | $0.75\pm 0.03$ | $0.71\pm 0.01$
130307 | 72312 | 5030 | 4.7 | -0.10 | $0.78\pm 0.03$ | $0.74\pm 0.01$
130948 | 72567 | 6030 | 4.5 | 0.05 | $1.09\pm 0.04$ | $1.06\pm 0.02$
131156 | 72659 | 5510 | 4.7 | -0.05 | $0.89\pm 0.03$ | $0.84\pm 0.04$
131511 | 72848 | 5360 | 4.6 | 0.18 | $0.88\pm 0.02$ | $0.80\pm 0.01$
131582 | 72875 | 4720 | 4.6 | -0.33 | $0.70\pm 0.02$ | $0.69\pm 0.01$
132142 | 73005 | 5090 | 4.5 | -0.34 | $0.76\pm 0.02$ | $0.78\pm 0.01$
133640 | 73695 | 5820 | 4.1 | -0.50 | $0.84\pm 0.03$ | $1.28\pm 0.21$
135599 | 74702 | 5260 | 4.7 | 0.02 | $0.86\pm 0.03$ | $0.80\pm 0.01$
136923 | 75277 | 5340 | 4.6 | -0.02 | $0.86\pm 0.03$ | $0.83\pm 0.04$
137778 | 75722 | 5190 | 4.5 | 0.28 | $0.90\pm 0.03$ | $0.84\pm 0.01$
139477 | 76315 | 4740 | 4.6 | 0.03 | $0.74\pm 0.02$ | $0.71\pm 0.01$
139323 | 76375 | 5130 | 4.4 | 0.38 | $0.91\pm 0.04$ | $0.88\pm 0.02$
140538 | 77052 | 5670 | 4.4 | 0.09 | $0.98\pm 0.05$ | $0.98\pm 0.07$
141004 | 77257 | 5880 | 4.1 | 0.04 | $1.05\pm 0.05$ | $1.39\pm 0.03$
141272 | 77408 | 5370 | 4.7 | 0.07 | $0.90\pm 0.03$ | $0.84\pm 0.02$
142267 | 77801 | 5720 | 4.5 | -0.39 | $0.83\pm 0.02$ | $0.99\pm 0.02$
143761 | 78459 | 5740 | 4.1 | -0.18 | $0.91\pm 0.03$ | $1.36\pm 0.03$
144287 | 78709 | 5380 | 4.4 | -0.01 | $0.87\pm 0.03$ | $0.97\pm 0.02$
144579 | 78775 | 5140 | 4.6 | -0.60 | $0.73\pm 0.01$ | $0.76\pm 0.01$
144872 | 78913 | 4790 | 4.7 | -0.25 | $0.71\pm 0.02$ | $0.69\pm 0.01$
145675 | 79248 | 5360 | 4.2 | 0.45 | $0.97\pm 0.04$ | $1.00\pm 0.02$
145958 | 79492 | 5430 | 4.5 | 0.02 | $0.89\pm 0.04$ | $0.88\pm 0.05$
146233 | 79672 | 5770 | 4.3 | 0.07 | $0.99\pm 0.04$ | $0.97\pm 0.02$
149661 | 81300 | 5280 | 4.6 | 0.11 | $0.88\pm 0.03$ | $0.84\pm 0.02$
149806 | 81375 | 5360 | 4.4 | 0.27 | $0.95\pm 0.04$ | $0.91\pm 0.02$
152391 | 82588 | 5490 | 4.6 | 0.07 | $0.93\pm 0.03$ | $0.87\pm 0.02$
153525 | 83006 | 4900 | 4.7 | 0.02 | $0.77\pm 0.03$ | $0.73\pm 0.01$
153557 | 83020 | 4940 | 4.7 | 0.03 | $0.79\pm 0.03$ | $0.76\pm 0.03$
154345 | 83389 | 5470 | 4.6 | -0.05 | $0.89\pm 0.04$ | $0.87\pm 0.02$
154363 | 83591 | 4440 | 4.6 | -0.43 | $0.63\pm 0.01$ | $0.62\pm 0.01$
154417 | 83601 | 6040 | 4.4 | 0.07 | $1.10\pm 0.05$ | $1.12\pm 0.09$
155712 | 84195 | 4930 | 4.6 | -0.05 | $0.78\pm 0.03$ | $0.77\pm 0.01$
