11institutetext: Institute for Particle Physics and Astrophysics, ETH Zurich,
Wolfgang-Pauli-Strasse 27, 8093 Zurich, Switzerland
11email<EMAIL_ADDRESS>22institutetext: Geneva Observatory,
University of Geneva, Chemin des Mailettes 51, 1290 Versoix, Switzerland
33institutetext: Center for Space and Habitability, University of Bern, Bern,
Switzerland 44institutetext: Institute of astrophysics, KU Leuven,
Celestijnlaan 200D, 3001 Leuven, Belgium 55institutetext: University of
Michigan, Astronomy Department, USA 66institutetext: Department of Astronomy,
Cornell University, Ithaca, NY 14853, USA 77institutetext: NASA/GSFC, USA

# VIBES: VIsual Binary Exoplanet survey with SPHERE

Upper limits on wide S-planet and S-BD frequencies, triple system discovery,
and astrometric confirmation of 20 stellar binaries and three triple
systems††thanks: Based on observations collected at the European Southern
Observatory, Chile (ESO Open Time 096.C-0835, 097.C-0826, 098.C-0643,
0100.C-0543, 0101.C-0405).

J. Hagelberg 1122 N. Engler 11 C. Fontanive 33 S. Daemgen 11 S. P. Quanz 11 J.
Kühn 1133 M. Reggiani 44 M. Meyer 55 R. Jayawardhana 66 V. Kostov 77

(Received September 18, 2020; accepted March 16, 1997)

###### Abstract

Context. Recent surveys indicate that planets in binary systems are more
abundant than previously thought, which is in agreement with theoretical work
on disc dynamics and planet formation in binaries. So far, most observational
surveys, however, have focused on short-period planets in binaries, thus
little is known about the occurrence rates of planets on longer periods ($\geq
10$ au).

Aims. In order to measure the abundance and physical characteristics of wide-
orbit giant exoplanets in binary systems, we have designed the ’VIsual Binary
Exoplanet survey with Sphere’ (VIBES) to search for planets in visual
binaries. It uses the SPHERE instrument at VLT to search for planets in 23
visual binary and four visual triple systems with ages of ¡145 Myr and
distances of ¡150 pc.

Methods. We used the IRDIS dual-band imager on SPHERE to acquire high-contrast
images of the sample targets. For each binary, the two components were
observed at the same time with a coronagraph masking only the primary star.
For the triple star, the tight components were treated as a single star for
data reduction. This enabled us to effectively search for companions around 50
individual stars in binaries and four binaries in triples.

Results. We derived upper limits of $<$13.7% for the frequency of sub-stellar
companions around primaries in visual binaries, $<$26.5% for the fraction of
sub-stellar companions around secondaries in visual binaries, and an
occurrence rate of $<$9.0% for giant planets and brown dwarfs around either
component of visual binaries. We have combined our observations with
literature measurements to astrometrically confirm, for the first time, that
20 binaries and two triple systems, which were previously known, are indeed
physically bound. Finally, we discovered a third component of the binary HD
121336.

Conclusions. The upper limits we derived are compatible with planet formation
through the core accretion and the gravitational instability processes in
binaries. These limits are also in line with limits found for single star and
circumbinary planet search surveys.

###### Key Words.:

Planets and satellites: detection – Planets and satellites: dynamical
evolution and stability – binaries: visual – Planet-star interactions

##  1 Introduction

One key statistical outcome from the more than 4000 planets that have been
detected so far is that almost every Sun-like star harbours a planet (Fressin
et al., 2013; Udry & Santos, 2007; Dressing & Charbonneau, 2013; Howard et
al., 2012). Given that almost half of all stars in our Milky Way are bound in
multiple systems (Raghavan et al., 2010), one could expect a large fraction of
the detected exoplanets to be in binaries. Yet, less than 200 planets are
known to reside in multiple stellar
systems111http://www.univie.ac.at/adg/schwarz/multiple.html (Schwarz et al.,
2016) , either as circumstellar planets orbiting one of the two stars in the
binary or as circumbinary planets orbiting the centre of mass of both stars
(also known as S- and P-planets, respectively, Dvorak (1982, 1984)).

Early theoretical work predicted a lower occurrence rate of circumstellar
planets compared to single star planets, due to the fact that the
gravitational potential of a multi-star system and truncated protoplanetary
discs would hinder planet formation (Artymowicz & Lubow, 1994) and also render
the planet dynamically unstable for long-term survival (Holman & Wiegert,
1999). Large exoplanet search surveys thus often avoided or gave low priority
to binary stars in order to enhance the planet detection yield. Furthermore,
radial velocity, transit, and direct imaging techniques all tend to have lower
detection sensitivities for most binary configurations, thus further enhancing
the observational bias against planets in binaries.

Various multiplicity surveys, mostly searching with imaging for stellar
companions to known radial velocity (RV) or transit detected planet hosting
stars, have shown that the occurrence rate of circumstellar planets is in the
$\approx$ 10–30% range (e.g. review of various surveys in Wang et al. (2014)).
However for very close binaries, which have a separation smaller than 47 au,
planet occurrence is only 0.34 times the one of single stars or wider binaries
(Kraus et al., 2016). For very close binaries tighter than 20 au, disc
truncation and the high velocities of gas and dust induced by the secondary
star render planet formation almost impossible (Zsom et al., 2011). The
existence of a handful of such planets, such as $\gamma$ Cephei Ab (Hatzes et
al., 2003), HD 196885 Ab (Correia et al., 2008; Chauvin et al., 2011), and
Kepler 420 Ab (Santerne et al., 2014), may actually be the results of stellar
scattering (Martí & Beaugé, 2012) instead of having formed within the binary.

In contrast to the earlier theoretical studies mentioned above, recent
theoretical and observational results show that there is a possibility that
planet formation in multiple stars is in fact enhanced compared to single
stars as long as the binary separation is larger than 50 au. This enhancement
can be caused by the secondary inducing spiral density waves in the
protoplanetary disc, which can potentially stimulate gravitational instability
(GI) (Batygin et al., 2011; Rafikov, 2013). This is in line with observations
that suggest a 3-fold increase in hot Jupiter occurrence in binary stars
compared to singles (Wang et al., 2015; Ngo et al., 2016; Evans et al., 2018;
Fontanive et al., 2019). Binary stars should thus be taken into consideration
when analysing planet formation and evolution, not only because binary stars
represent $\sim 1/3$ of stars in the Milky Way (M: $26.8\%\pm 1.4\%$ Winters
et al. (2019), FGK: $33\%\pm 2\%$ Raghavan et al. (2010), A:
$32.1^{+3.9}_{-3.5}\%$ De Rosa et al. (2014)), but also to understand the
robustness and diversity of planet formation.

In order to probe the population of wide companions to binary stars we started
the ’VIsual Binary Exoplanet survey with Sphere’ (VIBES) which combines
SPHERE’s planet discovery and characterisation potential with its ability to
simultaneously target all components of a stellar multiple, at the cost of a
degraded sensitivity around the secondary star. Our survey searching for wide
circumstellar planets also fills the gap between the SHINE survey targeting
planets orbiting single stars and the ’Search for Planets Orbiting Two Stars’
(SPOTS, Thalmann et al., 2014)) survey which looks for wide circumbinary
planets. By combining the result on S- and P-type planet populations probed by
the VIBES and SPOTS surveys respectively, we have a census of the overall
population of wide giant planets in binaries. Given that a majority of our
targets are in the Scorpius-Centaurus association we are probing younger but
farther stars than other surveys like the NACO-LP (Chauvin et al., 2015;
Desidera et al., 2015), the IDPS (Vigan et al., 2012), and the NICI Campaign
(Liu et al., 2010; Biller et al., 2013) which were more sensitive to higher-
mass planets at smaller orbital separations than our survey. The smaller inner
working angle achieved with SPHERE and the fact that we are probing a
population at wider separation mitigates the effect of larger distance.

The survey is described in the following sections starting with the target
sample definition (Sect. 2). The strategy adopted for the observations and
data reduction are described in Sects. 3 and 4 respectively. The confirmation
of 26 binaries is described in Sect. 5, followed by the statistical analysis
of the complete survey in Sect. 6, and a discussion of the survey outcome in
Sec. 7.

