# VLT/SPHERE Multi-Wavelength High-Contrast Imaging of the HD 115600 Debris
Disk: New Constraints on the Dust Geometry and the Presence of Young Giant
Planets

Aidan Gibbs  Steward Observatory, The University of Arizona, 933 N. Cherry
Avenue, Tucson, AZ 85721, USA  Kevin Wagner  Steward Observatory, The
University of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85721, USA  NSF
Graduate Research Fellow  NASA Nexus For Exoplanetary System Science: Earths
in Other Solar Systems Team  Daniel Apai  Steward Observatory, The University
of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85721, USA  NASA Nexus For
Exoplanetary System Science: Earths in Other Solar Systems Team  Lunar and
Planetary Laboratory, The University of Arizona, 1640 E. University Boulevard,
Tucson, AZ 85718, USA  Attila Moór  Konkoly Observatory, MTA CSFK, Konkoly-
Thege Miklós út 15-17, H-1121 Budapest, Hungary  Thayne Currie  Subaru
Telescope, National Astronomical Observatory of Japan, 650 North
A‘oh$\bar{o}$k$\bar{u}$ Place, Hilo, HI 96720, USA  NASA-Ames Research Center,
Moffett Field, CA, USA  Eureka Scientific, 2452 Delmer Street Suite 100,
Oakland, CA, USA  Mickäel Bonnefoy  Max Planck Institute for Astronomy  Maud
Langlois  Centre de Recherche Astrophysique de Lyon (CRAL)  Aix Marseille
Université, CNRS, LAM (Laboratoire d ’Astrophysique de Marseille)  Carey Lisse
Johns Hopkins University Applied Physics Laboratory

###### Abstract

Young and dynamically active planetary systems can form disks of debris that
are easier to image than the planets themselves. The morphology and evolution
of these disks can help to infer the properties of the putative planets
responsible for generating and shaping the debris structures. We present
integral field spectroscopy and dual-band imaging from VLT/SPHERE ($1.0-1.7\mu
m$) of the debris disk around the young F2V/F3V star HD 115600. We aim to 1)
characterize the geometry and composition of the debris ring, 2) search for
thermal emission of young giant planets, and 3) in the absence of detected
planets, to refine the inferred properties of plausible planets around HD
115600 to prepare future attempts to detect them. Using a different dust
scattering model (ZODIPIC) than in the discovery paper (Henyey-Greenstein) to
model the disk geometry, we find $a_{0}=46\pm 2$ au for the disk’s central
radius and offsets $\Delta\alpha$, $\Delta\delta$ = $-1.0\pm 0.5,0.5\pm 0.5$
au. This offset is smaller than previously found, suggesting that unseen
planets of lower masses could be sculpting the disk. Spectroscopy of the disk
in Y-J bands with SPHERE shows reddish color, which becomes neutral or
slightly blue in H band seen with GPI, broadly consistent with a mixed bulk
disk composition of processed organics and water ice. While our observed field
contains numerous background objects at wide separations, no exoplanet has
been directly observed to a mass sensitivity limit of $2-3(5-7)M_{\rm J}$
between a projected separation of 40 and 200 au for hot (cold)-start models.

††software: IDL

##  1 Introduction

Debris disks provide important clues to the late stages of planet formation
(e.g., Jang-Condell et al. 2015; Apai et al. 2015). Second-generation dust
disks can be produced by young, active planetary systems in which collisional
cascades generate large amounts of dust, observable in infrared and longer
wavelengths (Wyatt, 2008; Gáspár et al., 2012). Surveys have also shown that
giant planets are more frequently found at large separations around stars with
debris disks (Meshkat et al., 2017). Planets can interact with the disk to
produce large-scale structures (rings, warps, spirals, etc.) that are often
easier to observe than the planets themselves (e.g., Su et al. 2013; Matthews
et al. 2017; Currie et al. 2017). Thus, stars with debris disks are not only
good places to look for exoplanets, but disk structures can also be used to
predict and constrain unseen planets for possible future detections and
studies.

The Scorpius-Centaurus OB association is an optimal target for disk studies,
due to its proximity ($\sim$100-200 pc) and large collection of young systems.
A high fraction of early-type Sco-Cen stars show strong infrared excess
indicative of copious circumstellar dust (Carpenter et al., 2009; Chen, 2014;
Jang-Condell et al., 2015; Pecaut & Mamajek, 2016). Sco-Cen includes many of
the currently known directly-imaged planetary companions (e.g. Rameau et al.
2013; Quanz et al. 2013; Bailey et al. 2014; Currie et al. 2015a; Chauvin et
al. 2017; Keppler et al. 2018) and numerous debris disks resolved in scattered
light (e.g Thalmann et al., 2013; Currie et al., 2015b; Lagrange et al., 2016;
Matthews et al., 2017; Bonnefoy et al., 2017).

HD 115600 (HIP 64995) is an F2V/F3V star located in the Lower Centaurus Crux
region of the Sco-Cen OB Association whose debris disk could provide key
insights into the planet formation. It has a measured distance of $109.6\pm
0.5$ pc (Gaia Collaboration et al., 2018) and is estimated to be $\sim$15 Myr
old (Pecaut et al., 2012; Pecaut & Mamajek, 2016). The system was first
observed to have an infrared excess by Spitzer/MIPS, with a fractional
luminosity $L_{IR}/L_{*}=1.7\times 10^{-3}$ and dust mass 0.03 $M_{moon}$
(Chen et al., 2011), consistent with an extremely luminous dusty debris disk.
Using the Gemini Planet Imager, Currie et al. (2015b) obtained the first
images of the HD 115600 debris disk, resolving it into a bright ring-like
structure. Given the system’s age, the primary’s mass and birth environment,
and the disk’s stellocentric distance, they hypothesized that the disk may be
a good analogue for the early Kuiper belt. Its H-band spectrum is at least
broadly consistent with predictions for a water ice composition, although
other compositions could not be ruled out. By fitting an ellipse to the debris
ring, they inferred a semi-major axis of $48\pm 1.1$ au, an eccentricity of
0.1-0.2, and a disk offset of $\Delta\alpha,\Delta\delta=0".018\pm
0".008,0".029\pm 0".014$ ($2.0\pm 0.9,3.2\pm 1.5$ au from the central star).
To explore additional disk properties, Currie et al. (2015b) created scattered
light disk models using a Henyey-Greenstein phase function and forward-modeled
them through their reduction pipeline, finding neutral scattering was the best
fit. Furthermore, they found that the debris disk is very thick, with a width-
to-mean radius ratio of $\Delta r/r_{0}\sim 0.37$.

The sharp ring-like structure of the disk may suggest sculpting by a planet
(or planets) and the measured offset of the disk further implies that this
planet could be massive. Numerical modeling by Thilliez & Maddison (2017)
determined that disk structures driven by a $\sim 8M_{J}$ planet located at
$\sim$30 au are the best fit to observed morphology. While the predicted
contrast ($4\times 10^{-5}$ in H band) of this hypothetical planet is within
the theoretical performance capabilities of current extreme adaptive optics
(hereafter extreme AO) instruments such as SPHERE and GPI (Mesa et al., 2015;
Bailey et al., 2016), the orientation of the disk (i = $79^{\circ}$) makes the
detection very challenging.

By analyzing new multi-wavelength extreme AO observations of HD 115600’s
debris disk with updated models, we can better constrain the disk’s geometry,
provide new constrains on the system’s inventory of massive planets possibly
sculpting the disk, and provide new insights into the disk’s composition.
Numerous debris disk studies including Currie et al. (2015b) interpret
scattered light images using the standard, simple Henyey-Greenstein (H-G)
scattering phase function. In addition to noting that H-G functions are ad
hoc, recent results further challenge their accuracy (Milli et al., 2017;
Hughes et al., 2018; Goebel et al., 2018). Empirically derived scattering
functions of debris disks more closely resemble a combination of Henyey-
Greenstein functions, similar to the phase function of zodiacal light (Graham
et al., 2007; Ahmic et al., 2009). Disk modeling with these functions, rather
than H-G, may lead to a revision in disk properties. Additionally,
spectroscopy covering a wider wavelength range may clarify whether HD 115600’s
disk composition shows evidence for a single constituent or has mixed dust
composition.

In this study, we present new, multi-wavelength images and spectra of the
debris disk around HD 115600 taken with VLT/SPHERE. The processed data are
presented and described in Section 2. These images provide a more detailed
look at the disk structure as well as expanded wavelength coverage in the $Y-$
and $J$-bands, and the results are discussed in Section 3. To better constrain
the disk structure, we model the disk as an optically thin scattering ring
using a scattering function derived from zodiacal light (Hong, 1985), and
present our model in Section 4, with a discussion in Section 5. In Section 6,
we examine the new YJ spectra in combination with the H-band data from Currie
et al. (2015b). Finally, we provide new limits on the mass and orbit of any
unseen exoplanets in Section 7, and conclude with a summary of our findings in
Section 8.

