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A Pan-STARRS1 Search for Planet Nine
Michael E. Brown
1
, Matthew J. Holman
2
, and Konstantin Batygin
1
1
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 9125, USA; mbrown@caltech.edu
2
Center for Astrophysics, Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA
Received 2023 December 22; revised 2024 January 30; accepted 2024 January 30; published 2024 March 7
Abstract
We present a search for Planet Nine using the second data release of the Pan-STARRS1 survey. We rule out the
existence of a Planet Nine with the characteristics of that predicted in Brown & Batygin to a 50% completion depth
of V=21.5. This survey, along with previous analyses of the Zwicky Transient Facility and Dark Energy Survey
data, rules out 78% of the Brown & Batygin parameter space. Much of the remaining parameter space is at V>21
in regions near and in the area where the northern galactic plane crosses the ecliptic.
Unified Astronomy Thesaurus concepts: Sky surveys (1464);Solar system gas giant planets (1191)
1. Introduction
Speculation about the existence of planets beyond the orbit
of Neptune began almost as soon as the announcement of the
discovery of Neptune itself (Babinet 1848)and has continued
to the present day. While Standish (1993)demonstrated that no
evidence exists in the planetary ephemerides for any significant
perturber, the concurrent discovery of the large population of
small bodies in the Kuiper Belt beyond Neptune led to renewed
scrutiny of dynamical signatures of perturbation in this
population. The first concrete hint of the need for an external
perturber—at least at some point in solar system history—came
from the discovery of Sedna, with a perihelion at 76 au, well
beyond where it could have been perturbed by the known
planets (Brown et al. 2004). As more objects with the extreme
semimajor axis of Sedna were discovered, they were suggested
to be anomalously clustered around an argument of perihelion
of zero, though the physical mechanism for the apparent
clustering was unclear (Trujillo & Sheppard 2014). Subsequent
analysis showed that the apparent clustering in argument of
perihelion is actually a consequence of simultaneous clustering
in longitude of perihelion and in pole position, a phenomenon
that can be naturally explained by the presence of a massive
planet on a distant, eccentric, and inclined orbit (Batygin &
Brown 2016a). The prediction of the existence of this planet
has been called the Planet Nine hypothesis. Planet Nine is now
seen to be capable of accounting for a range of additional
otherwise unexplained phenomena in the solar system,
including the existence of highly inclined Kuiper Belt objects
and the existence of retrograde Centaurs (Batygin &
Brown 2016b).
Since the prediction of the existence of this planet,
discussions of alternative explanations have included sugges-
tions that it is instead a primordial black hole (Scholtz &
Unwin 2020): that the observed effects are caused by the
presence of a distant unseen ring of material (Madigan &
McCourt 2016;Sefilian & Touma 2019), or that the planet is
instead a collection of condensed dark matter (Sivaram et al.
2016), though a planet remains a far simpler explanation than
these exotic possibilities. Suggestions that observational bias
might be responsible for the observed clustering effects have
been made from analysis of limited data sets (Shankman et al.
2017; Napier et al. 2021), but analysis of the largest data sets
has repeatedly found the probability of such bias small (Brown
& Batygin 2019,2021). While the existence of Planet Nine
remains the most satisfactory explanation for a range of
phenomena, true detection of the planet will be required to
firmly discount these or other alternatives.
The Planet Nine hypothesis makes distinct predictions about
the properties of the planet and its orbit, based on the currently
observed distant eccentric Kuiper Belt population and on an
assumed source population for these bodies. Under the
assumption that the currently observed distant eccentric
population is sourced from an initial extended disk (rather than,
i.e., from the inner Oort cloud, Batygin & Brown 2021;
Nesvorný et al. 2023), Brown & Batygin (2021; hereafter
BB21)use a suite of numerical models corrected for
observational bias to construct statistical distributions of the
mass and orbital elements of the hypothetical Planet Nine
consistent with the observed clustering. All elements are well
constrained except for the mean anomaly; the current
observations show only the orbit of Planet Nine, not the
position within the orbit. To aid in the search for Planet Nine
and to better understand search limits for different surveys,
Brown & Batygin (2022; hereafter BB22)construct a synthetic
population by sampling from the posterior of the BB21 mass
and orbital element distributions. This synthetic population can
be injected into any data set to give a statistical representation
of Planet Nine parameter space and determine which parts of
parameter space a survey can rule out.