##  2 Target sample

We compiled a sample of 26 multiple systems that are members of young clusters
and associations based on the catalogues listed in Table 1. We set selection
criteria on age and distance to be younger than 50 Myr and closer than 150 pc.
Considered regions were Taurus (1–2 Myr, d$\simeq$145 pc, Torres et al., 2009;
Kraus & Hillenbrand, 2009), Scorpius Centaurus (5–15 Myr, d$\simeq$140 pc, de
Zeeuw et al., 1999; Song et al., 2012; Pecaut et al., 2012), the $\beta$ Pic
moving group ($22\pm 6$ Myr, d$\simeq$30 pc, Binks & Jeffries, 2014; Bell et
al., 2015; Shkolnik et al., 2017), Tucana–Horologium ($45\pm 4$ Myr,
d$\simeq$46 pc, Bell et al., 2015), and Columba ($42^{+6}_{-4}$ Myr,
d$\simeq$50 pc, Bell et al., 2015). The heterogeneity induced by the wide
range of age and distance in the associations has less impact on the survey
sensitivity than the large variation in observing conditions, also because the
final sample is dominated by Sco-Cen stars. Nonetheless this heterogeneity is
taken into account in the final statistical analysis.

Table 1: Astrometric and photometric uncertainties for this work and for measurements sourced in the literature. Ref | Error Sep | Error PA | mag
---|---|---|---
| (″) | (deg) |
This work | 0.005 | 0.3 | 0.5
Chauvin et al. (2003) | 0.03 | 2.5 | 0.16
Daemgen et al. (2015) | 0.001 | 0.1 | 0.01
Elliott et al. (2015) | 0.01 | 0.29 | 0.03
Fabricius et al. (2002) | 0.14 | 1 | –
Gaia Collaboration et al. (2018) | 0.0001 | 0.07 | –
Hartkopf et al. (1996) | 0.003 | 0.2 | –
Herschel et al. (1874) | – | – | –
Janson et al. (2013) | 0.008 | 0.4 | 0.2
Kouwenhoven et al. (2005) | 0.0015 | 0.03 | –
Kouwenhoven (2006) | 0.0015 | 0.03 | 0.12
Kohler & Leinert (1998) | 0.003 | 0.4 | –
McAlister et al. (1990) | 0.003 | 0.2 | –
Tokovinin (1997) | – | – | –
Table 2: Associations and the age adopted for its members. Acronym | Name | Age | Ref
---|---|---|---
ABD | AB Doradus | $145^{+51}_{-19}$ | Bell et al. (2015)
BPMG |  $\beta$ Pictoris MG | $22\pm 6$ | Shkolnik et al. (2017)
LCC | Lower Centaurus Crux | $17\pm 1$ | Pecaut et al. (2012)
ROPH |  $\rho$ Ophiuchi | 2–5 | Wilking et al. (2008)
TAU | Taurus Molecular Cloud | 1–2 | Kenyon & Hartmann (1995)
THA | Tucana-Horologium Association | $45\pm 4$ | Bell et al. (2015)
UCL | Upper Centaurus Lupus | $16\pm 1$ | Pecaut et al. (2012)
USCO | Upper Scorpius | $11\pm 3$ | Pecaut et al. (2012)

Our targets were selected in order to be bright enough (R ¡ 11 mag) to provide
good adaptive optics correction, with a binary separation in the 0.8″– 5″range
to prevent AO wavefront sensing instabilities while still having both stars
simultaneously in the IRDIS field of view. The targets’ primary spectral types
range between B6 and M0 with a median mass of 2.1 $M_{\sun}$. The selected
projected binary separations translate to $\sim 30$–600 au at their respective
distances. Considering that dynamical interaction limits the maximum orbital
separation of potential planetary companions on S-type orbits to $\sim 0.3$
times the binary separation on average, we should expect these planetary
companions to be in the $\sim 10$–170 au range (Holman & Wiegert, 1999). The
age range of 1–145 Myr is chosen in order to optimise our planet detection
sensitivity as planets are brightest when they are young (independent of the
exact formation process, e.g. hot- or cold-start; Fortney et al., 2008; Marley
et al., 2007).

The publications out of which our sample was sourced are listed in Table 1
along with the uncertainties on the measured separation, position angle, and
magnitude differences given by the authors. To determine the age of our
targets we used the BANYAN $\Sigma$ tool (Gagné et al., 2018). This Bayesian
analysis algorithm uses galactic coordinates (XYZ) and space velocities (UVW)
of the star to compute the membership probability to nearby young associations
listed in Table 2. The results of this analysis are presented in Table 3
giving the identified moving group to which the star is most probably
associated and the membership probability. The table also lists the spectral
types, coordinates, and distance given by Gaia (Gaia Collaboration et al.,
2018). Finally, for HD 102026 which was identified as a field star we used the
age determined photometrically by Tetzlaff et al. (2011) of $15.8\pm 7.3$ Myr.

The sample we assembled based on these criteria is listed in Table 3 including
the existing astrometric measurements of the binaries. The spread in binary
separations is illustrated in Fig. 1. It should be noted that for the triple
systems HD 112381, HD 121336, HD 138138, and HD 146331 we are not sensitive to
planets which would be orbiting in between the two tight components of these
hierarchical triple systems. Except for HD 217379 and HD 285281 that have an
estimated age of $\sim 145$ Myr and $\sim 45$ Myr respectively, all remaining
targets in the sample have an age less than 22 Myr as illustrated in Fig. 2.

##  3 Observational strategy

The observations were carried out with SPHERE at VLT (Beuzit et al., 2008) in
IRDIFS mode which simultaneously acquires data in dual-band imaging through
the $H2H3$ filters ( $\lambda_{H2}=1.593\pm 0.055\mu\mathrm{m}$;
$\lambda_{H3}=1.667\pm 0.056\mu m$, Vigan et al., 2010) with IRDIS (Dohlen et
al., 2008), and integral field spectroscopy in $Y$ – $J$ (0.95–1.35 $\mu$ m,
$R_{\lambda}\sim$ 54) with the IFS (Claudi et al., 2008). Both instruments are
situated behind the 185 mas diameter apodized Lyot coronagraph (Carbillet et
al., 2011; Guerri et al., 2011) which is masking the brighter star in $H$
band. The observations were all carried out in pupil-tracking (i.e. where the
field of view rotates), in order to apply Angular Differential Imaging (ADI,
Schneider & Silverstone, 2003; Liu, 2004; Marois et al., 2006) as described in
Sect. 4.

The same observing sequence is carried out for each target, which starts with
calibrations followed by science observations. The calibration sub-sequence is
composed of long exposure sky frames taken by offsetting the stars out of the
field of view in order to carry out bad pixel correction and to estimate the
background flux. We rely on unsaturated non-coronagraphic exposures with both
stars in the field-of-view to calibrate the photometry, the PSF profile, and
the astrometric configuration of the targeted binary system. The last part of
the calibration sequence are the star centring frames where satellite spots
are generated through a sinusoidal pattern on the deformable mirror (Jovanovic
et al., 2015; Rickman et al., 2020) in order to determine precisely the
position of the primary star behind the coronagraph. Typically the total
observing time dedicated to each target is an hour, which besides the 15
minutes spent on telescope slewing and target acquisition is split in
$80/20\%$ between the science observation and calibrations.

A certain number of targets have been carried out through an ESO filler
programme, which implies bad weather conditions and sub-optimal field
rotation. In certain cases these targets have been re-observed in better
conditions and with larger field rotation (Table 4).

The observational strategy exploits the large field of view of IRDIS to
simultaneously acquire both stars of all selected binaries. In principle this
doubles the total number of stars probed in comparison to a single star
survey. However the sensitivity around the second star is lower because the
star is not behind a coronagraph, and the field of view gets smaller as the
secondary star gets closer to the edge of the detector.

Second epoch follow-up observations were carried out on all sub-stellar
candidates in order to verify if they are co-moving and thus physically bound.
For these observations the star centring satellite spots were kept during the
whole observing sequence to improve the astrometric fitting in case of a
confirmed planetary companion detection.