##  2 Observations and Data Reduction

HD 115600 was observed on 2015 June 04 as part of ESO Program 095.C-0298,
using the Spectro-Polarimetric High-contrast Exoplanet Research (VLT-SPHERE:
Beuzit et al. 2008) instrument. The observations were conducted with the
IRDIFS mode which allows operation of the infrared dual-band imager and
spectrograph (IRDIS: Dohlen et al. 2008; Vigan et al. 2010) with the H2 and H3
filters (central wavelengths $1.576\pm 0.052$ and $1.667\pm 0.054$ $\mu m$
respectively), simultaneously with the integral field spectrograph (IFS:
Claudi et al. 2008; Mesa et al. 2015) in YJ bands ( $0.95-1.35\mu m$) with a
spectral resolution of $R\sim 50$. SPHERE IFS has a narrow field-of-view of
$\sim 1$ arcsecond in radius, with a plate scale of 7.46 mas/pixel, roughly
0.8 au per pixel at the distance of HD115600. IRDIS has a wide field-of-view
of $11"\times 12.5"$, with a lower resolution plate scale of 12.25 mas/pixel
(Claudi et al., 2008; Dohlen et al., 2008; Vigan et al., 2010).

During the observations, the SAXO extreme adaptive optics system (Fusco et
al., 2006) corrected for wavefront errors, and the apodized Lyot coronograph,
which has an occulting mask with radius of 0$\farcs$0925, attenuated the
stellar light contribution. Before the observations, a short star-centering
sequence was obtained by applying a sinusoidal pattern to the deformable
mirror (Langlois et al., 2013). This deformation creates 4 satellite spots in
a diagonal cross pattern around the star that can be used to precisely locate
the star center behind the coronagraph. Using this method, the uncertainty in
the star center is 0.25 pixels in both the x and y directions (Mesa et al.,
2015). Flux calibrations were obtained before the observation by slewing the
star off of the coronagraph while inserting a neutral density filter to avoid
saturation. SPHERE obtained 64 images for IFS and and 64 for IRDIS with
exposure times of 64 seconds each, for a total integrated exposure time of 68
minutes for each of the two instruments. The instrument field derotator was
switched off for the observations to allow angular differential imaging in
post-processing, and the field rotation during the observation sequence was
$\sim 27^{\circ}$. The key parameters of the observation are summarized in
Table 1.

The basic reduction steps for IFS data, including dark current subtraction,
flat field division, microspectra extraction, and wavelength calibration, were
performed using a combination of the ESO SPHERE pipeline (version 0.15.0,
Pavlov et al. 2008) and the custom IDL tools described in Vigan et al. 2015.
These tools add some additional pre-processing steps beyond those of the
pipeline, such as bad pixel correction using the IDL procedure MASKINTERP, and
cross-talk correction (spurious light contamination between integral field
unit lenslets). Small scale cross-talk is corrected using a $41\times 41$
kernel with a Moffatt function to reduce the presence of doubles to the
aforementioned satellite spots, which are a result of cross-talk. To reduce
the stellar halo and speckle noise which remains after extreme AO
coronagraphy, we utilized both angular differential imaging (ADI; Marois et
al. 2006) and spectral differential imaging (SDI; Racine et al. 1999)
techniques. These methods utilize varying field rotation and wavelength
respectively to separate astrophysical objects from the speckle noise. We used
our own principal component analysis (PCA) based Karhunen Loéve image
projection (KLIP; Soummer et al. 2012) algorithms of ADI and SDI, which are
described in Hanson & Apai (2015) and Apai et al. (2016). We used annular PCA,
with 15 annuli, 14 pixels in width each, and six angular segments. We retained
7 KL basis vectors for ADI and SDI, with a minimum field rotation of
$0.5\times FWHM$ for ADI, and a minimum speckle radial movement of $1.5\times
FWHM$ for SDI between images. Before the PSF subtraction, we applied a
sharpening high-pass filter with a width of 11 pixels to the data to reduce
low-frequency noise. The reduced images were stored in x-y-$\lambda$
(Cartesian and wavelength) cubes, as well as band-averaged flux density images
in the Y- and J- bands, which greatly improves signal to noise (S/N) compared
to the detection in the individual IFS channels. The first seven frames of the
cube were not included in the band-averaged images to eliminate frames
impacted by detector persistence from the flux calibration frames. All images
for a respective wavelength were combined with a noise-weighted mean (Bottom
et al., 2017). Basic reduction steps for IRDIS data were similarly performed
using custom IDL tools for bad pixel correction, dark current subtraction,
flat fielding, and star centering. We again used classical and KLIP ADI and
SDI techniques for PSF subtraction.

Table 1: HD 115600 SPHERE Observations Date | Instrument | Filter | Total Exposure Time | Sub-Int. Time | #Int. | Average Seeing | Average Airmass |
---|---|---|---|---|---|---|---|---
June 4 2015 | IFS | YJ (0.96 - 1.34 $\mu m$) |  $\sim 68$ min | 64.0 s | 64 | 1.1" | 1.22 |
June 4 2015 | IRDIS | H23 (1.593 - 1.667 $\mu m$) |  $\sim 68$ min | 64.0 s | 64 | 1.1" | 1.22 |

##  3 Results

We detect the disk in the band-integrated flux density IFS images, shown in
Figure 1, with a radial extent from r$\sim$0."35 to 0."48, corresponding to a
projected distance of $\sim$ 39 to 53 au. The disk is also visible in IRDIS
images, presented in Figure 2. To estimate the signal-to-noise ratio of the
disk, we calculate the noise as the standard deviation of the band-integrated
image in circular apertures with widths corresponding to the diffraction
limited, full-width half maximum (FWHM, $\sim 9$ pixels), which do not overlap
with the regions where the disk is visible, but are centered at similar radii
to disk ansae ($\sim$55 pixels from image center). The signal is defined as
the average pixel value of two circular regions of FWHM radius centered on the
north and south disk ansae where signal is highest (center pixels 115x, 190y
and 165x, 90y). By this metric, with SPHERE-IFS we achieve a peak SNR$\sim$20
in the disk ansae, decreasing in disk regions with smaller separations. Currie
et al. (2015b) report a peak SNR of $\sim$ 8 in GPI data, where they do not
mask the disk or self-subtraction footprints in determining the noise. Using
the same approach for calculating noise, the disk’s SNR in the SPHERE data is
slightly higher ($\sim$ 10%) than in GPI data.

To look for any additional structure that may be present outside of the disk,
we also performed PSF subtractions without high-pass filtering, which can
suppress extended and faint features. We did not find evidence for any new
disk features. Within a projected distance of $\sim 25$ au from the star,
which is masked in Figures 1 and 2, residual speckle noise dominates the image
and we are not able to probe disk structure reliably.

Multi-wavelength coverage is used to discriminate between speckle noise, which
scales with wavelength, and point-source detections, which do not. We did not
detect any point-sources in the vicinity of the disk within 1" of the star.
Point-sources detected in the IRDIS field radially closer than the point
labeled 4, shown in Figure 2, exhibit motion visually consistent with
background stars due to the proper motion of HD 115600 between GPI and SPHERE
image epochs. Multiple epoch data was not attainable for point-sources
radially further out than point 4. Appendix A includes the properties for all
detected sources in the SPHERE and GPI fields. In Section 7, we will quantify
and discuss the contrast and mass-sensitivity limits these observations place
on the potential presence of planets in the system based on hot and cold start
core-accretion models of planet formation and evolution.

Figure 1: Band-averaged (0.96 - 1.34 $\mu m$), IFS images of the HD 115600
disk. The raw image is presented in the top left. ADI and SDI reductions are
presented in the other 3 panels with high-pass filtering. The disk is clearly
visible with ADI and SDI techniques. The image center is marked by a cross in
each image, and noise from the central star is masked to $\sim 25$ au. Figure
2: IRDIS dual-band (H23, 1.587 and 1.667 $\mu m$) images of the HD 115600
disk. Left: Signal-to-nose map of the full field-of-view (11 arcseconds) with
detected point-sources labeled. The point labeled 1 is a disk feature. Center:
KLIP ADI reductions with high-pass filtering for H2 (top) and H3 (bottom) in a
region of 2.5" around the disk. Right: Classical ADI reduction with no high-
pass filtering for the same region in both wavelengths. Noise from the central
star is masked in inset images to $\sim 25$ au with the star center marked by
a cross. Note that the brightness scaling changes between left, center, and
right.