156668 | 84607 | 4790 | 4.5 | 0.05 | $0.78\pm 0.02$ | $0.77\pm 0.01$
156985 | 84616 | 4790 | 4.5 | -0.09 | $0.75\pm 0.02$ | $0.73\pm 0.01$
157214 | 84862 | 5620 | 4.2 | -0.32 | $0.85\pm 0.02$ | $1.12\pm 0.03$
157347 | 85042 | 5630 | 4.3 | 0.05 | $0.94\pm 0.04$ | $1.07\pm 0.02$
159062 | 85653 | 5290 | 4.4 | -0.31 | $0.79\pm 0.02$ | $0.86\pm 0.01$
159222 | 85810 | 5890 | 4.4 | 0.19 | $1.10\pm 0.05$ | $1.11\pm 0.03$
160269 | 86036 | 5910 | 4.4 | 0.05 | $1.05\pm 0.05$ | $1.15\pm 0.03$
161198 | 86722 | 5180 | 4.5 | -0.36 | $0.81\pm 0.01$ | $0.92\pm 0.01$
$\cdots$ | 87579 | 4960 | 4.7 | -0.03 | $0.77\pm 0.03$ | $0.73\pm 0.01$
164922 | 88348 | 5350 | 4.3 | 0.21 | $0.92\pm 0.03$ | $1.00\pm 0.02$
165341 | 88601 | 5360 | 4.5 | 0.14 | $0.91\pm 0.04$ | $0.88\pm 0.05$
165401 | 88622 | 5800 | 4.5 | -0.32 | $0.87\pm 0.04$ | $0.98\pm 0.02$
166620 | 88972 | 4960 | 4.5 | -0.14 | $0.78\pm 0.02$ | $0.78\pm 0.01$
168009 | 89474 | 5770 | 4.2 | 0.04 | $0.98\pm 0.04$ | $1.21\pm 0.03$
170493 | 90656 | 4770 | 4.4 | 0.27 | $0.81\pm 0.03$ | $0.78\pm 0.01$
$\cdots$ | 91605 | 4860 | 4.7 | -0.31 | $0.71\pm 0.03$ | $0.69\pm 0.01$
174080 | 92283 | 4730 | 4.5 | 0.12 | $0.77\pm 0.03$ | $0.74\pm 0.01$
176051 | 93017 | 6020 | 4.4 | -0.04 | $1.06\pm 0.05$ | $1.24\pm 0.03$
176377 | 93185 | 5810 | 4.5 | -0.22 | $0.91\pm 0.05$ | $0.96\pm 0.02$
178126 | 93871 | 4480 | 4.5 | -0.43 | $0.65\pm 0.01$ | $0.64\pm 0.01$
178428 | 93966 | 5670 | 4.1 | 0.18 | $1.02\pm 0.05$ | $1.30\pm 0.03$
179957 | 94336 | 5670 | 4.2 | 0.03 | $0.94\pm 0.04$ | $1.14\pm 0.03$
180161 | 94346 | 5480 | 4.6 | 0.23 | $0.97\pm 0.03$ | $0.89\pm 0.02$
182488 | 95319 | 5420 | 4.4 | 0.22 | $0.95\pm 0.05$ | $0.95\pm 0.02$
185414 | 96395 | 5800 | 4.4 | -0.08 | $0.95\pm 0.05$ | $1.00\pm 0.02$
186408 | 96895 | 5760 | 4.2 | 0.11 | $1.02\pm 0.05$ | $1.28\pm 0.03$
186427 | 96901 | 5720 | 4.2 | 0.10 | $0.99\pm 0.04$ | $1.16\pm 0.03$
187691 | 97675 | 6060 | 4.1 | 0.15 | $1.20\pm 0.05$ | $1.47\pm 0.04$
189733 | 98505 | 5070 | 4.6 | 0.08 | $0.83\pm 0.03$ | $0.78\pm 0.01$
190067 | 98677 | 5340 | 4.6 | -0.29 | $0.79\pm 0.03$ | $0.80\pm 0.01$
190360 | 98767 | 5560 | 4.1 | 0.26 | $0.99\pm 0.04$ | $1.17\pm 0.03$
190404 | 98792 | 4860 | 4.5 | -0.59 | $0.70\pm 0.01$ | $0.72\pm 0.00$
190406 | 98819 | 5950 | 4.4 | 0.08 | $1.07\pm 0.04$ | $1.07\pm 0.03$