##  4 Data reduction

The IRDIS data reduction is based on GRAPHIC (Hagelberg et al., 2016), with
modifications in order to have an end-to-end reduction for SPHERE data
(Cheetham et al., 2018). The pipeline cosmetics pre-processing involves sky
subtraction, flat fielding, bad pixel correction, filter splitting, distortion
correction (based on Maire et al. (2016)) and individual frame registration.
For the PSF subtraction we use ADI combined with algorithms based on principal
component analysis (PCA; Amara & Quanz, 2012; Soummer et al., 2012) applied on
$2\times FWHM$ wide concentric annular sections. In order to minimise
companion self-subtraction we exclude frames where the field rotation is less
than $1.25\times FWHM$ (the detailed procedure is described in Cheetham et al.
(2018)). The final images are then derotated in Fourier space and median
combined to produce the final image.

Additionally a Spectral Differential Imaging (SDI, Sparks & Ford, 2002)
reduction is also used where the $H3$ filter image is spatially and flux
rescaled by the wavelength ratio and subtracted from the simultaneous $H2$
image. The resulting frames are then run through the same PCA algorithm. This
Angular and Spectral Differential Imaging (ASDI) data reduction product thus
adds a third potential companion detection image to the two ADI reductions of
the $H2$ and $H3$ filters.

The IFS data reduction uses the SPHERE Data Reduction and Handling pipeline
(DRH, Pavlov et al., 2008) to produce calibrated data. This calibration
includes the background subtraction, bad pixel correction, the wavelength
calibration, correction for the spectral cross-talk (Mesa et al., 2015) and
the flat fielding. The wavelength-dependent centring of the frames is
performed using the satellite spots. The extracted IFS data cube consists of
temporal sequences of 39 monochromatic images with a format of $290\times 290$
pixels each, which are then processed using the PCA-based pipeline PynPoint
(Stolker et al., 2019).

Companions close to the secondary star may not be uncovered with a data
reduction centred on the primary star because of the typical azimuthal
smearing of bright field components from the ADI processing. More importantly
ADI (and ASDI) rely on the fact that the target PSF is not rotating, the data
reduction can thus only be optimised for one central PSF at a time. The only
changes in the data reduction for the secondary component is that the centring
is done by fitting a 2D Gaussian to the secondary PSF instead of using the
satellite spots. This comes from the fact that the secondary component is not
behind a coronagraph so that the satellite spots disappear in the stellar
halo. For similar mass binaries the PSF of the secondary is slightly saturated
but still usable for a Gaussian fit.

Each telescope pointing thus results in at least 5 data products, two IRDIS
reductions for each star in the field and one IFS reduction for the brightest
star in the field. In the case of triple systems we only reduced the two
brightest components as the third star was in every case too close to one of
the brighter stars.

Contrast curves are then computed by first estimating the noise as a function
of the separation to the central star by measuring the standard deviation in
concentric $\lambda/D$ wide annuli. These contrast curves are then calibrated
for throughput by injecting fake-planets based on a non-saturated PSF of the
star before PCA subtraction. Following the method proposed by Mawet et al.
(2014) the injection is done in order to keep a constant false positive rate
of $2.9\times 10^{-7}$ to correct for small sample statistics. The procedure
is repeated 10 times with a varying azimuth. Systematic effects such as
detector defects are not taken into account which thus results in a more
conservative detection limit.

All the IRDIS $H2$ contrast curves for the primary and secondary stars are
given in Fig. 3 with an arbitrary cut-off at $\sim 1.5$ and $\sim 2.5$
arcseconds for readability, along with the median contrast curves of the
primaries and of the secondaries. $H3$ yielded a similar sensitivity while
ASDI and IFS contrast curves where not used as they were deemed not robust
enough for a statistical analysis, even though ASDI and ADI-IFS data reduction
was done.

##  5 Observational results

Except for HIP 1910 (Chauvin et al., 2003), HD 165189 (Herschel et al., 1874),
HD 285281 (McAlister et al., 1990; Daemgen et al., 2015) which were already
identified as co-moving binaries, and HD 138138 (Hartkopf et al., 1996; Kohler
& Leinert, 1998) which is a known triple system, all the other targets in our
sample were suggested to be physically bound prior to our observations only
based on a statistical approach showing that the probability of the secondary
to be a background object was low (Chauvin et al., 2003; Daemgen et al., 2015;
Elliott et al., 2015; Fabricius et al., 2002; Gaia Collaboration et al., 2018;
Hartkopf et al., 1996; Herschel et al., 1874; Janson et al., 2013; Kouwenhoven
et al., 2005; Kouwenhoven, 2006; Kohler & Leinert, 1998; McAlister et al.,
1990; Tokovinin, 1997). As all our targets have now been observed at least
once by our programme we are able to verify through astrometry whether the
systems in our sample are indeed gravitationally bound. The more than five
years time baseline between the binary discovery observations and our
observations is sufficient for an unambiguous determination of the binaries’
physical link.

In order to measure the position angle (PA), separation, and magnitude
difference between the primary and secondary star we have used the photometric
calibration frames taken at the beginning of every sequence. In this data all
the PSFs are non-coronagraphic and unsaturated ensuring the best photometric
and astrometric accuracy. The PSF measurements are carried out using the
DAOPHOT FIND algorithm (Stetson, 1987) as implemented in Astropy (Astropy
Collaboration et al., 2013) where peaks over a given threshold are searched
and a two-component Gaussian is then fitted to that peak. The resulting FWHM
fit in x and y is used to determine the roundness of the PSF. From these
measurements we determine the astrometric position as well as the brightness
of the companion with respect to the primary star. Given that all observations
are taken in pupil stabilised mode we need to take into account the
parallactic angle correction when deriving the position angle (PA). This angle
correction is calculated by GRAPHIC (Hagelberg et al., 2016). The resulting PA
measurement when including PSF fitting (estimated at 0.4 pixel), true north
determination (Maire et al., 2016), and instrument pupil offset results in a
0.3 degree uncertainty. The separation measurement once corrected for the
anamorphic distortion between x and y axis is mostly dependent on the PSF
fitting accuracy resulting in an estimated 0.005″uncertainty on the
separation. For the magnitude measurement we have used the peak value of the
Gaussian fit on the primary and companion star. The magnitude of the companion
star is then derived by taking the 2MASS $H$ band magnitude (Skrutskie et al.,
2006) of the primary and applying the magnitude difference. This quick first
order approach is thus not taking into account the difference between the
2MASS-$H$ and IRDIS-$H2$ filters, which thus yields an overall uncertainty of
0.5 mag on the companion star magnitude.

A third faint stellar component was detected in the HD 121336 AB binary (Fig.
4). Given the faintness and close proximity to the secondary it probably was
beyond reach of previous instruments so that only a single epoch is available
for the moment. Using BT-Settl models with Solar metallicity (Allard, 2014) we
estimate the mass of component C to be around 0.95 M☉. Given that component C
is only at $0.346\arcsec$ of B (with a PA of $205.3\deg$ and $\Delta H$=2.3
with respect to B), we can assume that B-C form a binary orbiting A, if it is
bound.

Table 1 summarises the uncertainties on the measured stellar companions’ PAs,
separations, and magnitudes as well as those reported in the literature we
used to source previous measurements. The measurements from this and previous
work for the binaries are listed in Table LABEL:tab:vibes_sample.

To determine if the secondary component is bound or not we plot the positions
from all epochs (orange crosses in Fig. 7 to 9) and compare them to the
relative motion a background object would exhibit (blue lines). The line
starts at the companion coordinates of the first observed position and then
shows the astrometric track an object would follow with respect to the primary
star if it had no proper motion. The green crosses along this line represent
the positions a background object would have at the given observing dates. The
wobbles are caused by the parallactic motion. The proper motion we used is
based on Gaia (Collaboration et al., 2016; Lindegren et al., 2018) and
Hipparcos (Perryman et al., 1997) and it should be noted that all the parallax
measurements were graded as best quality, which means that the double star
nature of the targets did not interfere with the observations.