##  4 Disk Models

Our aim with disk modeling is to identify the simplest physically viable model
of the disk, accounting for projection and light-scattering effects. By
understanding the fundamental geometry, we can later attempt to make
predictions about disk sculpting by possible exoplanets. We perform the disk
modeling by two independent methods, ellipse fitting and with the model
ZODIPIC, in order to asses the accuracy of both. We make no attempt to model
the disk at the wavelengths of IRDIS, given the benefits of the IFS data such
as extended wavelength coverage compared to previous studies and the smaller
inner working angle.

We performed primary modeling of the data using the model ZODIPIC (Kuchner,
2012), originally written to model exozodiacal dust, but which is generally
applicable to optically thin debris disks. ZODIPIC is based on the radiative
model of zodiacal dust in Kelsall et al. (1998), derived from all-sky data
collected by the Cosmic Background Experiment/Diffuse Infrared Background
Experiment (COBE/DIRBE). This model was chosen not only for ease-of-use, but
because the scattering phase function for zodiacal light (Hong, 1985)
contained in ZODIPIC resembles those empirically derived from debris disks
(Graham et al., 2007; Ahmic et al., 2009). This setup allows us to control the
disk size, inclination, offset, and scattering phase function, among other
parameters. More complex radiative transfer models could be used, but were not
warranted by the data quality and the goals of our study.

The general procedure we used to find a best-fit disk model with ZODIPIC was
to produce an evenly spaced grid of disk models with varying geometric
parameters, and to subtract them from the raw data to find the model that
minimizes the residual disk signal in our processed images without considering
noise. The residual was calculated for every model in the grid to avoid local
minimums and find the absolute lowest value. ZODIPIC parameters related to the
star and instrument performance were kept constant through all models. These
include the stellar type, distance ($\sim 109.6\pm 0.5pc$), and pixel-scale
for SPHERE IFS. ZODIPIC also required the observation wavelength as a
parameter, therefore we produced a separate model in each IFS wavelength
channel for every geometry. The phase function parameter we used is the
standard function included in ZODIPIC from Hong (1985) for wavelengths below
4.2 microns, which covered scattering between 30 and 180 degrees. We do not
alter or explore the scattering phase function in the interest of keeping a
manageable grid size. The geometric parameters varied within our grid search
were position angle, inclination, disk radius, and projected location of the
disk’s center. We set the inner and outer disk radius to the approximate
visual size, 39 to 53 au, and did not vary these parameters, first to reduce
the grid dimensions, and also because we found their variance produced
negligible effects in the subtraction residuals. The grid was centered on the
best-fit parameters determined by Currie et al. (2015b) using ellipse fitting,
which we also performed as a secondary analysis. Table 2 contains our range
and step size for the varied parameters.

To subtract a geometric model from our data, we first convolved the model with
the measured SPHERE PSF. The width of this PSF was measured from the flux
calibration data cube in each wavelength channel. The convolved model was then
multiplied by a brightness scale factor, which was initially the same for
every wavelength, to bring the brightness of the model to the same approximate
brightness as the disk. We subtracted the convolved model from our raw data,
and processed it with classical ADI. Classical ADI was chosen to cover a
larger parameter space since PCA-based KLIP routines are more computationally
expensive. After processing, the disk residuals were measured in the band-
averaged images in two circular apertures of 40 pixel ($\sim 33$ au) diameter
centered on each disk ansa. These apertures were chosen to avoid strong
speckle noise near the image center, but to be large enough to accommodate the
range of disk sizes and angles in our grid. The geometry that produced the
least-squared residual was adopted as our best-fit. The error is estimated as
twice the step size, that is twice the smallest difference between parameter
values in our grid, for all grid parameters. The uncertainty in the star
centering ($0.2$ au in x and y) is also considered, but is a minor
contribution compared to the step size.

Our final step was to find the optimal brightness of the geometric model in
each wavelength channel to further minimize the disk residual after
subtraction. We subtracted the geometric model in each channel at varying
brightnesses similarly to before, but measured the disk residual in the
corresponding channel of the processed data cube instead of the band-averaged
image. The brightness that minimizes the residual in a channel is adopted as
the best-fit for that channel. The error again was estimated as twice the
final step size, the final difference between neighboring brightness values we
tried, which is 1/16 of the best-fit brightness value.

Figure 3 shows a KLIP SDI reduction of the disk in IFS, the best-fit model
disk produced by ZODIPIC, and the KLIP SDI reduction of the raw data with the
best-fit model subtracted. KLIP SDI was chosen to display the before and after
disk model subtraction as it has the least speckle noise, even though SDI
processing was not used for the actual disk modeling. The best-fit disk model
was only processed with SDI for display. With ZODIPIC, we found the best-fit
model has a position angle and inclination of $P.A.=27.0\degree\pm 1.0$ and
$i=80.0\degree\pm 1.0$. Our model estimated the disk radius at $46.0\pm 2.0$
au and that the projected center of the disk is minimally offset from the
central star, with best-fit $\Delta x,\Delta y=-1.0\pm 0.5,0.5\pm 0.5$ au.

Since we did not directly probe the inner and outer disk radius with our
ZODIPIC model, we must consider what the disk radius indicates further.
Scattered light observations mostly probe those small grains that can strongly
be affected by interaction with stellar radiation forces or wind. Pushed onto
eccentric or unbound orbits by the radiation pressure these small particles
can form an extended halo outside the ring of parent planetesimals. Such halos
can make difficult the accurate determination of the planetesimal belts’ outer
edge in scattered light observations, however we can reasonably assume that
the peak of the surface brightness distribution is coincided with the
planetesimal belt thereby probing its location. Though the presence of gas can
alter this picture in special cases (Richert et al., 2018), sensitive
observation of HD 115600 with the ALMA interferometer found no gas (Lieman-
Sifry et al., 2016), therefore in the following the belt is assumed to be
located at a radius of 46 au.

We performed a secondary, independent geometric analysis using the same
ellipse fitting method used originally by Currie et al. 2015b on HD 115600 and
described in Thalmann et al. 2011. First, we applied a median filter (FWHM,
$\sim 4$ pixels width) to the classical ADI image of the disk to reduce the
pixel-to-pixel noise. We then constructed a large grid of ellipses ($\sim
10,000$) around the best-fit disk model as determined by ZODIPIC and find a
new best-fit geometry as the ellipse which contains the highest average signal
within its trace. The center of the image ($\sim 25$ au) was again masked out
for this analysis to avoid contributions from the strong speckle noise.

The best-fit ellipse trace is shown superimposed over the disk in Figure 3. It
has projected semi-major and minor axes of $a=44.5\pm 0.8,b=8.9\pm 0.8$ au
corresponding to an inclination of $i=78.5^{\circ}\pm 1.0$, with a position
angle $P.A.=27.5^{\circ}\pm 1.1$. The disk center is offset $\Delta x,\Delta
y=0.8\pm 0.6,0.0\pm 0.6$ au. Uncertainty is again estimated as twice the step
size. We also perform the same analysis on KLIP ADI and SDI images. In those
cases, we achieve consistent results, and the estimated disk offset is
actually smaller.

Figure 3: Disk Modeling. Top Left: Band-averaged, KLIP SDI reduction of IFS images without subtraction. Top Right: The same reduction with the best-fit disk model from ZODIPIC subtracted. Bottom Left: Best fit ellipse model in green over classical ADI reduction. Noise from the central star is masked to $\sim 25$ au. Bottom Right: ZODIPIC best-fit model which is used for subtraction. The star center marked by a cross. The centers of the respective disk models are marked with diamonds. Table 2: ZODIPIC Disk Modeling Parameter | Range Tested | Step Size | Best Fit | Uncertainty
---|---|---|---|---
Disk Radius (au) | 45 to 49 | 1.0 | 46 | $\pm 2.0$
Position Angle (deg) | 23 to 29 | 0.5 | 27 | $\pm 1.0$
Inclination (deg) | 78 to 81 | 0.5 | 80 | $\pm 1.0$
x Offset (au) | -1 to 4 | 0.25 | -1.0 | $\pm 0.5$
y Offset (au) | -1 to 4 | 0.25 | 0.5 | $\pm 0.2$

##  5 Discussion

###  5.1 Disk Offset

A disk pericenter offset can be caused by gravitational perturbations from one
or more exoplanets in close proximity to the disk, and thus is an indication
that unseen planets may be present. Many other disks in Sco-Cen have shown
structures and asymmetries, including HD 106906 (Kalas et al., 2015), HD
111520 (Draper et al., 2016), HD 110058 (Kasper et al., 2015), HIP 67497
(Bonnefoy et al., 2017), etc.