The first wide-field search for Planet Nine examined three
years of the Zwicky Transient Facilities (ZTF)archives
(BB22). This search covered most of the predicted path of
Planet Nine (with the exception of regions below −25 in decl.
and in the densest regions near the galactic center)and
concluded that a Planet Nine candidate with the predicted
parameters was not detectable in the archive. Though typical
ZTF images reach a depth of only r∼20.5, injection of the
synthetic population into the ZTF catalog showed that ZTF was
sensitive to 56% of the predicted parameter space of
Planet Nine.
The Dark Energy Survey (DES)covered only a modest
fraction of the predicted orbital path of Planet Nine, but the
entire survey region was surveyed for moving objects with
The Astronomical Journal, 167:146 (7pp), 2024 April https://doi.org/10.3847/1538-3881/ad24e9
© 2024. The Author(s). Published by the American Astronomical Society.
Original content from this work may be used under the terms
of the Creative Commons Attribution 4.0 licence. Any further
distribution of this work must maintain attribution to the author(s)and the title
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1
distances from 30 to 2000 au (Bernardinelli et al. 2022).No
Planet Nine or other distant object candidates were found.
Belyakov et al. (2022)used the previously developed synthetic
population to show that the DES ruled out 10.2% of the
predicted phase space of Planet Nine, of which 5.0% had not
previously been ruled out by ZTF. Between the ZTF and DES
survey 61.2% of the predicted Planet Nine phase space has
been ruled out.
Here, we extend the search for Planet Nine to the Pan-
STARRS1 DR2 (PS1)database. PS1 observed a similar
amount of sky as ZTF, but with deeper—though less
frequent—coverage. For much of the sky, PS1 should extend
the magnitude limit of the search for Planet Nine by
approximately a magnitude.
2. The Pan-STARRS1 Data
The Pan-STARRS1 survey covered the approximately 3π
steradians of the sky north of a decl. of −30°. Each area in the
sky was covered approximately 12 times from 2009 to 2015 in
each of five broadband filters (grizy reaching a single epoch
depth of approximately 22.0, 21.8, 21.5, 20.9, 19.7, respec-
tively). If Planet Nine was detected by PS1, it would appear as
a single night transient in each detection. To search for Planet
Nine, we will search for collections of single night transients
which appear at locations consistent with a Keplerian motion
moving on an orbit within the range of parameters predicted by
Brown & Batygin (2021).
Directly querying the PS1 catalog for single night transients
is not possible, so to construct our list of single night transients,
we begin by using the PS1 CasJobs SQL server
3
to download
every detection of every cataloged object for which there were
fewer than 12 detections, with at least one of those detections in
the g,r,oribands which would be most sensitive to a solar-
colored Planet Nine. Downloading these data in small batches
required several months of continuous querying of the server.
Many of these collections of up to 12 detections will be real
stationary astrophysical sources that appear at the same location
on multiple nights. Thus, after downloading, we discarded any
object for which the detections of a cataloged object occurred
on more than a single date. Over the Planet Nine search region
—defined as the decl. range over which 99% of the Planet Nine
synthetic population occurs—we find 1.26 billion single night
objects, most of which consist of a single detection in a night,
but a small number consisting of between 2 and 11 detections
at a single location in one night.
The vast majority of single night transients in PS1 at the faint
end are not real astrophysical sources, but rather arise from
systematic noise (Chambers et al. 2016). Our goal is to find any
set of transients that appear to follow a Keplerian orbit
consistent with that predicted for Planet Nine over the five year
period of the data. Finding such a set in the background of a
billion bad detections is formidable problem. We thus explore
ways to remove at least some of these bad detections. Any
method which removes bad detections has the possibility of
removing real detections also, thus we develop a method of
calibration to take this possibility into account in the next
section.
To better understand the characteristics of real single night
transients, we extract any detections within the data set of the
first 501,090 numbered asteroids by selecting every detection
within 2″and 2 mag of an asteroid’s predicted position and
brightness at each moment of observation. We have high
confidence that those detections are nearly all real, as chance
alignments on the scale of 2″on individual PS1 exposures are
rare, despite the abundance of spurious detections. This sample
probes the full magnitude range of potential PS1 detections
including the faintest end where the asteroids detected are those
which were initially discovered at closer distances and were
thus brighter but are now more distant and can have brightness
at and well beyond the PS1 magnitude limit.