190470 | 98828 | 5090 | 4.5 | 0.20 | $0.86\pm 0.04$ | $0.83\pm 0.02$
190771 | 98921 | 5860 | 4.5 | 0.22 | $1.10\pm 0.05$ | $1.07\pm 0.07$
191499 | 99316 | 5260 | 4.5 | -0.07 | $0.82\pm 0.03$ | $0.81\pm 0.04$
191785 | 99452 | 5100 | 4.3 | -0.07 | $0.81\pm 0.02$ | $0.85\pm 0.02$
192263 | 99711 | 5010 | 4.6 | 0.07 | $0.82\pm 0.03$ | $0.78\pm 0.03$
197076 | 102040 | 5820 | 4.4 | -0.05 | $0.97\pm 0.05$ | $1.00\pm 0.02$
198425 | 102766 | 4970 | 4.6 | -0.01 | $0.79\pm 0.03$ | $0.75\pm 0.01$
$\cdots$ | 103256 | 4700 | 4.6 | 0.01 | $0.75\pm 0.02$ | $0.72\pm 0.01$
200560 | 103859 | 5060 | 4.6 | 0.24 | $0.88\pm 0.03$ | $0.82\pm 0.01$
201091 | 104214 | 4510 | 4.6 | -0.24 | $0.60\pm 0.01$ | $0.58\pm 0.01$
202575 | 105038 | 4790 | 4.7 | -0.04 | $0.74\pm 0.02$ | $0.71\pm 0.01$
202751 | 105152 | 4720 | 4.5 | -0.09 | $0.74\pm 0.02$ | $0.73\pm 0.01$
206860 | 107350 | 6010 | 4.6 | 0.02 | $1.06\pm 0.03$ | $1.01\pm 0.02$
208038 | 108028 | 5050 | 4.7 | 0.01 | $0.81\pm 0.03$ | $0.76\pm 0.01$
208313 | 108156 | 5060 | 4.6 | 0.04 | $0.82\pm 0.03$ | $0.78\pm 0.01$
210277 | 109378 | 5510 | 4.2 | 0.23 | $0.96\pm 0.04$ | $1.11\pm 0.03$
210667 | 109527 | 5460 | 4.5 | 0.25 | $0.97\pm 0.04$ | $0.96\pm 0.07$
211472 | 109926 | 5350 | 4.7 | 0.09 | $0.89\pm 0.03$ | $0.83\pm 0.01$
214683 | 111888 | 4850 | 4.7 | -0.24 | $0.71\pm 0.02$ | $0.68\pm 0.01$
215152 | 112190 | 4900 | 4.5 | 0.06 | $0.80\pm 0.03$ | $0.77\pm 0.01$
216259 | 112870 | 4780 | 4.6 | -0.65 | $0.68\pm 0.02$ | $0.67\pm 0.01$
217014 | 113357 | 5780 | 4.2 | 0.23 | $1.07\pm 0.05$ | $1.19\pm 0.03$
217107 | 113421 | 5650 | 4.2 | 0.37 | $1.06\pm 0.04$ | $1.17\pm 0.03$
217580 | 113718 | 5010 | 4.5 | 0.16 | $0.81\pm 0.03$ | $0.76\pm 0.01$
217813 | 113829 | 5900 | 4.5 | 0.10 | $1.06\pm 0.05$ | $1.04\pm 0.07$
218868 | 114456 | 5550 | 4.4 | 0.27 | $1.00\pm 0.05$ | $0.98\pm 0.02$
219134 | 114622 | 4840 | 4.5 | 0.11 | $0.79\pm 0.03$ | $0.76\pm 0.01$
219538 | 114886 | 5100 | 4.6 | 0.03 | $0.83\pm 0.03$ | $0.80\pm 0.01$
219623 | 114924 | 6110 | 4.2 | 0.09 | $1.15\pm 0.05$ | $1.27\pm 0.03$
220182 | 115331 | 5410 | 4.7 | 0.13 | $0.92\pm 0.03$ | $0.85\pm 0.01$
220221 | 115341 | 4850 | 4.5 | 0.20 | $0.81\pm 0.03$ | $0.77\pm 0.01$
221354 | 116085 | 5210 | 4.3 | 0.10 | $0.86\pm 0.03$ | $0.90\pm 0.02$
221851 | 116416 | 5170 | 4.6 | 0.01 | $0.83\pm 0.03$ | $0.79\pm 0.01$