Figures 10 to 12 show the positions of the secondary star at all epochs. The
primary star is represented by the blue cross at the origin of the polar plot,
while the red dots represent the secondary positions. The orange error bars on
the secondary positions are mostly invisible due to the precision of the
measurements. A green curve is also plotted to illustrate the counter-
clockwise movement the secondary would have if it was on a face-on circular
orbit with the observed time baseline. The apparent movement caused by the
proper motion is not included in this plot as it would be too small to be
visible. Orbital motion is clearly visible in the $\sim 150$ years binary HIP
1910 AB.

From this analysis we can astrometrically confirm that 21 previously known
visible binaries and three triple systems are indeed physically bound. The
only exception are HD 165189 which was already known to be bound and HD 121336
AB where the binary is proven to be bound for the first time through
astrometry but where a third component is also detected for the first time.

##  6 Statistical analysis

We are able to place upper limits on the occurrence rates of brown dwarfs and
giant planets in binary systems. We must also rely on model predictions for
the underlying population distributions in order to constrain these
frequencies. As wide-orbit exoplanets and brown dwarfs in stellar binaries
have yet to be studied in depth on the theoretical side, we use population
synthesis models developed for single stars, and assume that circumprimary
sub-stellar companions follow similar distributions in mass and separation.
Our model population consists of two sub-populations, based on the GI (Boss,
1998) and core accretion (CA, Mizuno, 1980; Lin & Papaloizou, 1986; Pollack et
al., 1996) formation scenarios. The GI synthetic population comes from the
disc fragmentation simulations first presented in Forgan & Rice (2013) and
later updated by Forgan et al. (2018). The synthetic CA population was
obtained from the latest version of the Bern models of planetary formation and
evolution (Mordasini, 2018), and corresponds to population NG76 from the new
generation planetary population synthesis (NGPPS) series (Emsenhuber et al.,
2020a, b). Combined together, these two population synthesis models form the
full population model used in our statistical analysis. The CA formation
channel predicts lower-mass and closer-separation planets, while the GI
mechanism predicts more massive brown dwarfs on wider orbits (see e.g. Vigan
et al., 2020). The CA and GI populations serve as a first order approach as
they are based on single star models which do not take into account an outer
perturber. CA populations should be affected by truncation of the disc
(Artymowicz & Lubow, 1994), an increase of the random velocities of the
planetesimals, and a possible destabilisation of the planetary system through
N-body interactions with the binary, including Kozai (Lidov, 1962; Kozai,
1962). The outer stellar perturber could stabilise the protoplanetary disc
which would inhibit GI planet formation (Forgan & Rice, 2009) but it could
also trigger spiral density waves which would spark GI (Batygin et al., 2011;
Rafikov, 2013).

In order to constrain the statistical properties of our observed sample, we
first converted the obtained detection limits for each target into detection
probability maps in terms of companion masses and semi-major axes. We used the
COND-2003 evolutionary tracks (Baraffe et al., 2003) to convert observed
contrasts into companion mass using the respective magnitude, distance and age
of each target. Given the large range in the levels of contrast obtained, we
found these models to be the only ones covering the full span of achieved
detection limits. We then followed the Monte-Carlo procedure described in
Vigan et al. (2020) to perform the de-projection of the limits from projected
separation onto semi-major axis. We adopted for this the Beta distribution
from Bowler et al. (2020) to describe the eccentricity distribution of the
overall population of wide sub-stellar companions.

The resulting survey completeness is presented in Fig. 5, showing the average
2-dimensional completeness around the stellar primaries (left panel), stellar
secondaries (middle panel) and considering all binary components together
(right panel). These provide the average probability of detecting an object of
given mass and semi-major axis. The poorer contrasts achieved around the
secondaries result in a significant loss in the depth of the mass limits
reached around these stars compared to the primaries. In addition, some
detection limits were cut at very short projected separations due to the small
angular separation of the stellar binary system. As the limits around the most
distant stars in the sample start at wider physical separations than the cuts
made on some of these targets, there is no region in the parameter space with
a completeness fraction of 1.

From the derived completeness maps and adopted model populations, we
constrained the frequencies of sub-stellar companions in visual stellar
binaries using the statistical code from Fontanive et al. (2018). This Markov
Chain Monte Carlo (MCMC) sampling tool was built using the emcee (Foreman-
Mackey et al., 2013) python algorithm, and aims at placing statistical
constraints on stellar and sub-stellar populations (occurrence rates, mass and
separation distributions of companions) based on observed surveys (e.g.
Fontanive et al., 2018, 2019; Vigan et al., 2020). We performed separately the
same analyses on the 27 stellar primaries, the 27 secondaries, and the
combined 54 binary components, in order to place constraints on the occurrence
rates of sub-stellar companions around primaries, secondaries and either
component of wide binary systems, respectively. In all cases, we fitted the
relative frequencies associated with each part of the model population and
derive the total fraction of sub-stellar companions as the sum of the two
parts, as was done in Vigan et al. (2020).

Based on the average completeness maps presented in Fig. 5, we chose to focus
on the region of parameter space ranging from 10–200 au in semi-major axis,
and from 10–75 MJup in mass. We used uniform priors between 0 and 1 for the
two companion fractions. In each simulation, the MCMC code was run with 2000
walkers taking 5000 steps each. We found that convergence of the chains was
reached after a couple hundred steps and discarded the initial 500 steps as
the ’burn-in’ phase. The results from the three analyses performed are
presented in Fig. 6, for the primaries (left), secondaries (centre) and all
stars (right). The posterior distributions of the companion fractions
associated with the GI and CA formation models are shown in red and blue,
respectively. The yellow curves are inferred from the sum of the GI and CA
frequencies at each step in the MCMC, and thus provide the total fraction of
sub-stellar companions between 10–200 au and 10–75 MJup.

We obtained upper limits for all cases. In general, roughly comparable limits
were placed on both parts of the model, since the lack of detected companions
does not allow us to scale the relative fractions between the GI and CA
populations. The posteriors of the CA part (blue) are slightly wider due to
the lower completeness levels achieved in the regions of the parameter space
where most CA planets are predicted. This effect is emphasised for the
secondaries (middle panel) around which the completeness reached is strongly
decreased at the lowest masses and separations. The much lower completeness
fraction reached around the secondaries in the probed parameter space (see
Fig. 5) is also responsible for the overall looser constraints derived for
this subset. The shaded yellow areas in Fig. 6 indicate the 68% confidence
intervals for the full sub-stellar companion occurrences rates. The resulting
values (1-$\sigma$ level) are: $<$13.7% for the frequency of sub-stellar
companions around primaries in visual binaries, $<$26.5% for the fraction of
sub-stellar companions around secondaries in visual binaries, and an
occurrence rate of $<$9.0% for giant planets and brown dwarfs around either
component of visual binaries. Sample size is clearly a driving factor in the
obtained results, with the tightest constraints derived the full sample of 54
stars. Of course, these results are only valid under the assumption that the
GI and CA theories are the only possible formation channels for brown dwarfs
and giant planets in the explored mass and separation ranges.

##  7 Discussion

Vigan et al. (2020) recently presented the first statistical results from the
SPHERE GTO F150 (Desidera et al., submitted) single star sub-sample from the
SHINE (SpHere INfrared survey for Exoplanets, Chauvin et al., 2017) survey.
This sub-sample is based on the first half of SHINE targets observed excluding
bad condition observations and newly discovered stellar binaries. Our results
appear to be broadly consistent with the frequencies of sub-stellar companions
reported in Vigan et al. (2020) for single stars, although a direct comparison
between the VIBES and SHINE F150 sample is not trivial. Indeed, our targets
cover a wide range of spectral types, while the statistical analyses performed
in Vigan et al. (2020) considered three different spectral bins, with
different associated population models. The most relevant comparison to our
results is arguably the constraints they obtained for FGK stars using the same
GI and CA synthetic populations as in the present work, for which they derived
a companion fraction of $5.7^{+3.8}_{-2.8}$% between 1–75 MJup and 5–300 au.
This is in agreement with our obtained value of $<$9% for our full sample of
54 stars over the ranges 10–75 MJup and 10–200 au, noting that this fraction
would be enhanced over the wider parameter space probed in the SHINE survey.
Since our sample of secondary stars is somewhat biased towards lower stellar
masses, we also compare the results from our analysis on secondaries to the
occurrence rate measured by Vigan et al. (2020) for M dwarfs. Our loose
constraints of $<$26.5% is also consistent with the frequency of
$12.6^{+12.9}_{-7.1}$% and $4.7^{+2.0}_{-1.5}$% from the SHINE (Vigan et al.,
2020) and GPIES (5–80 MJup, 10–100 au, Nielsen et al., 2019) surveys
respectively. Although we note again that the two analyses are not directly
comparable and that slightly different companion mass and semi-major axis
ranges were explored.