Both disk modeling methods individually indicate there may be a small ($\sim
1$ au) projected disk offset in the x-direction, but do not agree on which
direction, and are consistent with zero offset within $2\sigma$ errors.
Offsets on the order of $\sim 1$ au are at the scale of one IFS pixel, and are
therefore difficult to determine precisely. Thus, we are only able to
constrain that the projected offset does not exceed $\sim 1.5$ au in either
direction.

Our results on the disk eccentricity imply that any unseen exoplanets are
likely less massive than previously estimated. Using the projected disk offset
estimated by Currie et al. (2015b), Thilliez & Maddison (2017) showed that a
7.8 $M_{J}$ planet could be present in the system at 30 au with an
eccentricity of $e=0.2$, which could be detected by SPHERE or GPI. Due to the
inclination of the system, however, there is a high probability that the
planet would lie within the inner working angle of both instruments, and thus
undetected. If the disk eccentricity is smaller, though, planets far less
massive than $7.8M_{J}$ may be able to sculpt the disk, plausibly low enough
to elude detection in our data and those in Currie et al. 2015b.

###  5.2 Stirring of the Disk

Planetesimals must be stirred to produce smaller and smaller fragments and
finally dust via their collisions (Matthews et al., 2014). This stirring could
either be related to large planetesimals embedded in the belt (self-stirring,
Kenyon & Bromley, 2008) or a giant planet(s) located somewhere in the system
(planetary stirring, Mustill & Wyatt, 2009). In the following we will explore
whether the self-stirring scenario is feasible in the HD 115600 system or we
need a giant planet to explain the dust production. In the classical self-
stirring model collisional coagulation among smaller bodies leads to the
formation of Pluto-sized ( r $\sim$1000 km) planetesimals that then can ignite
a collisional cascade in the belt via their dynamical perturbation (Kenyon &
Bromley, 2008). The larger the radial distance and the smaller the surface
density of the disk, the longer is the time needed for the buildup of these
large bodies. Kenyon & Bromley (2008) provided an analytical formula for this
timescale: $t_{\rm 1000}=145x_{\rm m}^{-1.15}(a/80\,{\rm
au})^{3}(2M_{\sun}/M_{*})^{3/2}[\rm Myr]$. Here $a$ is the radial distance,
while the $x_{\rm m}$ parameter scales with the initial surface density, in
the case of the minimum-mass solar nebula $x_{\rm m}=1$. Adopting the 15 Myr
estimated age of HD115600 as the available time for the formation of Pluto-
sized bodies and a primary stellar mass of 1.5 M⊙ (taken from Chen, 2014), we
found that a disk with $x_{\rm m}\sim 2.5$ is enough to explain the stirring
at 46 au. Alternatively, according to turbulent concentration and
gravitational clumping models (Johansen et al., 2007; Cuzzi et al., 2008)
large planetesimals can form directly from the concentration of small pebbles
in a protoplanetary disk. Though these models predict very rapid formation of
hundred kilometer size bodies even at radii $>$100 au (Carrera et al., 2017),
additional time is needed to excite the neighbouring disk sufficiently (Krivov
& Booth, 2018). According to equation 34 from Krivov & Booth (2018) in this
model the stirring of the belt at a time of 15 Myr needs an even lower initial
surface density ($x_{\rm m}\sim 0.3$) than in the case of the gradual buildup
approach. Obviously, the initial surface density could not be arbitrarily
large: according to Mustill & Wyatt (2009), $x_{\rm m}>10$ would imply that
the self-stirring scenario may not be feasible in the given system raising the
suspicion that the dynamical excitation is rather related to a planet. The
obtained low $x_{m}$ values thus indicate that self-stirring can work in the
HD 115600 systems and it is not necessary to assume the presence of a giant
planet.

###  5.3 Analysis of the Spectral Energy Distribution

The spectral energy distribution (SED) of a stellar system can be used to
determine if there are one or more circumstellar disk components based on the
number of temperature peaks. If a disk has two spatially distinct components,
the inner component will be warmer than the outer component and both will have
unique blackbody distributions visible in the SED. Examining the SED of HD
115600, we can determine if there may be an inner disk component not visible
in our data.

By modeling the SED of HD 115600, Chen (2014) proposed a two-temperature disk
model with $T_{\rm d,warm}=499$ K and $T_{\rm d,warm}=109$ K to fit the
observed mid- and far-infrared (far-IR) excess. Similar analyses of Ballering
et al. (2013) and more recently Currie et al. (2015b), however, led to single
component models at temperatures of 109 and 115 K, respectively. All of these
results were based on measurements shortward of 70 µm. In order to better
characterize the far-IR SED of our target we examined unpublished 100 and 160
µm observations obtained with the PACS instrument onboard the Herschel Space
Observatory. We used the Herschel Interactive Processing Environment (HIPE
v13, Ott, 2010) to process the data following the basic steps described in
Moór et al. (2015). HD 115600 is detected at both wavelengths, however at
160µm it is contaminated by surrounding extended emission therefore no
photometry was derived. At 100µm our aperture photometry yielded a flux
density of 138$\pm$12 mJy. The aperture radius was set to 6″, the sky
background was measured in an annulus between 40″ and 50 ″. The quoted
uncertainty is a quadratic sum of the measurement and calibration (7%, Balog
et al., 2014) errors. To compile the final SED, previously obtained infrared
photometric and spectroscopic data taken from the literature and public
databases (Wright et al., 2010; Ishihara et al., 2010; Lebouteiller et al.,
2011; Chen, 2014) were supplemented by our new 100µm and a recent 1.24 mm
(lieman-sifry2016) photometry of the source. To estimate the photospheric
contribution at relevant wavelengths an ATLAS9 model (Castelli & Kurucz, 2003)
was fitted to the optical and near-IR photometric data of HD 115600 (taken
from Perryman, 1997; Høg et al., 2000; Cutri et al., 2003). Based on our
analysis of the source’s Strömgren photometry (Paunzen, 2015) the influence of
reddening was neglected, while for the metallicity a value of [Fe/H] = $-$0.16
was derived using the method proposed by Casagrande et al. (2011). By fixing
[Fe/H] = $-$0.16 as well as $\log g$ = 4.25 (i.e. assuming the primary is a
young dwarf star of slightly subsolar metallicity) a good best fit
photospheric model was achieved using $T_{\mathrm{eff}}$ = 6850 K, consistent
with an F2V star (Mamajek
http://www.pas.rochester.edu/~emamajek/EEM_dwarf_UBVIJHK_colors_Teff.txt).
Figure 4 displays the SED and the obtained photosphere model of our target.

After the subtraction of the photospheric contribution, the excess SED was
fitted by 1) a single temperature modified blackbody model where the
emissivity is 1 at wavelengths shorter than 100µm and varies as
$(\lambda/100{\micron})^{\beta}$ at longer wavelengths, and 2) by a two-
temperature model where the single modified blackbody component was
complemented with a warmer simple blackbody. We utilized a Levenberg-Marquardt
algorithm (Markwardt, 2009) to find the best-fitting model. In good accordance
with Ballering et al. (2013) and Currie et al. (2015b) we found that the SED
is adequately described by a single modified blackbody with $T_{\rm
dust}=111\pm 3$ K, $\beta=0.92\pm 0.22$, and $L_{\rm disk}/L_{\rm bol}=0.002$.

Figure 4: Spectral energy distribution of HD 115600 overplotted by the fitted
modified blackbody model.

###  5.4 Dust-rich Debris Disks around F-G-type Sco-Cen members

HD 115600 is a member of the Sco-Cen association. To put into context our
results on its disk parameters in the following we make a comparison with
other dust-rich debris disks in the same group. Using Spitzer 24$\mu$m
photometric data Chen et al. (2011) inferred an excess fraction of $\sim$33%
for F-G-type members of the Sco-Cen association. Eleven out of the revealed
disks were found to exhibit fractional luminosity higher than $10^{−3}$, and
ten from this bright disk subsample, including HD 115600, harbored dust colder
than 150 K. Observations of these ten dust-rich systems with ground-based
high-contrast imaging systems such as SPHERE and GPI in scattered light and
with the ALMA interferometer at millimeter wavelengths resulted in spatially
resolved disk images in all but one case (HD 117214). Fundamental properties
of these objects, derived based on their resolved images and analysis of their
SED, are summarized in Table 3.