We examine these 6.21 million asteroid detections detections
and find that we can use two parameters extracted from the PS1
database, PSFCHI2 and PSFQF (measures of the fit of the
detection to the point-spread function (PSF)and of the total
coverage of the detection to the PSF), to help eliminate some of
the most likely false positives. We make a simple cut and
include only detections with PSFQF greater than 0.99 and, for
objects with a magnitude less than 16, PSFCHI2 between 0 and
8, and for fainter objects, PSFCHI2 between 0 and 1.6. We also
reject all objects with a reported magnitude fainter than 22.5 as
this magnitude is both beyond the stated depth of PS1 and our
asteroid detections quickly drop before this magnitude. These
simple cuts reduce the number of objects detected as single
night transients to 772 million.
Significant spatial structure still exists in the locations of the
remaining objects. A plot of the position of each object on the
imaging array shows that each of the 60 individual detectors
has distinct regions with greatly increased numbers of detected
objects. We create individual masks for each of the 60 detectors
by hand, and discard all objects within these masked regions.
This masking reduces the number of objects under considera-
tion to 428 million.
Another source for large number of clustered objects is
scattered light from bright stars. We use the ATLAS REFCAT2
star catalog (Tonry et al. 2018)to examine the positions of all
bright stars in the data. Stars fainter than about r∼13 have
limited issues with scattered light, but brighter stars are
surrounded by clusters of objects. The radius of these clusters
increases with the brightness of the star. We empirically define
a bright star exclusion radius, r
e
, based on m
r
, the rmagnitude
in REFCAT2, as
()=+ -
r
m50 0.08 13
er
4
where r
e
is in arcseconds. After excluding these objects,
314 million objects remain.
Even with detector masking and regions around bright stars
excluded, clusters of objects can sometimes be found
associated with individual image frames. We find that these
can be effectively identified by using a clustering algorithm.
Specifically, we remove all collections of five or more objects
occurring within 40″of each other. This final cut reduces our
data set to 244 million objects.
We have reduced our original data set from 1.2 billion to
244 million objects, a decrease of 80%. While searching for
Planet Nine through this large data set remains a formidable
task, we could find no additional filters that appeared to safely
further reduce the data set. Any one of these cuts in the data has
the possibility of removing real detections of Planet Nine. Our
calibration method must by necessity take this possibility into
account.
3
http://mastweb.stsci.edu/ps1casjobs/
2
The Astronomical Journal, 167:146 (7pp), 2024 April Brown, Holman, & Batygin
3. Calibration
The footprints and depths of individual PS1 pointings are not
easily available, thus we use the method developed in BB22 to
self-calibrate the data set. In short, we use the asteroids
identified above as probes of both the coverage and depth of
PS1 on individual nights.
We use the JPL Horizons system
4
to calculate the positions
and magnitudes of the first 501,090 numbered asteroids for
each night of the 5 yr PS1 survey. We interpolate the positions
to the time of each image taken during a night and keep a log of
which asteroids are and which are not detected each night and
of what their predicted Vmagnitude for that night was. We
record this information in ∼1.8 square degrees patches of the
sky using an NSIDE =32 HEALPix grid.
5
A typical HEALPix
cell near the ecliptic has hundreds of asteroid detections per
night and even at the maximum distance from the ecliptic
searched, many asteroids are available. From this data set we
now have a nightly record of asteroid detections and
nondetections as a function of predicted magnitude within
each HEALPix footprint that we can use to calibrate the
detectability of Planet Nine on any night at any position. Note
that we exclusively work in predicted V-band magnitude,
ignoring the actual filter used for each observation. This
method is equivalent to assuming that Planet Nine has the same
color as the average asteroid. The effects of other color
assumptions are generally small and discussed in Belyakov
et al. (2022).BB22 examined the use of predicted asteroids
magnitudes as calibrators and found that there were no
systematic offsets between the asteroid predictions and
measured brightnesses, and that the rms deviation between
predictions and measurements is 0.2 mag. We thus conclude
that our self-calibration with asteroids is accurate to approxi-
mately this level. Note that these asteroid extractions are
performed after all of the data filtering discussed above, thus
they correctly account for any real detections that would have
been inadvertently removed in the filtering.