Several studies have suggested that systems with giant planets on very tight
orbits may be more likely to host a wide stellar companion (Eggenberger et
al., 2004; Desidera & Barbieri, 2007; Mugrauer et al., 2007). In the Friends
of hot Jupiters campaign, Ngo et al. (2016) found that the occurrence of
50–2000 au binary companions is strongly increased when hot Jupiters are
present, and concluded that binarity plays a role in the formation or
evolution of such giant planets. More recently, Fontanive et al. (2019)
searched for stellar companions to stars hosting planets and brown dwarfs
companions with masses $>$7 MJup within 1 au and measured a further inflated
binary fraction of $\sim$80% on separations in the range 20–10,000 au. These
results confirmed the trends observed for lower-mass planets, and suggested
that the effects of stellar multiplicity on the presence of massive close-in
companions may be enhanced for higher-mass planets and brown dwarfs. The study
conducted by Fontanive et al. (2019) is of particular relevant for imaging
programmes as the masses of the inner companions probed in that survey (7–60
MJup) are comparable to the typical masses of sub-stellar companions
detectable with direct imaging. This mass range is indeed very similar to the
mass interval explored in the statistical analyses presented in Sect. 6 (10–75
MJup).

It has been proposed that these massive planets and brown dwarfs detected on
short periods originally formed at wider orbital separations (i.e. where the
directly-imaged population lies) and that their inward migration was induced
or facilitated by the gravitational influence of a stellar companion (Ngo et
al., 2016; Fontanive et al., 2019). Although the precise stages in the
system’s evolution at which these processes would take place are not clear, we
would expect in this case a number of giant planets and brown dwarf companions
to reside at large separations in extremely young binary star systems. This
makes targets like the ones probed in the VIBES survey potentially promising
samples for the direct detection of sub-stellar companions.

However, even if binarity is indeed required to obtain massive planets on hot
Jupiter orbits, such systems remain sparse and this scenario must hence be a
rather rare event (there are less than 40 known systems with masses $>$7 MJup
within 1 au despite the magnified sensitivity to bigger and more massive
companions with the transit and radial velocity detection methods; see
Fontanive et al., 2019). The low occurrence of such a mechanism could be due
to the migration processes involved being somewhat inefficient, or be a direct
consequence of low formation rates of wide giant planets in binaries.
Therefore, the positive correlation between the existence of massive short-
period planets and stellar multiplicity may not necessarily imply a higher
frequency of massive planets in binaries. The null detection from our survey
appears to confirm this idea, although larger sample sizes will be needed to
more robustly measure the frequency of distant sub-stellar companions among
visual binaries compared to single stars.

The SPOTS survey (Thalmann et al., 2014; Bonavita et al., 2016; Asensio-Torres
et al., 2018) which searched for companions in the $\sim 30$–300 au range
around tight binaries found upper limits on the frequency of circumbinary
planets (1–15 M${}_{\text{Jup}}$) and brown dwarfs (16–70 M${}_{\text{Jup}}$)
of 10 and 6% respectively, at a 95% confidence level. Contrary to VIBES, the
SPOTS survey sensitivity was only marginally impacted by the binary nature of
the targets yielding tighter upper limits but still no planet detection.
Circumbinary discs are thought not to be perturbed by the binary star beyond
two to four times the binary separation (Holman & Wiegert, 1999) so that the
formation history would be very different from the intra-binary planet
formation where the secondary star acts as an external perturber on the disc
and on the planet orbit (Ngo et al., 2016; Fontanive et al., 2019). The
results of the SPOTS survey even though probing a different type of binary
planet population is compatible with our results.

##  8 Conclusion

We presented the ’VIsual Binary Exoplanet survey with Sphere’ (VIBES). We
observed a total of 23 binaries and 4 triple systems with SPHERE in IRDIFS
mode. The main results of the survey are as follows.

  * •

We have searched for sub-stellar companions around 46 stars which were part of
a binary system, around 4 stars in triples which were orbiting another binary
star, and around 4 binaries in triples which were orbiting another star. No
sub-stellar companion was detected as all companion candidates were followed
up and turned out to be background objects.

  * •

We derived (1-$\sigma$ level) upper limits of $<$13.7% for the frequency of
sub-stellar companions around primaries in visual binaries, $<$26.5% for the
fraction of sub-stellar companions around secondaries in visual binaries, and
an occurrence rate of $<$9.0% for giant planets and brown dwarfs (10–75 MJup)
around either component of visual binaries, in the 10-200 au range.

  * •

We confirmed 20 binaries and 2 triple systems to be co-moving and thus
physically bound.

  * •

One new 0.95 M☉ star was detected in the previously known binary system HD
121336.

The upper limits we have derived for planets in binaries are compatible with
frequencies obtained in single stars direct imaging surveys such as SHINE
($12.6^{+12.9}_{-7.1}$%, Vigan et al., 2020) and GPIES ($4.7^{+2.0}_{-1.5}$%,
Nielsen et al., 2019). A direct comparison is however not possible given the
difference in parameter ranges probed by these three surveys.

Studies of planets in binaries has increased in recent years specially through
the search of stellar companions to transiting and RV planets, revealing the
short period planets population. To probe the wider orbit planets direct
imaging remains the primary technique, however current direct imaging
techniques have a limited performance in the search of planets within close
binaries. Dual star coronagraphs (Cady et al., 2011; Aleksanyan et al., 2017;
Kühn et al., 2018) and multi-star wavefront control techniques (Sirbu et al.,
2017) offer promising prospects to significantly increase planet sensitivity
in binaries. Nonetheless statistical completeness is limited by the varying
separation range which is set by the binary separation instead of the field of
view.

###### Acknowledgements.

We would like to thank Markus Janson for his input on the binaries he has
observed, Ken Rice and Christoph Mordasini for useful discussions on
population models, and the anonymous referee for the very constructive
comments which significantly improved the scientific quality of the article.
J.H. is supported by SNSF through the Ambizione grant #PZ00P2_180098. J.H,
N.E, S.Q, J.K acknowledge the financial support from the SNSF through the
National Centre for Competence in Research “PlanetS”. This research has made
use of the SIMBAD database, operated at CDS, Strasbourg, France, and of NASA’s
Astrophysics Data System. This research made use of Astropy, a community-
developed core Python package for Astronomy (Astropy Collaboration et al.,
2013), as well as numpy (Walt et al., 2011), and pandas (Mckinney, 2011).

Figure 1: Projected separation of the binaries in the observing sample. The
primary and secondary stars are represented by the yellow and blue dots
respectively. The green area represents the 10–200 au considered by the
statistical analysis. HR 8799 bcde are represented by vertical dotted lines to
illustrate the position of this wide giant planet system. Figure 2: Histogram
of age and distance of the VIBES sample, blue bars and green line
respectively. HD 217379 and HD 285281 (145 and 45 Myr respectively) have been
left out in the age histogram for better readability. Figure 3: All IRDIS H2
contrast curves for the primary and secondary stars, blue and grey
respectively, along with their median contrast curves in red and green
respectively. Figure 4: Binary HD 121336 AB along with the third newly
discovered component C. Figure 5: Average completeness of the survey
considering the primary stars only (left panel), secondaries only (centre) and
all stars together (right). The colourmap is the same in all panels and
indicates the average completeness level as a function of mass and semi-major
axis for each sub-sample. The black contours show the number of stars from
each subset around which companions of given masses and separations are
detectable, and are therefore out of 27 stars for the left and middle panels,
and out of 54 targets for the right panel. Figure 6: Posterior probability
density functions of the frequencies of sub-stellar companions with masses in
the range 10–75 MJup and semi-major axes between 10–200 au around the primary
stars (left), secondary stars (centre) and all binary components (right). Each
plot shows the posteriors obtained from the MCMC analysis for the relative
frequencies of the two parts of the model population (GI in red and CA in
blue), as well as for the total frequency for the full model (GI+CA in
yellow). The shaded yellow areas mark the intervals of 68% confidence derived
on the total frequencies of sub-stellar companions in the considered ranges.