Table 3: Stellar and Disk Parameters

Name | Spt. | Dist. | Lum. | Group | $L_{\rm disk}/L_{\rm bol}$ | $T_{\rm dust}$ | Ref. | $R_{\rm BB}$ | $R_{\rm belt}$ | Ref.
---|---|---|---|---|---|---|---|---|---|---
|  | (pc) | (L⊙) |  |  $\times$10-3 | (K) |  | (au) | (au) |
HD 106906 | F5V | 103.3 | 6.56 | LCC | 1.8 | 53∗ | 1 | 70.5 | 71 | 4
HD 111520 | F5/F6V | 108.9 | 2.58 | LCC | 1.5 | 49∗ | 3 | 51.7 | 70 | 2
HD 114082 | F3V | 95.7 | 3.86 | LCC | 4.3 | 110 | 1 | 12.5 | 31 | 7
HD 115600 | F2IV/V | 109.6 | 4.84 | LCC | 2.0 | 111 | 4 | 13.8 | 46 | 8
HD 120326 | F0V | 113.9 | 4.51 | UCL | 2.2 | 114 | 1 | 12.6 | 62 | 1
HD 129590 | G3V | 136.0 | 2.98 | UCL | 7.7 | 89 | 1 | 16.9 | 57 | 6
HD 145560 | F5V | 120.4 | 3.24 | UCL | 3.9 | 53∗ | 1 | 49.5 | 79∗ | 5
HD 146181 | F6V | 125.0 | 2.73 | UCL | 3.0 | 71∗ | 1 | 25.3 | 79∗ | 5
HD 146897 | F2/F3V | 131.5 | 3.29 | US | 5.6 | 94∗ | 2 | 15.9 | 78 | 3

Note. — Column (1): Target name. Column (2): Spectral type (from SIMBAD).
Column (3): Distance (from Gaia DR2 parallax, Lindegren et al., 2018). Column
(4): Luminosity. Data were taken from Gaia DR2 catalogue (Lindegren et al.,
2018; Andrae et al., 2018) except for HD 115600 where we used our own
estimate. Column (5): Group membership. LCC: Lower Centaurus Crux association;
UCL: Upper Centaurus Lupus association; US: Upper Scorpius association. Column
(6): Fractional luminosity of the disk. Column (7): Dust temperature. For two-
temperature disks (marked by asterisks) the temperature of the colder
component was quoted. Column (8): Reference for dust temperature. (1)
Ballering et al. (2013); (2) Chen (2014); (3) Draper et al. (2016); (4) this
work. Column (9): Disk radius estimated from the characteristic dust
temperature assuming grains act like blackbodies ($\frac{R_{\rm
BB}}{au}=\left(\frac{L_{\rm
star}}{L_{\odot}}\right)^{0.5}\left(\frac{278\,K}{T_{\rm disk}}\right)^{2}$ ).
Column (10): Disk radius derived from spatially resolved scattered light or
millimeter images. For scattered light data the radius of peak dust density
was adopted, in the case of millimeter data (marked by asterisks) the average
between the inner and outer belt radii were used. The original estimates (for
references see Col. 11) were recomputed considering the new Gaia DR2
distances. Column (11): Reference for $R_{\rm belt}$ values: (1) Bonnefoy et
al. (2017); (2) Draper et al. (2016); (3) Engler et al. (2017); (4) Lagrange
et al. (2016); (5) Lieman-Sifry et al. (2016); (6) Matthews et al. (2017); (7)
Wahhaj et al. (2016); (8) this work.

The radii of the resolved dust belts range between 30 and 80 au. With its
radius of 46 au, HD 115600 is the second smallest object after the disk of HD
114082 and resembles our Kuiper-belt the most among all the targets (see also
Currie et al., 2015b). The ratio of the disk sizes derived from the spatially
resolved images ($R_{\rm belt}$) to radii inferred from dust temperatures
assuming large grains behaving as blackbodies ($R_{\rm BB}$) is higher than 1
for all cases. This is a well known signature of the presence of small dust
particles that, as being inefficient emitters at long wavelengths, are hotter
than blackbodies (Booth et al., 2013). Small grains should not be stable under
the influence of radiation pressure, and their presence suggests an active
source of supply from grinding/aggregating planetesimals and sublimating icy
bodies.

Figure 5 shows the $\Gamma=R_{belt}/R_{BB}$ ratios as a function of stellar
luminosity for our targets. For comparison, debris disks resolved with the
Herschel Space Observatory around stars having luminosity similar to that of
our study are also displayed (data for these objects were taken from table 4
of Pawellek et al., 2014). As Figure 5 demonstrates dust-rich debris disks
around F-G-type members of Sco-Cen show a great variety in terms of $\Gamma$
values. On the one hand HD 106906 with its $\Gamma$ value close to 1 may
mostly harbor larger particles. On the other hand the belts around HD 120326
and HD 146897 are significantly larger than expected using a blackbody
assumption suggesting a substantial overabundance of hot small grains.
Interestingly, HD 146897 is one of those rare F-type stars where CO emission
was detected (lieman-sifry2016) raising the possibility that the presence of
gas may play a role in accumulation of small grains in these systems e.g. by
retaining those particles that would be blown out by the radiation pressure in
a gas-free environment (see e.g. Wyatt, 2018). As already noted by Currie et
al. (2015b), HD 115600 exhibits a moderately high $\Gamma$ value of $\sim$3.3
indicating the presence of copious amount of small dust particles in the disk.

Figure 5: The ratio of the measured disk radius to its blackbody radius as a
function of stellar luminosity. Dust-rich debris disks around Sco-Cen members
are displayed by red circles while disks from Pawellek et al. (2014) are
marked by gray diamonds.

##  6 Spectral Analysis

We extracted YJ-band spectra from both the processed data, and from our
wavelength-dependent disk model that was previously described in Section 4.
Both spectra were extracted with a similar methodology to Currie et al.
(2015b), to better compare our results. We first measured the average surface
brightness, that is the mean pixel value, in each wavelength channel within
two 8-pixel diameter apertures centered on the disk extremities in both the
IFS and disk model images. These were the same regions selected in Section 3
and by (Currie et al., 2015b) for spectra, and contain the highest signal-to-
noise. We converted these raw surface brightnesses into reflectance spectra
(i.e., the relative brightness of the disk compared to HD 115600) by dividing
the peak value of the flux calibration PSF in each wavelength, as determined
by a Gaussian fit (IDL routine GAUSS2DFIT). The spectra from both disk ansa
were then averaged together to produce a combined spectrum. Unfortunately, we
found that the spectrum produced from the processed IFS data suffers from
significant variability due to speckle noise, and therefore only kept the
combined reflectance spectrum from our model for analysis. The uncertainty in
the model reflectance spectrum is determined as a combination of the measured
speckle noise in each wavelength channel (using the same method from Section
3), and the brightness uncertainty in the model from the grid step size,
discussed in Section 4.

The spectrum of a debris disk depends, among other factors, on the dust
composition. By combining our SPHERE YJ-band reflectance spectrum with the GPI
H-band reflectance spectrum from Currie et al. (2015b), and comparing them to
reflectance models, we aim to constrain the disk composition further. To avoid
biases inherent in combining the data from multiple instruments into a single
spectrum, we compare the SPHERE spectrum to reflectance models individually.
We choose to compare the SPHERE spectrum to the same reflectance models used
in Currie et al. (2015b), for water ice, amorphous carbon, and amorphous
silicate dust compositions. These models are based on simple Mie scattering
theory predictions from Lisse et al. (1998) and Halley-like particle size
distributions (Krishna Swamy & Shah, 1988; Mazets et al., 1986), rather than
more realistic calculations. To convert our reflectance spectrum into
normalized units, we scale the spectrum so that the spectrum’s maximum is
equal to a reflectivity of one. We then scale each reflectance model to
minimize $\chi^{2}$, and the lowest is the best-fit.

Our reflectance spectrum for SPHERE is shown in Figure 6, along with the
aforementioned reflectance models. It shows a reddish slope, and, without
considering GPI H band spectra yet, has the best agreement with amorphous
carbon and silicate compositions, marginally favoring carbon. Water ice is
slightly disfavored due to its blue slope, with a $\chi^{2}$ values 2 times
that of amorphous carbon. The uncertainty on the spectrum, however, means that
none of the reflectance models are ruled out at the 95% confidence limit, and
any of the 3 compositions could be viable.