222143 | 116613 | 5860 | 4.5 | 0.18 | $1.08\pm 0.05$ | $1.06\pm 0.07$
224465 | 118162 | 5740 | 4.3 | 0.10 | $1.00\pm 0.05$ | $1.05\pm 0.02$

##  Appendix B AO Observations

##  Appendix C Binary Companions

Table 4: Binary Companions to 25 pc Stars HD | HIP | Comp | M0 ($M_{\odot}$) | Source | Mc ($M_{\odot}$) | Source | Mass Refs | a/Sep (AU) | Source | Sep Refs
---|---|---|---|---|---|---|---|---|---|---
224930 | 171 | B | $0.72\pm 0.03$ | Spec | $0.78\pm 0.09$ | Dyn | 2,3 | $10.23\pm 0.23$ | Dyn | 3
123 | 518 | Ba | $1.01\pm 0.04$ | Spec | $0.91\pm 0.09$ | Phot | 4 | $32.39\pm 0.42$ | Proj | 4,5
123 | 518 | Bb | $1.01\pm 0.04$ | Spec | $0.46\pm 0.05$ | Phot | 4 | $32.39\pm 0.42$ | Proj | 4,5
3196 | 2762 | Ab | $1.12\pm 0.05$ | BV | $0.37\pm 0.04$ | Dyn- | 4 | $0.04\pm 0.00$ | Dyn | 4
3196 | 2762 | B | $1.12\pm 0.05$ | BV | $0.97\pm 0.10$ | Phot | 4 | $4.64\pm 0.32$ | Dyn | 2,6
3651 | 3093 | B | $0.89\pm 0.03$ | Spec | $0.05\pm 0.01$ | Phot | 7 | $477.79\pm 4.45$ | Proj | 7
4614 | 3821 | B | $0.91\pm 0.04$ | Spec | $0.59\pm 0.02$ | Spec | 4,8,9 | $70.08\pm 1.13$ | Dyn | 9
6101 | 4849 | B | $0.73\pm 0.03$ | Spec | $0.40\pm 0.11$ | Dyn | 10,8 | $9.84\pm 0.35$ | Dyn | 10
6101 | 4849 | C | $0.73\pm 0.03$ | Spec | $0.37\pm 0.05$ | Phot | 11 | $19410.11\pm 170.99$ | Proj | 12,5
6582 | 5336 | Ab | $0.69\pm 0.03$ | Spec | $0.20\pm 0.05$ | Dyn | 13 | $7.39\pm 0.17$ | Dyn | 13,14
8997 | 6917 | Ab | $0.79\pm 0.04$ | BV | $0.63\pm 0.06$ | Dyn,Phot | 15 | $0.11\pm 0.00$ | Dyn | 16
10307 | 7918 | Ab | $1.03\pm 0.06$ | Spec | $0.29\pm 0.07$ | Dyn | 17 | $8.03\pm 0.18$ | Dyn | 16
13789 | 10416 | B | $0.78\pm 0.03$ | Spec | $0.22\pm 0.01$ | Phot | 1 | $62.63\pm 6.26$ | Proj | 1
13974 | 10644 | Ab | $1.07\pm 0.03$ | BV | $0.86\pm 0.09$ | Dyn | 4 | $0.11\pm 0.00$ | Dyn | 18
14214 | 10723 | Ab | $1.23\pm 0.11$ | Spec | $0.46\pm 0.01$ | Dyn | 19 | $0.48\pm 0.01$ | Dyn | 19
16160 | 12114 | Ab | $0.73\pm 0.03$ | Spec | $0.12\pm 0.01$ | Dyn | 20 | $14.55\pm 0.94$ | Dyn | 20,6
16160 | 12114 | B | $0.73\pm 0.03$ | Spec | $0.22\pm 0.02$ | Phot | 5 | $1187.79\pm 3.28$ | Proj | 21,5
16287 | 12158 | Ab | $0.87\pm 0.03$ | Spec | $0.12\pm 0.00$ | Dyn | 22,8 | $0.12\pm 0.00$ | Dyn | 22
16739 | 12623 | Ab | $1.10\pm 0.04$ | BV | $1.18\pm 0.12$ | Phot | 22 | $1.23\pm 0.02$ | Dyn | 18,6