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##  Appendix A Additional material

(a) HIP 1910 B (b) HD 22213 B

(c) HD 285281 B

(d) TYC 8083-45-5 B (e) HD 102026 B

(f) HD 104231 B

(g) HD 104897 B (h) HD 108568 B

(i) HD 112381 B

(j) HD 112381 C (k) HD 120178 B

(l) HD 121336 B

Figure 7: Astrometric plots of the binaries. Oranges crosses represent the
relative position of the companion where the origin is defined by the first
observation. The blue corkscrew line is the track a background object would
follow due to the primary star proper motion with the green crosses along that
track representing the positions it would have at the dates of observation.

(a) HD 127215 B (b) HD 128788 B

(c) HD 133954 B

(d) HD 138138 B (e) HD 138138 C

(f) HD 144175 B

(g) HD 144823 B (h) HD 145792 B

(i) HD 146331 B

(j) HD 146331 C (k) HD 147432 B

(l) HD 148562 B

Figure 8: continued.

(a) HD 148716 B (b) HD 165189 B

(c) HD 208233 B

(d) HD 217379 B

Figure 9: continued.

(a) HIP 1910 B (b) HD 22213 B

(c) HD 285281 B

(d) TYC 8083-45-5 B (e) HD 102026 B

(f) HD 104231 B

(g) HD 104897 B (h) HD 108568 B

(i) HD 112381 B

Figure 10: Position measurements of the companions from this work and
literature. The blue cross in the centre represents the primary star position
while the red dots show the companion position. The green arc represents the
movement a face-on circular orbit would have with the observed time baseline.
Error bars are too small to be visible in the plots.

(a) HD 112381 C (b) HD 120178 B

(c) HD 121336 B

(d) HD 127215 B (e) HD 128788 B

(f) HD 133954 B

(g) HD 138138 B (h) HD 138138 C

(i) HD 144175 B

Figure 11: continued.

(a) HD 144823 B (b) HD 145792 B

(c) HD 146331 B

(d) HD 146331 C (e) HD 147432 B

(f) HD 148562 B

(g) HD 148716 B (h) HD 165189 B

(i) HD 208233 B

(j) HD 217379 B

Figure 12: continued. Table 3: Gaia distance, coordinates, and spectral types, and probability of association or moving group (MG) for each target using the Banyan $\Sigma$ tool. ID | RA | DEC | SpT | Distance | Banyan $\Sigma$ | Banyan $\Sigma$ Prob.
---|---|---|---|---|---|---
(pc) | MG | (%)
HIP1910 | 00:24:08.9 | -62:11:04.3 | M0Ve | $44.23^{+1.09}_{-1.04}$ | THA | 58.0
HD22213 | 03:34:16.3 | -12:04:07.2 | G8V | $51.57^{+0.16}_{-0.16}$ | THA | 98.9
HD102026 | 11:44:09.7 | -53:44:54.3 | A1V | $219.07^{+2.26}_{-2.21}$ | FIELD | 99.9
HD104231 | 12:00:09.4 | -57:07:01.9 | F5V | $102.73^{+0.47}_{-0.47}$ | LCC | 99.9
HD104897 | 12:04:44.4 | -52:21:15.6 | F3V | $106.96^{+0.46}_{-0.45}$ | LCC | 97.7
HD108568 | 12:28:40.0 | -55:27:19.3 | G1IV | $120.44^{+0.56}_{-0.55}$ | LCC | 98.9
HD112381 | 12:56:58.2 | -54:35:14.4 | ApSi | $121.30^{+3.10}_{-2.95}$ | LCC | 97.6
HD120178 | 13:49:09.2 | -54:13:42.3 | F5V | $133.08^{+0.64}_{-0.63}$ | LCC / UCL | 42.1 / 49.5
HD121336 | 13:56:19.9 | -54:07:56.7 | A1V | $139.86^{+1.78}_{-1.74}$ | UCL | 63.7
HD127215 | 14:31:14.3 | -40:39:21.3 | A1V | $145.99^{+1.76}_{-1.72}$ | UCL | 99.6
HD128788 | 14:40:05.0 | -40:54:02.3 | A5V | $144.62^{+4.38}_{-4.13}$ | UCL | 99.8
HD133954 | 15:08:42.5 | -44:29:04.3 | A2/3III | $147.77^{+1.37}_{-1.34}$ | UCL | 99.8
HD138138 | 15:31:17.2 | -33:49:11.4 | A2/3V | $123.87^{+1.42}_{-1.39}$ | UCL | 98.7
HD144175 | 16:05:19.1 | -23:40:08.8 | B9V | $143.72^{+2.06}_{-2.00}$ | USCO | 99.9
HD144118 | 16:05:46.2 | -39:50:35.9 | A5V | $129.03^{+0.96}_{-0.95}$ | UCL | 99.5
HD144823 | 16:08:43.6 | -25:22:36.5 | F3V | $160.38^{+1.18}_{-1.16}$ | USCO | 97.1
HD145792 | 16:13:45.4 | -24:25:19.5 | B6IV | $143.68^{+2.76}_{-2.66}$ | USCO | 99.6
HD146331 | 16:16:50.6 | -25:51:46.7 | B9V | $156.03^{+1.65}_{-1.61}$ | USCO | 99.2
HD147432 | 16:22:51.7 | -23:07:07.5 | A1III/IV | $131.41^{+3.13}_{-2.99}$ | USCO | 98.9
HD148562 | 16:29:54.5 | -24:58:46.0 | A2V | $130.36^{+1.37}_{-1.35}$ | ROPH | 63.2
HD148716 | 16:31:11.0 | -29:59:52.4 | F3V | $118.15^{+1.09}_{-1.07}$ | UCL | 93.7
HD165189 | 18:06:49.8 | -43:25:30.8 | A6V | $44.56^{+0.32}_{-0.32}$ | BPMG | 94.0
HD199143 | 20:55:47.7 | -17:06:51.0 | F8V | $45.66^{+0.08}_{-0.08}$ | BPMG | 97.1
HD208233 | 21:57:51.4 | -68:12:50.1 | G9IV | $52.38^{+0.37}_{-0.37}$ | THA | 99.1
HD217379 | 23:00:27.9 | -26:18:42.7 | K6.5V | $30.54^{+0.05}_{-0.05}$ | ABD | 59.5
HD285281 | 04:00:31.0 | +19:35:20.9 | K1 | $135.34^{+1.19}_{-1.17}$ | TAU | 99.2
TYC8083-45-5 | 04:48:00.6 | -50:41:25.6 | K7Ve | $59.60^{+0.27}_{-0.27}$ | THA | 98.2
Table 4: Log of observations with the atmospheric conditions for each run.