Our spectral results in the Y-J bands suggest a more complex composition than
the range of those considered in Currie et al. (2015b) using the H band. The H
band spectrum from GPI data marginally favors water ice composition while
disfavoring amorphous carbon, and our Y-J band spectrum from SPHERE data
suggests the opposite, however, both spectra could be viable with all our
reflectance models. This illustrates the difficulty of obtaining precise
composition determinations of debris disks, as the spectra are mostly flat and
different compositions are highly degenerate. Of course, the true disk
composition is likely to be a complex mix of outer solar system materials
(i.e. ices, rock, and complex organics), such that a superposition of spectra
may yield a better fit. Comparing the overall Y-H colors of the HD 115600 disk
with those of solar system objects can provide some clues to a mixed
composition (Lisse et al. 2017, Fig. 4). As solar system objects become more
active, they tend to become redder in the NIR due to increasing exposure of
organic material and loss of surface ices. The active Centaur 5145 Pholus, for
example, has an extremely red reflectance spectrum up to about 1.5 microns,
but flattens out and becomes slightly blue at longer wavelengths, similar to
YJ to H bands the trends we see for the HD 115600 disk. The trend for 5145
Pholus is known to be caused by exposed reddish complex organic materials
mixed with surface ice absorption (Barucci et al., 2002). Other active
Centaurs display similar behavior. By contrast, highly processed short period
comets maintain a reddish color out to 4-5 microns due to their extreme
activity and solar processing (Quirico et al., 2016). Interestingly enough,
the approximately 100K dust in the bright ring surrounding A0.5V HR4796A at 75
AU distance matches this behavior (Lisse et al., 2017). On the other hand,
pristine Kuiper Belt Objects (KBOs) at T < 50K evince mostly neutral or blue
spectra due to the dominance of abundant ices (CO, CH4, N2, H2O) on their
surfaces. We can therefore hypothesize that the HD115600 disk has a mixed
composition and is composed of dust emitted from large semi-active
planetesimals.

Figure 6: HD 115600 reflectance spectra. Reflectance spectra (black) from
SPHERE YJ-band (top) and GPI H-band (bottom) compared to Mie theory
predictions for water ice, amorphous carbon, and amorphous silicates (Currie
et al., 2015b). Each reflectance model is shown scaled to best-fit the
spectra. Figure 7: Radial sensitivity for the KLIP reduction of IRDIS H23
Data. The disk is clearly present and limits sensitivity around 0.2
arcseconds. Mass sensitivity limits from hot and cold start model predictions
(Baraffe et al., 2003; Marley et al., 2007; Mordasini, 2013) are shown as
horizontal lines. Within the IFS field-of-view($\sim 1$ arcsecond), we achieve
a sensitivity of $\sim 3-4(7-12)M_{Jup}$ for hot (cold) start models. Limits
may be slightly higher for particularly dusty, cloudy planets that are
slightly underluminous in $H$ band (e.g. Currie et al., 2011, 2018; De Rosa et
al., 2016). The contrast of several point-sources within the IRDIS field-of-
view are numbered.

##  7 Planet Detection Limits

While no wide-orbit giant planet is obviously detected in our data, the lack
of detection places useful constraints on the presence of planets external to
the disk (projected radii $>50$ au). To analyze our detection threshold we
injected false-positive point sources at 4$\times 10^{-5}$ contrast into the
raw data prior to processing. We then measured the brightness of the recovered
sources compared to the originally injected PSF, and utilized the average
recovered contrast to estimate the throughput correction appropriate for the
data processing. We find that the throughput correction is essentially uniform
with radius exterior to the disk, and thus we adopt a constant correction
throughout the image. To compute a detection threshold for each radius, we
integrated the flux in non-overlapping apertures of radius equivalent to the
beam FWHM while accounting for small sample statistics (Mawet et al., 2014).
We multiplied the standard deviation of these measurements at a particular
radius by the throughput correction factor and scaled this by a factor of five
to arrive at the reported $5\sigma$ contrast limit shown in Figure 7. This
analysis suggests that we would have confidently detected a point source of
5$\times 10^{-6}$ (H<20.6) between the outer edge of the disk and 100 au, or a
source of 2$\times 10^{-6}$ (H<21.6) between 100-200 au. Compared to
hot(cold)-start evolutionary models (Baraffe et al., 2003; Marley et al.,
2007; Mordasini, 2013) which give theoretical planet luminosities at the
system’s age, these brightness measurements correspond to mass limits of 3-4
(7-12) and 2-3 (5-7) $M_{\rm J}$, respectively, depending on the core mass.

##  8 Summary

We present new extreme adaptive optics (VLT/SPHERE) imaging of the debris disk
surrounding the young star HD 115600. Our dataset improves characterization of
the surrounding debris disk and upper limits on possible exoplanets embedded
in the disk. We model the debris disk to investigate structure that could
indicate unseen exoplanet(s), as suggested by Currie et al. (2015b) and
Thilliez & Maddison (2017). We determine disk geometry by two methods, using
the code ZODIPIC (Kuchner, 2012) to inject and subtract a grid of disk models
into the raw data, and by a ’maximum merit’ ellipse fitting method on the ADI
images, as described in Thalmann et al. (2011). The key findings of our study
are as follows:

1) We confidently detect the disk (SNR$\sim 20$) at separations between 0.35″
and 0.48″ and at wavelength between 1.0 $\mu$m and 1.6 $\mu$m and present near
diffraction-limited images at these wavelengths.

2)Using ZODIPIC, we determine a best-fit disk model with central radius
$a_{0}=46\pm 2$au, inclination $i=80^{\circ}\pm 1$, position angle of
$27^{\circ}\pm 1$, and offsets $\Delta\alpha,\Delta\delta$ = $-1.0\pm
0.5,0.5\pm 0.5$ au. By ellipse fitting, we determine a best-fit disk model
with projected semimajor axis $a=44.5\pm 0.8$ au, inclination
$i=78.5^{\circ}\pm 1.0$, position angle of $27.5^{\circ}\pm 1.1$, and offsets
of $\Delta\alpha,\Delta\delta$ = $0.8\pm 0.6,0.0\pm 0.6$ au.

The disk offset is smaller than previous estimates derived from modeling with
the H-G scattering function. As a result, planets responsible for sculpting
the debris disk could be significantly lower in mass than previously
estimated.

3) Using SPHERE IFS, we produce a YJ-band reflectance spectrum for the disk,
which is reddish, and marginally supports an amorphous carbon composition,
although other compositions cannot be decisively ruled out. Considering the
flat to slightly blue H band reflectance previously seen with GPI and a survey
of our solar system’s small body reflectance spectra, the combined results are
consistent with a mixed organic and water ice dust composition typical of our
solar system’s active Centaurs interacting with the giant planets and the Sun.

4) We constrain a mass sensitivity limit of 3-4(7-12) $M_{\rm J}$ with 100 au
of the star, and 2-3(5-7) $M_{\rm J}$ within 100-200 au by combining contrast
limits from fake planet injections with hot(cold)-start model predictions.

##  Appendix A Properties of detected point-sources

To investigate the nature of the sources in the SPHERE and GPI images, we list
the astrometry in tables 4 and 5, and the astrometric difference of commonly
detected sources in table 6. Note that source 1 is a likely disk feature as it
falls within the region of the scattered light ring. Sources 2-4 exhibit
motions in a consistent direction with one another, and consistent with the
reflex motion of the proper motion of HD 115600, and thus we classify these
sources as background stars.

Table 4: Properties of Point-Sources detected around HD 115600 in the IRDIS
Field

Source | SNR | Contrast |  $\rho$ (") |  $\theta$ (deg)
---|---|---|---|---
1 | 5.81 | 6.78$\times 10^{-5}$ | 0.1284 | 53.69
2 | 14.69 | 8.25$\times 10^{-6}$ | 1.0476 | 211.60
3 | 22.14 | 7.56$\times 10^{-6}$ | 1.1727 | 63.03
4 | 47.91 | 1.32$\times 10^{-5}$ | 1.3481 | 202.95
5 | 6.81 | 1.29$\times 10^{-6}$ | 1.6482 | 144.53
6 | 76.50 | 1.48$\times 10^{-5}$ | 1.8739 | 222.25
7 | 23.77 | 2.67$\times 10^{-6}$ | 2.1634 | 272.49
8 | 5.11 | 6.04$\times 10^{-7}$ | 2.4740 | 259.48
9 | 9.51 | 1.04$\times 10^{-6}$ | 2.6082 | 290.10
10 | 11.66 | 1.26$\times 10^{-6}$ | 2.6159 | 103.44
11 | 53.06 | 6.48$\times 10^{-6}$ | 2.8354 | 306.45
12 | 420.11 | 5.05$\times 10^{-5}$ | 2.8929 | 3.56
13 | 290.13 | 3.51$\times 10^{-5}$ | 2.9851 | 217.98
14 | 36.92 | 3.30$\times 10^{-6}$ | 3.2857 | 69.59
15 | 9.65 | 8.88$\times 10^{-7}$ | 3.3603 | 322.31
16 | 17199.3 | 0.00154 | 3.3732 | 63.39
17 | 17.74 | 2.14$\times 10^{-6}$ | 3.4622 | 209.25
18 | 110.93 | 2.65$\times 10^{-5}$ | 3.5305 | 339.53
19 | 221.18 | 4.08$\times 10^{-5}$ | 3.5837 | 190.02
20 | 7.70 | 1.13$\times 10^{-6}$ | 3.6186 | 328.37
21 | 22.01 | 2.25$\times 10^{-6}$ | 3.7714 | 104.46
22 | 7.54 | 7.95$\times 10^{-7}$ | 3.7861 | 245.12
23 | 94.40 | 9.20$\times 10^{-6}$ | 3.9049 | 210.63
24 | 6.02 | 7.07$\times 10^{-7}$ | 3.9917 | 284.77
25 | 11.50 | 1.32$\times 10^{-6}$ | 3.9924 | 301.10
26 | 111.28 | 1.47$\times 10^{-5}$ | 4.0119 | 134.29
27 | 68.16 | 6.44$\times 10^{-6}$ | 4.0512 | 122.57
28 | 6.21 | 6.32$\times 10^{-7}$ | 4.1346 | 208.78
29 | 89.68 | 8.20$\times 10^{-6}$ | 4.2913 | 155.16
30 | 56.24 | 5.10$\times 10^{-6}$ | 4.6783 | 316.74
31 | 190.46 | 2.12$\times 10^{-5}$ | 4.7166 | 38.88
32 | 130.87 | 1.18$\times 10^{-5}$ | 4.9489 | 45.61
33 | 3203.20 | 0.0002726 | 5.0191 | 88.65
34 | 7.90 | 6.53$\times 10^{-7}$ | 5.0434 | 353.76