16909 | 12709 | Ab | $0.75\pm 0.03$ | Spec | $0.29\pm 0.01$ | Dyn | 16 | $2.27\pm 0.02$ | Dyn | 16
17382 | 13081 | Ab | $0.91\pm 0.04$ | Spec | $0.20\pm 0.01$ | Dyn | 16 | $6.66\pm 0.23$ | Dyn | 16
17382 | 13081 | B | $0.91\pm 0.04$ | Spec | $0.19\pm 0.01$ | Phot | 23 | $468.61\pm 2.18$ | Proj | 23,24,5
18143 | 13642 | B | $0.91\pm 0.04$ | Spec | $0.65\pm 0.02$ | Phot | 5 | $108.12\pm 3.89$ | Proj | 5
18143 | 13642 | C | $0.91\pm 0.04$ | Spec | $0.31\pm 0.02$ | Phot | 5 | $987.05\pm 5.21$ | Proj | 25
18757 | 14286 | D | $0.85\pm 0.03$ | Spec | $0.03\pm 0.00$ | Dyn- | 26 | $22.00\pm 2.31$ | Dyn | 26
18757 | 14286 | C | $0.85\pm 0.03$ | Spec | $0.32\pm 0.03$ | Phot | 4 | $6212.78\pm 4.51$ | Proj | 4
19994 | 14954 | Ba | $1.32\pm 0.09$ | Spec | $0.52\pm 0.05$ | Phot | 4 | $50.33\pm 16.87$ | Proj | 25,5
19994 | 14954 | Bb | $1.32\pm 0.09$ | Spec | $0.06\pm 0.01$ | U | 4 | $50.33\pm 16.87$ | Proj | 25,5
22879 | 17147 | B | $0.74\pm 0.03$ | Spec | $0.09\pm 0.00$ | Phot | 5 | $498.05\pm 10.95$ | Proj | 25,5
23439 | 17666 | Ba | $0.62\pm 0.02$ | Spec | $0.77\pm 0.08$ | Dyn | 27 | $164.72\pm 2.95$ | Proj | 25,5
23439 | 17666 | Bb | $0.62\pm 0.02$ | Spec | $0.12\pm 0.01$ | Dyn | 27 | $164.72\pm 2.95$ | Proj | 25,5
24496 | 18267 | B | $0.92\pm 0.04$ | Spec | $0.53\pm 0.05$ | U | 4 | $72.81\pm 5.07$ | Proj | 4,5
24409 | 18413 | D | $0.93\pm 0.04$ | Spec | $0.63\pm 0.06$ | Phot | 4 | $12.29\pm 0.86$ | Dyn | 4
24409 | 18413 | E | $0.93\pm 0.04$ | Spec | $0.33\pm 0.03$ | Phot | 4 | $216.39\pm 6.48$ | Proj | 25
25893 | 19255 | Ba | $0.97\pm 0.04$ | Spec | $0.68\pm 0.02$ | Phot | 1 | $59.05\pm 1.40$ | Proj | 25
25893 | 19255 | Bb | $0.97\pm 0.04$ | Spec | $0.68\pm 0.02$ | Phot | 1 | $61.11\pm 6.11$ | Proj | 1
25893 | 19255 | E | $0.97\pm 0.04$ | Spec | $1.31\pm 0.05$ | Spec | 8 | $15834.80\pm 88.87$ | Proj | 25,28,5
26965 | 19849 | B | $0.73\pm 0.03$ | Spec | $0.57\pm 0.02$ | Dyn | 29 | $393.33\pm 16.43$ | Proj | 5
26965 | 19849 | C | $0.73\pm 0.03$ | Spec | $0.20\pm 0.01$ | Dyn | 29 | $393.33\pm 16.43$ | Proj | 5
26923 | 19859 | B | $1.04\pm 0.05$ | Spec | $0.97\pm 0.04$ | Spec | 4,8 | $1418.08\pm 3.62$ | Proj | 25,4
26913 | 19855 | A | $0.97\pm 0.04$ | Spec | $1.03\pm 0.05$ | Spec | 4,8 | $1419.17\pm 3.50$ | Proj | 25,4