HD ID | HIP ID | Date | Field | Exposure | Observing conditions on average
---|---|---|---|---|---
rotation | time, IRDIS1 | Airmass | Seeing | Coherence time
[∘] | [sec / min] |  | [′′] | [ms]
- | 1910 | 2015-10-13 | 12 |  $8\times 1\times 124=992$ / 16.5 | 1.43–1.54 | $1.18$ | $1.4$
- | 1910 | 2018-08-05 | 14 |  $8\times 26\times 8=1664$ / 27.7 | 1.26–1.27 | $0.97$ | $3.6$
22213 | - | 2016-12-06 | 42 |  $0.8\times 16\times 98=1312$ / 21.9 | 1.03–1.04 | $0.65$ | $6.5$
285281 | - | 2017-10-05 | 20 |  $0.8\times 18\times 72=1085$ / 18.1 | 1.40–1.42 | $1.07$ | $4.8$
285281 | - | 2017-12-08 | 19 |  $0.8\times 18\times 80=1206$ / 20.1 | 1.41–1.46 | $0.53$ | $7.1$
TYC8083-45-5 | - | 2016-10-21 | 21 |  $2\times 17\times 48=1632$ / 27.2 | 1.12–1.13 | $1.13$ | $2.7$
TYC8083-45-5 | - | 2017-10-09 | 21 |  $2\times 17\times 48=1632$ / 27.2 | 1.11–1.12 | $0.61$ | $5.2$
102026 | 57238 | 2016-02-12 | 22 |  $32\times 1\times 80=2560$ / 42.7 | 1.15–1.16 | $0.81$ | $4.4$
104231 | 58528 | 2016-03-18 | 20 |  $8\times 1\times 256=2048$ / 34.1 | 1.19–1.19 | $1.08$ | $2.6$
104231 | 58528 | 2018-04-30 | 14 |  $4\times 28\times 11=1232$ / 20.5 | 1.19–1.19 | $1.14$ | $5.6$
104897 | 58899 | 2016-02-11 | 23 |  $8\times 1\times 256=2048$ / 34.1 | 1.13–1.14 | $1.36$ | $2.7$
108568 | 60885 | 2016-02-11 | 20 |  $16\times 1\times 144=2304$ / 38.4 | 1.17–1.19 | $1.07$ | $3.3$
112381 | 63204 | 2016-02-02 | 21 |  $1\times 1\times 831=831$ / 13.9 | 1.16–1.16 | $1.73$ | $2.0$
120178 | 67428 | 2016-06-06 | 20 |  $8\times 16\times 17=2176$ / 36.3 | 1.15–1.15 | $0.97$ | $1.8$
120178 | 67428 | 2018-03-25 | 15 |  $32\times 27\times 2=1728$ / 28.8 | 1.15–1.16 | $0.85$ | $5.1$
121336 | 68080 | 2016-06-05 | 20 |  $16\times 8\times 16=2048$ / 34.1 | 1.15–1.15 | $0.76$ | $2.8$
127215 | 70998 | 2018-05-14 | 32 |  $16\times 8\times 16=2048$ / 34.1 | 1.04–1.05 | $0.95$ | $2.2$
128788 | 71708 | 2018-05-17 | 32 |  $4\times 15\times 32=1920$ / 32.0 | 1.04–1.05 | $0.31$ | $11.0$
133954 | 74104 | 2018-04-24 | 27 |  $16\times 9\times 16=2304$ / 38.4 | 1.06–1.07 | $0.48$ | $9.7$
138138 | 76001 | 2018-03-14 | 59 |  $8\times 16\times 32=4096$ / 68.3 | 1.01–1.02 | $1.07$ | $3.6$
144175 | 78809 | 2018-05-14 | 49 |  $4\times 30\times 32=3840$ / 64.0 | 1.00–1.01 | $1.00$ | $1.8$
144118 | 78853 | 2018-05-15 | 34 |  $4\times 30\times 16=1920$ / 32.0 | 1.04–1.04 | $0.67$ | $4.1$
144118 | 78853 | 2019-05-26 | 32 |  $8\times 15\times 16=1920$ / 32.0 | 1.04–1.04 | $1.09$ | $2.2$
144823 | 79097 | 2018-07-08 | 6 |  $8\times 15\times 16=1920$ / 32.0 | 1.02–1.03 | $0.50$ | $6.1$
145792 | 79530 | 2018-06-11 | 2 |  $4\times 13\times 32=1664$ / 27.7 | 1.01–1.02 | $1.11$ | $1.8$
146331 | 79771 | 2018-06-23 | 7 |  $2\times 15\times 48=1440$ / 24.0 | 1.02–1.03 | $0.70$ | $3.2$
147432 | 80238 | 2018-08-15 | 4 |  $1\times 52\times 32=1664$ / 27.7 | 1.02–1.04 | $0.66$ | $5.9$
148562 | 80799 | 2018-07-04 | 7 |  $2\times 15\times 48=1440$ / 24.0 | 1.01–1.02 | $1.55$ | $1.7$
148716 | 80896 | 2016-07-13 | 47 |  $4\times 15\times 32=1920$ / 32.0 | 1.01–1.02 | $0.77$ | $2.8$
165189 | - | 2016-04-30 | 28 |  $2\times 32\times 16=1024$ / 17.1 | 1.06–1.06 | $0.80$ | $4.0$
165189 | - | 2018-04-30 | 23 |  $4\times 26\times 12=1248$ / 20.8 | 1.06–1.06 | $0.53$ | $6.3$
199143 | 103311 | 2019-08-26 | 46 |  $2\times 15\times 48=1448$ / 24.0 | 1.01–1.02 | $0.95$ | $4.0$
208233 | 108422 | 2016-07-23 | 14 |  $16\times 9\times 16=2304$ / 38.4 | 1.38–1.38 | $0.67$ | $4.5$
217379 | 113597 | 2016-09-01 | 12 |  $2\times 16\times 48=1536$ / 25.6 | 1.01–1.02 | $0.66$ | $6.3$

Notes. (1) The exposure time of the IRDIS observation is given in a format
“DIT $\times$ NDIT $\times$ NEXP = Total exposure time in seconds / Total
exposure time in minutes”, where DIT is the detector integration time, NDIT is
a number of integrations per exposure, NEXP is a number of exposures (files).
The total exposure time of the IFS data is either the same as for the IRDIS
data or slightly shorter depending on target.

3]

Table 5: Target sample along with astrometric measurements from literature and
from this work. The subscript A always stands for the primary star while B
stands for either the secondary or tertiary component. Uncertainties reported
in Table 1.