Note. — Column(1): Sources as numbered in Figure 2 and Figure 7. Column(2):
Detection significance. Column(3): Contrast to HD 115600. Column(4):
Separation in arcseconds. Column(5): Position angle in degrees.

Table 5: Properties of Point-Sources detected around HD 115600 in the GPI Field Source | SNR |  $\rho$ (") |  $\theta$ (deg)
---|---|---|---
2 | 8.070 | 1.0854 | 213.18
3 | 8.15 | 1.1488 | 62.94
4 | 8.99 | 1.3737 | 204.12
Table 6: Astrometric Motion of Commonly Detected Sources (Relative to HD 115600 over 2015.42-2014.31) Source |  $\Delta$ RA (mas) |  $\Delta$ Dec (mas)
---|---|---
2 | 45.1 | 16.2
3 | 22.1 | 9.3
4 | 35.6 | 12.4
Mean | 34.2 | 12.6
StdDev | 11.5 | 3.4
HD 115600 | PM RA | PM Dec
GAIA DR2 | -32.63$\pm$0.05 mas/yr | -18.32$\pm$0.05 mas/yr

## References

  * Ahmic et al. (2009) Ahmic, M., Croll, B., & Artymowicz, P. 2009, ApJ, 705, 529
  * Andrae et al. (2018) Andrae, R., Fouesneau, M., Creevey, O., et al. 2018, A&A, 616, A8
  * Apai et al. (2015) Apai, D., Schneider, G., Grady, C. A., et al. 2015, ApJ, 800, 136
  * Apai et al. (2016) Apai, D., Kasper, M., Skemer, A., et al. 2016, ApJ, 820, 40
  * Bailey et al. (2014) Bailey, V., Meshkat, T., Reiter, M., et al. 2014, ApJ, 780, L4
  * Bailey et al. (2016) Bailey, V. P., Poyneer, L. A., Macintosh, B. A., et al. 2016, in Proc. SPIE, Vol. 9909, Adaptive Optics Systems V, 99090V
  * Ballering et al. (2013) Ballering, N. P., Rieke, G. H., Su, K. Y. L., & Montiel, E. 2013, ApJ, 775, 55
  * Balog et al. (2014) Balog, Z., Müller, T., Nielbock, M., et al. 2014, Experimental Astronomy, 37, 129
  * Baraffe et al. (2003) Baraffe, I., Chabrier, G., Barman, T. S., Allard, F., & Hauschildt, P. H. 2003, A&A, 402, 701
  * Barucci et al. (2002) Barucci, M. A., Cruikshank, D. P., Mottola, S., & Lazzarin, M. 2002, Physical Properties of Trojan and Centaur Asteroids, ed. W. F. Bottke, Jr., A. Cellino, P. Paolicchi, & R. P. Binzel, 273
  * Beuzit et al. (2008) Beuzit, J.-L., Feldt, M., Dohlen, K., et al. 2008, in Proc. SPIE, Vol. 7014, Ground-based and Airborne Instrumentation for Astronomy II, 701418
  * Bonnefoy et al. (2017) Bonnefoy, M., Milli, J., Ménard, F., et al. 2017, A&A, 597, L7
  * Booth et al. (2013) Booth, M., Kennedy, G., Sibthorpe, B., et al. 2013in  (Cambridge University Press), 330
  * Bottom et al. (2017) Bottom, M., Ruane, G., & Mawet, D. 2017, Research Notes of the American Astronomical Society, 1, 30
  * Carpenter et al. (2009) Carpenter, J. M., Bouwman, J., Mamajek, E. E., et al. 2009, ApJS, 181, 197
  * Carrera et al. (2017) Carrera, D., Gorti, U., Johansen, A., & Davies, M. B. 2017, The Astrophysical Journal, 839, 16
  * Casagrande et al. (2011) Casagrande, L., Schönrich, R., Asplund, M., et al. 2011, A&A, 530, A138
  * Castelli & Kurucz (2003) Castelli, F., & Kurucz, R. L. 2003, in IAU Symposium, Vol. 210, Modelling of Stellar Atmospheres, ed. N. Piskunov, W. W. Weiss, & D. F. Gray, A20
  * Chauvin et al. (2017) Chauvin, G., Desidera, S., Lagrange, A.-M., et al. 2017, A&A, 605, L9
  * Chen (2014) Chen, C. 2014, in Habitable Worlds Across Time and Space, proceedings of a conference held April 28-May 1 2014 at the Space Telescope Science Institute, P8
  * Chen et al. (2011) Chen, C. H., Mamajek, E. E., Bitner, M. A., et al. 2011, ApJ, 738, 122
  * Claudi et al. (2008) Claudi, R. U., Turatto, M., Gratton, R. G., et al. 2008, in Proc. SPIE, Vol. 7014, Ground-based and Airborne Instrumentation for Astronomy II, 70143E
  * Currie et al. (2015a) Currie, T., Cloutier, R., Brittain, S., et al. 2015a, ApJ, 814, L27
  * Currie et al. (2015b) Currie, T., Lisse, C. M., Kuchner, M., et al. 2015b, ApJ, 807, L7
  * Currie et al. (2011) Currie, T., Burrows, A., Itoh, Y., et al. 2011, ApJ, 729, 128
  * Currie et al. (2017) Currie, T., Guyon, O., Tamura, M., et al. 2017, ApJ, 836, L15
  * Currie et al. (2018) Currie, T., Brandt, T. D., Uyama, T., et al. 2018, ArXiv e-prints, arXiv:1810.09457 [astro-ph.EP]
  * Cutri et al. (2003) Cutri, R. M., Skrutskie, M. F., van Dyk, S., et al. 2003, VizieR Online Data Catalog, 2246
  * Cuzzi et al. (2008) Cuzzi, J. N., Hogan, R. C., & Shariff, K. 2008, ApJ, 687, 1432
  * De Rosa et al. (2016) De Rosa, R. J., Rameau, J., Patience, J., et al. 2016, ApJ, 824, 121
  * Dohlen et al. (2008) Dohlen, K., Langlois, M., Saisse, M., et al. 2008, in Proc. SPIE, Vol. 7014, Ground-based and Airborne Instrumentation for Astronomy II, 70143L
  * Draper et al. (2016) Draper, Z. H., Duchêne, G., Millar-Blanchaer, M. A., et al. 2016, ApJ, 826, 147
  * Engler et al. (2017) Engler, N., Schmid, H. M., Thalmann, C., et al. 2017, A&A, 607, A90
  * Fusco et al. (2006) Fusco, T., Rousset, G., Sauvage, J.-F., et al. 2006, Optics Express, 14, 7515
  * Gaia Collaboration et al. (2018) Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1
  * Gáspár et al. (2012) Gáspár, A., Psaltis, D., Rieke, G. H., & Özel, F. 2012, ApJ, 754, 74
  * Goebel et al. (2018) Goebel, S., Currie, T., Guyon, O., et al. 2018, AJ submitted
  * Graham et al. (2007) Graham, J. R., Kalas, P. G., & Matthews, B. C. 2007, ApJ, 654, 595
  * Hanson & Apai (2015) Hanson, J. R., & Apai, D. 2015, in American Astronomical Society Meeting Abstracts, Vol. 225, American Astronomical Society Meeting Abstracts #225, 258.13
  * Høg et al. (2000) Høg, E., Fabricius, C., Makarov, V. V., et al. 2000, A&A, 357, 367
  * Hong (1985) Hong, S. S. 1985, A&A, 146, 67
  * Hughes et al. (2018) Hughes, A. M., Duchêne, G., & Matthews, B. C. 2018, ARA&A, 56,
  * Ishihara et al. (2010) Ishihara, D., Onaka, T., Kataza, H., et al. 2010, A&A, 514, A1