32850 | 23786 | Ab | $0.84\pm 0.03$ | Spec | $0.30\pm 0.03$ | Dyn- | 30 | $0.71\pm 0.01$ | Dyn | 30
32923 | 23835 | Ab | $0.95\pm 0.06$ | Spec | $0.94\pm 0.09$ | Phot | 8 | $2.86\pm 0.29$ | Proj | 4
34673 | 24819 | Ba | $0.71\pm 0.03$ | Spec | $0.47\pm 0.01$ | Phot | 1 | $26.32\pm 39.96$ | Proj | 5
34673 | 24819 | Bb | $0.71\pm 0.03$ | Spec | $0.30\pm 0.06$ | Phot | 1 | $32.43\pm 3.24$ | Proj | 1
35112 | 25119 | B | $0.77\pm 0.03$ | Spec | $0.50\pm 0.05$ | Dyn | 6,8 | $21.82\pm 1.49$ | Dyn | 31
37394 | 26779 | B | $0.93\pm 0.04$ | Spec | $0.56\pm 0.02$ | Phot | 5 | $1204.00\pm 1.74$ | Proj | 25,28,5
39587 | 27913 | Ab | $1.05\pm 0.04$ | Spec | $0.15\pm 0.01$ | Dyn | 32 | $6.23\pm 0.08$ | Dyn | 32
40397 | 28267 | B | $0.87\pm 0.03$ | Spec | $0.27\pm 0.03$ | Phot | 4 | $60.84\pm 6.08$ | Proj | 25,5
40397 | 28267 | D | $0.87\pm 0.03$ | Spec | $0.12\pm 0.01$ | Phot | 4 | $2168.22\pm 216.82$ | Proj | 25,5
43587 | 29860 | Ab | $1.06\pm 0.03$ | BV | $0.54\pm 0.05$ | Phot | 33 | $12.33\pm 0.16$ | Dyn | 19
43587 | 29860 | Ea | $1.06\pm 0.03$ | BV | $0.25\pm 0.06$ | Dyn | 34 | $1991.66\pm 53.07$ | Proj | 25,4
43587 | 29860 | Eb | $1.06\pm 0.03$ | BV | $0.12\pm 0.02$ | Dyn | 34 | $1991.66\pm 53.07$ | Proj | 25,4
45088 | 30630 | Ab | $0.80\pm 0.04$ | BV | $0.63\pm 0.06$ | Dyn | 35 | $0.08\pm 0.00$ | Dyn | 16
45088 | 30630 | B | $0.80\pm 0.04$ | BV | $0.27\pm 0.03$ | Phot | 35 | $47.69\pm 4.77$ | Proj | 25
263175 | 32423 | B | $0.69\pm 0.03$ | Spec | $0.41\pm 0.03$ | Phot | 5 | $795.98\pm 79.60$ | Proj | 25,5
50281 | 32984 | Ba | $0.76\pm 0.03$ | Spec | $0.48\pm 0.03$ | Phot | 5 | $513.58\pm 51.36$ | Proj | 25,5
50281 | 32984 | Bb | $0.76\pm 0.03$ | Spec | $0.39\pm 0.10$ | Phot | 1 | $513.58\pm 51.36$ | Proj | 25,5
54371 | 34567 | Ab | $0.97\pm 0.01$ | BV | $0.50\pm 0.00$ | Dyn | 36 | $0.23\pm 0.00$ | Dyn | 36
hip36357 | 36357 | A | $0.79\pm 0.03$ | Spec | $1.40\pm 0.05$ | Phot | 37 | $13722.92\pm 10.65$ | Proj | 25,5
hip36357 | 36357 | B | $0.79\pm 0.03$ | Spec | $0.09\pm 0.01$ | Type | 23 | $13728.78\pm 1372.88$ | Proj | 25
61606 | 37349 | B | $0.81\pm 0.03$ | Spec | $0.65\pm 0.01$ | Phot | 5 | $816.45\pm 3.03$ | Proj | 25,5
64606 | 38625 | Ab | $0.67\pm 0.03$ | Spec | $0.18\pm 0.01$ | Dyn | 38 | $1.09\pm 0.01$ | Dyn | 38