HD | Pair | $M_{I}$ | $M_{II}$ | $K_{I}$ | $K_{II}$ | $H_{I}$ | $H_{II}$ | Sep | PA | Date | Ref.
---|---|---|---|---|---|---|---|---|---|---|---
|  | $M_{\sun}$ | $M_{II}$ | (mag) | (mag) | (mag) | (mag) | ($\arcsec$) | (deg) |  |
HD102026 | AB | $1.47$ | $0.14$ | – | – | – | – | $1.18$ | $264.40$ | 2011-03-21 | 10
| AB | – | – | $8.33$ | – | $8.35$ | $12.62$ | $1.17$ | $266.00$ | 2016-02-12 | 1
HD104231 | AB | $1.33$ | $0.30$ | – | – | – | – | $4.46$ | $161.30$ | 2011-03-21 | 10
| AB | – | – | $7.42$ | – | $7.48$ | $9.90$ | $4.44$ | $163.07$ | 2016-03-18 | 1
| AB | – | – | $7.42$ | – | $7.48$ | $10.26$ | $4.45$ | $162.91$ | 2018-04-30 | 1
HD104897 | AB | $1.49$ | $0.44$ | $7.35$ | – | – | – | $4.22$ | $258.00$ | 2011-03-21 | 10
| AB | – | – | – | – | $7.42$ | $10.49$ | $4.21$ | $259.56$ | 2016-02-11 | 1
HD108568 | AB | $2.47$ | $0.24$ | $7.29$ | – | – | – | $0.89$ | $317.30$ | 2011-05-03 | 10
| AB | – | – | – | – | $7.41$ | $11.11$ | $0.89$ | $318.84$ | 2016-02-11 | 1
HD112381 | AB | – | – | $6.47$ | $7.36$ | – | – | $1.80$ | $46.90$ | 2001-12-31 | 11
| AB | – | – | $6.09$ | – | $6.22$ | $7.27$ | $2.23$ | $52.08$ | 2016-02-02 | 1
| AC | $2.05$ | $1.06$ | $6.78$ | $8.40$ | – | – | $0.15$ | $237.00$ | 2004-04-07 | 12
| AC | – | – | – | – | $6.22$ | $7.81$ | $0.13$ | $331.62$ | 2016-02-02 | 1
HD120178 | AB | $1.44$ | $0.11$ | $7.63$ | – | – | – | $3.56$ | $327.00$ | 2012-04-07 | 10
| AB | – | – | – | – | $7.71$ | $12.10$ | $3.54$ | $328.69$ | 2016-06-06 | 1
| AB | – | – | – | – | $7.71$ | $11.74$ | $3.54$ | $328.76$ | 2018-03-25 | 1
HD121336 | AB | $2.91$ | $1.92$ | $6.28$ | $7.19$ | – | – | $1.92$ | $10.20$ | 2001-06-05 | 12
| AB | – | – | – | – | $6.00$ | $7.00$ | $1.99$ | $14.71$ | 2016-06-05 | 1
| AC | – | – | $5.94$ | – | $6.00$ | $9.31$ | $1.65$ | $12.56$ | 2016-06-05 | 1
HD127215 | AB | $2.54$ | $0.48$ | $7.06$ | $10.83$ | – | – | $1.17$ | $355.00$ | 2001-06-06 | 12
| AB | – | – | $7.05$ | – | $7.09$ | $11.08$ | $1.18$ | $355.69$ | 2018-05-14 | 1
HD128788 | AB | $1.45$ | $0.44$ | $7.80$ | – | – | – | $3.45$ | $73.00$ | 2011-06-11 | 10
| AB | – | – | – | – | – | – | $3.47$ | $74.31$ | 2018-05-17 | 1
HD133954 | AB | $2.45$ | $0.49$ | $7.67$ | – | – | – | $1.85$ | $210.70$ | 2011-04-26 | 10
| AB | – | – | – | – | $7.70$ | $10.52$ | $1.81$ | $212.60$ | 2018-04-24 | 1
HD138138 | AB | – | – | – | – | – | – | $0.08$ | $7.60$ | 1992-06-15 | 7
| AB | – | – | – | – | – | – | $0.09$ | $6.40$ | 1993-02-05 | 7
| AB | $1.54$ | $1.36$ | $7.60$ | $7.80$ | – | – | $0.25$ | $3.20$ | 2001-06-08 | 12
| AB | – | – | $6.43$ | – | $6.49$ | $6.72$ | $0.41$ | $2.33$ | 2018-03-14 | 1
| AC | – | – | – | – | – | – | $1.55$ | $131.20$ | 1989-04-22 | 14
| AC | – | – | – | – | – | – | $1.54$ | $130.00$ | 1991 | 15
| AC | $1.54$ | $1.36$ | $7.60$ | $8.20$ | – | – | $1.48$ | $124.80$ | 2001-06-08 | 12
| AC | – | – | – | – | $6.49$ | $7.20$ | $1.43$ | $119.37$ | 2018-03-14 | 1
HD144118 | AB | $1.82$ | $1.14$ | $7.50$ | $8.45$ | – | – | $1.99$ | $270.39$ | 2001-06-08 | 12
| AB | – | – | – | – | $7.15$ | $8.29$ | $2.01$ | $271.01$ | 2018-05-15 | 1
HD144175 | AB | $2.03$ | $0.30$ | $7.51$ | $10.26$ | – | – | $1.18$ | $25.67$ | 2001-06-07 | 12
| AB | – | – | $7.35$ | – | $7.37$ | $10.28$ | $1.13$ | $25.76$ | 2018-05-14 | 1
HD144823 | AB | $1.99$ | $0.75$ | $7.25$ | – | – | – | $0.81$ | $340.00$ | 2011-05-31 | 10
| AB | – | – | – | – | $7.33$ | $10.44$ | $0.81$ | $342.23$ | 2018-07-08 | 1
HD145792 | AB | $3.73$ | $1.58$ | $6.60$ | $8.34$ | – | – | $1.69$ | $219.66$ | 2000-05-31 | 12
| AB | – | – | $7.11$ | – | $6.12$ | $8.16$ | $1.73$ | $219.16$ | 2018-06-11 | 1
HD146331 | AB | $2.14$ | $0.19$ | $7.10$ | $11.42$ | – | – | $0.44$ | $128.59$ | 2004-06-19 | 12
| AB | – | – | $7.10$ | – | $7.19$ | $11.64$ | $0.43$ | $130.54$ | 2018-06-23 | 1
| AC | $2.14$ | $0.19$ | $7.10$ | $10.89$ | – | – | $3.67$ | $313.38$ | 2004-06-19 | 12
| AC | – | – | – | – | $7.19$ | $11.12$ | $3.65$ | $314.85$ | 2018-06-23 | 1
HD147432 | AB | – | – | – | – | – | – | $1.05$ | $318.30$ | 1991 | 5
| AB | $1.94$ | $1.67$ | $7.34$ | $7.49$ | – | – | $1.03$ | $318.46$ | 2001-06-07 | 12
| AB | – | – | $6.62$ | – | $6.69$ | $6.91$ | $1.01$ | $320.15$ | 2018-08-15 | 1
HD148562 | AB | $1.86$ | $0.34$ | $7.45$ | $9.80$ | – | – | $2.94$ | $205.02$ | 2004-05-05 | 12
| AB | – | – | $7.31$ | – | $7.41$ | $10.92$ | $2.93$ | $206.40$ | 2018-07-04 | 1
HD148716 | AB | $1.81$ | $0.24$ | $7.44$ | $10.33$ | – | – | $2.28$ | $177.23$ | 2004-06-08 | 12
| AB | – | – | $7.45$ | – | $7.55$ | $11.28$ | $2.28$ | $177.89$ | 2016-07-13 | 1
HD165189 | AB | – | – | $4.40$ | – | – | – | $1.80$ | $249.00$ | 1874 | 8
| AB | – | – | – | – | – | – | $1.71$ | $187.91$ | 2015-06-24 | 6
| AB | – | – | – | – | – | – | $1.79$ | $181.89$ | 2018-04-30 | 1
HD199143 | AB | $1.19$ | $0.35$ | $8.12$ | – | – | – | $1.08$ | $325.00$ | 2001-05-31 | 9
| AB | – | – | – | – | $5.95$ | $8.19$ | $0.84$ | $321.71$ | 2019-08-26 | 1
HD208233 | AB | $1.03$ | $0.28$ | $6.83$ | $9.78$ | – | – | $1.98$ | $192.10$ | 2001-10-28 | 2
| AB | – | – | $6.83$ | – | $6.86$ | $9.82$ | $1.62$ | $193.94$ | 2016-07-23 | 1
HD217379 | AB | – | – | $6.75$ | $7.38$ | – | – | $2.24$ | $239.02$ | 2012-07-11 | 4
| AB | – | – | – | – | $6.45$ | $7.31$ | $2.29$ | $240.64$ | 2016-09-01 | 1
HD22213 | AB | $0.94$ | $0.54$ | $6.97$ | $5.10$ | – | – | $1.67$ | $281.10$ | 2012-07-11 | 4
| AB | – | – | – | – | $6.95$ | $8.84$ | $1.69$ | $281.12$ | 2016-12-06 | 1
HD285281 | AB | – | – | $7.61$ | – | – | – | $0.77$ | $190.70$ | 1994-12-12 | 13
| AB | $1.11$ | $0.71$ | $7.61$ | – | – | – | $0.78$ | $186.00$ | 2011-10-17 | 3
| AB | – | – | – | – | $7.75$ | $8.99$ | $0.76$ | $186.22$ | 2017-10-05 | 1
| AB | – | – | – | – | $7.75$ | $9.45$ | $0.76$ | $186.15$ | 2017-12-08 | 1
HIP1910 | AB | $0.71$ | $0.30$ | $7.69$ | $9.22$ | – | – | $0.70$ | $46.90$ | 2000-11-12 | 2
| AB | – | – | $7.69$ | – | – | – | $0.70$ | $50.20$ | 2001-10-28 | 2
| AB | – | – | $7.69$ | – | $7.71$ | $9.37$ | $0.52$ | $81.98$ | 2015-10-12 | 1
| AB | – | – | $7.69$ | – | $7.71$ | $9.32$ | $0.48$ | $89.96$ | 2018-08-05 | 1
TYC8083-45-5 | AB | $0.70$ | $0.35$ | $8.25$ | $7.10$ | – | – | $1.06$ | $347.80$ | 2011-11-08 | 4
| AB | – | – | – | – | $8.08$ | $9.24$ | $1.05$ | $346.58$ | 2016-10-21 | 1

11footnotetext:  References: (1) this work; (2 )Chauvin et al. (2003); (3)
Daemgen et al. (2015); (4) Elliott et al. (2015); (5) Fabricius et al. (2002);
(6) Gaia Collaboration et al. (2018); (7) Hartkopf et al. (1996); (8) Herschel
et al. (1874); (9) Janson et al. (2013); (10) Kouwenhoven et al. (2005) (11)
Kouwenhoven (2006); (12) Kohler & Leinert (1998); (13) McAlister et al.
(1990); (14) Tokovinin (1997)

Table 5: continued.