  * Jang-Condell et al. (2015) Jang-Condell, H., Chen, C. H., Mittal, T., et al. 2015, ApJ, 808, 167
  * Johansen et al. (2007) Johansen, A., Oishi, J. S., Mac Low, M.-M., et al. 2007, Nature, 448, 1022
  * Kalas et al. (2015) Kalas, P. G., Rajan, A., Wang, J. J., et al. 2015, ApJ, 814, 32
  * Kasper et al. (2015) Kasper, M., Apai, D., Wagner, K., & Robberto, M. 2015, ApJ, 812, L33
  * Kelsall et al. (1998) Kelsall, T., Weiland, J. L., Franz, B. A., et al. 1998, ApJ, 508, 44
  * Kenyon & Bromley (2008) Kenyon, S. J., & Bromley, B. C. 2008, ApJS, 179, 451
  * Keppler et al. (2018) Keppler, M., Benisty, M., Müller, A., et al. 2018, ArXiv e-prints, arXiv:1806.11568 [astro-ph.EP]
  * Krishna Swamy & Shah (1988) Krishna Swamy, K. S., & Shah, G. A. 1988, MNRAS, 233, 573
  * Krivov & Booth (2018) Krivov, A. V., & Booth, M. 2018, Monthly Notices of the Royal Astronomical Society, 479, 3300
  * Kuchner (2012) Kuchner, M. 2012, ZODIPIC: Zodiacal Cloud Image Synthesis, Astrophysics Source Code Library, ascl:1202.002
  * Lagrange et al. (2016) Lagrange, A.-M., Langlois, M., Gratton, R., et al. 2016, A&A, 586, L8
  * Langlois et al. (2013) Langlois, M., Vigan, A., Moutou, C., et al. 2013, in Proceedings of the Third AO4ELT Conference, ed. S. Esposito & L. Fini, 63
  * Lebouteiller et al. (2011) Lebouteiller, V., Barry, D. J., Spoon, H. W. W., et al. 2011, ApJS, 196, 8
  * Lieman-Sifry et al. (2016) Lieman-Sifry, J., Hughes, A. M., Carpenter, J. M., et al. 2016, The Astrophysical Journal, 828, 25
  * Lindegren et al. (2018) Lindegren, L., Hernández, J., Bombrun, A., et al. 2018, A&A, 616, A2
  * Lisse et al. (1998) Lisse, C. M., A’Hearn, M. F., Hauser, M. G., et al. 1998, ApJ, 496, 971
  * Lisse et al. (2017) Lisse, C. M., Sitko, M. L., Marengo, M., et al. 2017, AJ, 154, 182
  * Markwardt (2009) Markwardt, C. B. 2009, in Astronomical Society of the Pacific Conference Series, Vol. 411, Astronomical Data Analysis Software and Systems XVIII, ed. D. A. Bohlender, D. Durand, & P. Dowler, 251
  * Marley et al. (2007) Marley, M. S., Fortney, J. J., Hubickyj, O., Bodenheimer, P., & Lissauer, J. J. 2007, ApJ, 655, 541
  * Marois et al. (2006) Marois, C., Lafrenière, D., Doyon, R., Macintosh, B., & Nadeau, D. 2006, ApJ, 641, 556
  * Matthews et al. (2014) Matthews, B. C., Krivov, A. V., Wyatt, M. C., Bryden, G., & Eiroa, C. 2014, Protostars and Planets VI, 521
  * Matthews et al. (2017) Matthews, E., Hinkley, S., Vigan, A., et al. 2017, ApJ, 843, L12
  * Mawet et al. (2014) Mawet, D., Milli, J., Wahhaj, Z., et al. 2014, ApJ, 792, 97
  * Mazets et al. (1986) Mazets, E. P., Aptekar, R. L., Golenetskii, S. V., et al. 1986, Nature, 321, 276
  * Mesa et al. (2015) Mesa, D., Gratton, R., Zurlo, A., et al. 2015, A&A, 576, 121
  * Meshkat et al. (2017) Meshkat, T., Mawet, D., Bryan, M. L., et al. 2017, AJ, 154, 245
  * Milli et al. (2017) Milli, J., Vigan, A., Mouillet, D., et al. 2017, A&A, 599, A108
  * Moór et al. (2015) Moór, A., Kóspál, Á., Ábrahám, P., et al. 2015, MNRAS, 447, 577
  * Mordasini (2013) Mordasini, C. 2013, A&A, 558, A113
  * Mustill & Wyatt (2009) Mustill, A. J., & Wyatt, M. C. 2009, MNRAS, 399, 1403
  * Ott (2010) Ott, S. 2010, in Astronomical Society of the Pacific Conference Series, Vol. 434, Astronomical Data Analysis Software and Systems XIX, ed. Y. Mizumoto, K.-I. Morita, & M. Ohishi, 139
  * Paunzen (2015) Paunzen, E. 2015, A&A, 580, A23
  * Pavlov et al. (2008) Pavlov, A., Möller-Nilsson, O., Feldt, M., et al. 2008, in Advanced Software and Control for Astronomy II (SPIE)
  * Pawellek et al. (2014) Pawellek, N., Krivov, A. V., Marshall, J. P., et al. 2014, ApJ, 792, 65
  * Pecaut & Mamajek (2016) Pecaut, M. J., & Mamajek, E. E. 2016, MNRAS, 461, 794
  * Pecaut et al. (2012) Pecaut, M. J., Mamajek, E. E., & Bubar, E. J. 2012, ApJ, 746, 154
  * Perryman (1997) Perryman, M. A. C. 1997, in ESA Special Publication, Vol. 402, Hipparcos - Venice ’97, ed. R. M. Bonnet, E. Høg, P. L. Bernacca, L. Emiliani, A. Blaauw, C. Turon, J. Kovalevsky, L. Lindegren, H. Hassan, M. Bouffard, B. Strim, D. Heger, M. A. C. Perryman, & L. Woltjer, 1
  * Quanz et al. (2013) Quanz, S. P., Amara, A., Meyer, M. R., et al. 2013, ApJ, 766, L1
  * Quirico et al. (2016) Quirico, E., Moroz, L. V., Schmitt, B., et al. 2016, Icarus, 272, 32
  * Racine et al. (1999) Racine, R., Walker, G. A. H., Nadeau, D., Doyon, R., & Marois, C. 1999, Publications of the Astronomical Society of the Pacific, 111, 587
  * Rameau et al. (2013) Rameau, J., Chauvin, G., Lagrange, A.-M., et al. 2013, ApJ, 772, L15
  * Richert et al. (2018) Richert, A. J. W., Lyra, W., & Kuchner, M. J. 2018, ApJ, 856, 41
  * Soummer et al. (2012) Soummer, R., Pueyo, L., & Larkin, J. 2012, ApJ, 755, L28
  * Su et al. (2013) Su, K. Y. L., Rieke, G. H., Malhotra, R., et al. 2013, ApJ, 763, 118
  * Thalmann et al. (2011) Thalmann, C., Janson, M., Buenzli, E., et al. 2011, ApJ, 743, L6
  * Thalmann et al. (2013) Thalmann, C., Janson, M., Buenzli, E., et al. 2013, ApJ, 763, L29
  * Thilliez & Maddison (2017) Thilliez, E., & Maddison, S. T. 2017, MNRAS, 464, 1434
  * Vigan et al. (2015) Vigan, A., Gry, C., Salter, G., et al. 2015, MNRAS, 454, 129
  * Vigan et al. (2010) Vigan, A., Moutou, C., Langlois, M., et al. 2010, MNRAS, 407, 71
  * Wahhaj et al. (2016) Wahhaj, Z., Milli, J., Kennedy, G., et al. 2016, A&A, 596, L4
  * Wright et al. (2010) Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868
  * Wyatt (2008) Wyatt, M. C. 2008, ARA&A, 46, 339
  * Wyatt (2018) Wyatt, M. C. 2018, ArXiv e-prints, arXiv:1804.08636 [astro-ph.EP]