64606 | 38625 | B | $0.67\pm 0.03$ | Spec | $0.31\pm 0.01$ | Phot | 1 | $13.58\pm 1.36$ | Proj | 1
64468 | 38657 | Ab | $0.79\pm 0.03$ | BV | $0.13\pm 0.11$ | Dyn | 16,5 | $0.57\pm 0.02$ | Dyn | 16
64468 | 38657 | E | $0.79\pm 0.03$ | BV | $0.11\pm 0.01$ | Phot | 1 | $93.37\pm 9.34$ | Proj | 1
65277 | 38931 | B | $0.70\pm 0.03$ | Spec | $0.33\pm 0.03$ | Phot | 5 | $94.73\pm 5.56$ | Proj | 25,5
65430 | 39064 | Ab | $0.80\pm 0.03$ | Spec | $0.08\pm 0.01$ | Dyn | 38,8 | $4.32\pm 0.15$ | Dyn | 38,39
68017 | 40118 | Ab | $0.78\pm 0.03$ | Spec | $0.16\pm 0.02$ | Phot | 40 | $15.46\pm 1.50$ | Dyn | 1
72760 | 42074 | Ab | $0.93\pm 0.04$ | Spec | $0.13\pm 0.01$ | Dyn | 1 | $31.19\pm 6.35$ | Dyn | 1
72946 | 42173 | Bb | $1.01\pm 0.04$ | Spec | $0.07\pm 0.00$ | Dyn | 41 | $6.49\pm 0.10$ | Dyn | 41
72946 | 42173 | Aa | $1.01\pm 0.04$ | Spec | $1.24\pm 0.12$ | Phot | 4 | $259.94\pm 4.41$ | Proj | 25,5
72946 | 42173 | Ab | $1.01\pm 0.04$ | Spec | $0.46\pm 0.05$ | Dyn | 4 | $259.94\pm 4.41$ | Proj | 25,5
72946 | 42173 | G | $1.01\pm 0.04$ | Spec | $0.35\pm 0.03$ | Phot | 5 | $3376.25\pm 5.40$ | Proj | 5
75767 | 43557 | Ab | $1.03\pm 0.04$ | BV | $0.28\pm 0.01$ | Dyn | 4,42,43 | $0.10\pm 0.00$ | Dyn | 42,44
75767 | 43557 | Ba | $1.03\pm 0.04$ | BV | $0.37\pm 0.04$ | U | 4,44 | $72.64\pm 4.45$ | Proj | 35,44,5
75767 | 43557 | Bb | $1.03\pm 0.04$ | BV | $0.37\pm 0.04$ | U | 4,44 | $72.64\pm 4.45$ | Proj | 35,44,5
75732 | 43587 | B | $0.99\pm 0.04$ | Spec | $0.28\pm 0.03$ | Phot | 5 | $1067.86\pm 3.63$ | Proj | 25,5
79096 | 45170 | Ab | $0.94\pm 0.03$ | BV | $0.85\pm 0.03$ | Dyn | 18 | $2.36\pm 0.02$ | Dyn | 16
79096 | 45170 | Ea | $0.95\pm 0.03$ | BV | $0.05\pm 0.02$ | Spec | 45 | $874.89\pm 87.49$ | Proj | 25,45
79096 | 45170 | Eb | $0.94\pm 0.03$ | BV | $0.05\pm 0.02$ | Phot | 46 | $874.89\pm 87.49$ | Proj | 25,45
79028 | 45333 | Ab | $1.07\pm 0.03$ | BV | $0.66\pm 0.15$ | Dyn | 36 | $0.15\pm 0.00$ | Dyn | 36
79555 | 45383 | Ab | $0.76\pm 0.03$ | Spec | $0.50\pm 0.05$ | Dyn | 47 | $17.04\pm 0.56$ | Dyn | 47
79555 | 45383 | B | $0.76\pm 0.03$ | Spec | $0.06\pm 0.00$ | Phot | 5 | $11793.76\pm 48.82$ | Proj | 5
79969 | 45617 | B | $0.77\pm 0.04$ | BV | $0.74\pm 0.07$ | Phot | 2 | $12.09\pm 0.82$ | Dyn | 2,6
80715 | 45963 | Ab | $0.78\pm 0.04$ | BV | $0.79\pm 0.05$ | Dyn | 16 | $0.06\pm 0.00$ | Dyn | 16
82443 | 46843 | B | $0.90\pm 0.04$ | Spec | $0.12\pm 0.01$ | Phot | 4 | $1175.30\pm 4.67$ | Proj | 25,5
82885 | 47080 | B | $1.06\pm 0.05$ | Spec | $0.32\pm 0.03$ | Phot | 5 | $38.25\pm 2.55$ | Dyn | 48
86728 | 49081 | Ba | $1.11\pm 0.07$ | Spec | $0.10\pm 0.01$ | Type | 49 | $2005.90\pm 22.57$ | Proj | 25,5
86728 | 49081 | Bb | $1.11\pm 0.07$ | Spec | $0.10\pm 0.01$ | Type | 49 | $2005.90\pm 22.57$ | Proj | 25,5
90508 | 51248 | B | $0.85\pm 0.03$ | Spec | $0.25\pm 0.01$ | Phot | 5 | $72.30\pm 16.82$ | Dyn | 50
96064 | 54155 | B | $0.95\pm 0.04$ | Spec | $0.60\pm 0.10$ | Dyn | 2,6 | $312.23\pm 31.22$ | Proj | 25
96064 | 54155 | C | $0.95\pm 0.04$ | Spec | $0.60\pm 0.10$ | Dyn | 2,6 | $312.23\pm 31.22$ | Proj | 25
97334 | 54745 | Ea | $1.10\pm 0.05$ | Spec | $0.05\pm 0.00$ | Dyn,Phot | 51 | $2032.04\pm 203.20$ | Proj | 25,5
97334 | 54745 | Eb | $1.10\pm 0.05$ | Spec | $0.05\pm 0.00$ | Dyn,Phot | 51 | $2032.04\pm 203.20$ | Proj | 25,5
99491 | 55846 | B | $1.03\pm 0.05$ | Spec | $0.86\pm 0.03$ | Spec | 8 | $513.10\pm 7.56$ | Proj | 25,5
99492 | 55848 | A | $0.86\pm 0.03$ | Spec | $1.03\pm 0.05$ | Spec | 8 | $513.21\pm 7.72$ | Proj | 25,5
100180 | 56242 | B | $1.06\pm 0.05$ | Spec | $0.74\pm 0.07$ | Phot | 4,5 | $366.67\pm 7.75$ | Proj | 25,5
101177 | 56809 | Ba | $0.93\pm 0.05$ | Spec | $0.82\pm 0.08$ | Phot | 4 | $195.53\pm 13.90$ | Dyn | 52
101177 | 56809 | Bb | $0.93\pm 0.05$ | Spec | $0.39\pm 0.04$ | Dyn | 4 | $176.89\pm 11.76$ | Dyn | 52
101206 | 56829 | Ab | $0.66\pm 0.02$ | Spec | $0.31\pm 0.03$ | Dyn- | 53 | $0.11\pm 0.00$ | Dyn | 22,53
109011 | 61100 | B | $0.77\pm 0.03$ | Spec | $0.64\pm 0.01$ | U | 54 | $2.60\pm 0.02$ | Dyn | 16,54
110833 | 62145 | Ab | $0.86\pm 0.03$ | Spec | $0.02\pm 0.01$ | Dyn- | 16 | $0.78\pm 0.01$ | Dyn | 16
112758 | 63366 | Ab | $0.70\pm 0.03$ | Spec | $0.03\pm 0.00$ | Dyn- | 16 | $0.39\pm 0.00$ | Dyn | 16
112758 | 63366 | B | $0.70\pm 0.03$ | Spec | $0.48\pm 0.10$ | Type | 23 | $10.77\pm 1.08$ | Proj | 25
112914 | 63406 | Ab | $0.70\pm 0.03$ | Spec | $0.21\pm 0.01$ | Dyn | 55 | $1.51\pm 0.02$ | Dyn | 55
113449 | 63742 | Ab | $0.85\pm 0.03$ | BV | $0.41\pm 0.01$ | Dyn- | 56 | $0.76\pm 0.01$ | Dyn | 56
115404 | 64797 | B | $0.80\pm 0.04$ | BV | $0.55\pm 0.10$ | Type | 23 | $92.87\pm 6.78$ | Dyn | 52