We next make use of the Planet Nine reference population
of BB22.BB22 created a sample of 100,000 potential Planets
Nine drawn from the statistical model of BB21 for the orbital
parameters and mass of Planet Nine. They assumed a simple
mass–diameter relationship of r
9
=(m
9
/3)R
earth
, where m
9
is in
Earth masses and R
earth
is the mass of the Earth, based on fits to
planets in this radius and mass range (Wu & Lithwick 2013),
and assume albedos from half that of Neptune, 0.2, to a value
predicted by a model in which all absorbers are condensed out
of the atmosphere (Fortney et al. 2016), 0.75. Each Planet Nine
was assigned a mean anomaly on a reference date of 2018 June
1, so their position, distance from the Sun, and brightness can
be predicted for any night. For each member of this reference
population, we calculate the position of the the body for each
night of the PS1 survey, determine the apparent magnitude of
the body (ignoring the small contribution from phase effects),
and calculate the HEALPix grid point in which it would appear.
We then use the record of asteroids detected and not detected
on that night in that grid point to determine the probability that
Planet Nine with its predicted magnitude would have been
detected on that night. We then randomly select a number
between 0 and 1 and, if that number is lower than the detection
probability, we record a detection of a member of the reference
population at that position—with an astrometric offset
randomly applied based on the reported uncertainties of
asteroids of the same magnitude—and magnitude. Note that
we do not explicitly consider whether Planet Nine would be
detected in a specific exposure on a night, but just whether it
would have been detected on any exposure that night and thus
result in a transient object in our PS1 database. We embed these
reference population detections into our PS1 data and use them
for the ultimate calibration of the survey. The PS1 survey is
extremely sensitive to the predicted range of potential Planets
Nine. Of the 100,000 members of the reference population,
88736 would be detected at least once, with 69,082 detected
nine times or more. The variable night-to-night detection limit
at the faint end is ultimately responsible for the stochastic
nature of detections of the faintest members of the population
(as would be the case for detection of the real Planet Nine at
this magnitude).
4. Orbit Linking
Examining 244 million detections of transient objects made
over a 5 yr period to find any set of objects consistent with
Keplerian motion is a computational intensive task. A variation
of the algorithm developed by Holman et al. (2018)and
implemented in BB22 greatly speeds this process. The process,
described in detail in BB22, begins with the simplifying
realization that, when viewed from the Sun, Keplerian orbits
travel in simple great circles across the sky. At the large
distance of Planet Nine, the motions are essentially a constant
velocity over long time spans.
To determine if a transient object is part of a collection of
objects consistent with a Keplerian orbit, the algorithm takes
the object, assumes a range of heliocentric distances for this
object, and transforms all other detected transient objects from
their observed geocentric R.A. and decl. to their heliocentric
longitude and latitude as if they were at the assumed
heliocentric distance and observed from the Sun. Any real
detection of a solar system body on a distant Keplerian orbit
will now appear as a collection of transient objects on a great
circle at different dates, separated by a constant angular speed
between the detections. To search for such a collection, angular
velocity vectors are calculated from the initial transient object
to every other transient object in the heliocentric system. A real
detection will now appear as a cluster of objects with similar
angular velocity vectors. A small spread in angular velocity
vectors occurs even for a real detection owing to the
discrepancy between the assumed and true distance of the
object, but can also occur because the collection of objects is
spurious and due to chance and does not precisely conform to a
Keplerian orbit. Thus for every cluster of objects (with a cluster
size larger than a given number threshold, discussed below),
the cluster of objects (plus the original object)is fit to a full
Keplerian orbit with the algorithm of Bernstein & Khushalani
(2000), and the astrometric residuals are determined. If the
residuals are below a given threshold, the set of objects is
retained as a candidate for a detection of a real object with
Keplerian motion.
For the PS1 data, we implement this algorithm on the
latitudinal swath of sky that contains 99% of the reference
population at each longitude. We further restrict the analysis to
motions and distances consistent with this reference population.
Distant objects which exist but do not fit the Planet Nine
hypothesis will therefore not be found in this analysis. Faster
4
ssd.jpl.nasa.gov
5
https://healpix.jpl.nasa.gov
3
The Astronomical Journal, 167:146 (7pp), 2024 April Brown, Holman, & Batygin
linking algorithms or increased processing power will be
required before such a larger analysis is possible.
Several choices must be made for the analysis algorithm,
including the spacing of assumed heliocentric distances, the
size of an angular velocity box to be used to identify clusters of
potentially linked objects, the threshold for the minimum
number of objects to consider a link, and the astrometric
threshold to retain the linkage as a true candidate. These
choices involve a complex set of trade offs. For example, a
wider spacing of assumed heliocentric distances leads to fewer
geometric transforms but also to the need to use a larger box in
angular velocity for identifying clusters, as the discrepancy
between the true and assumed distance causes the spread of
angular velocities to increase. The larger angular velocity box
causes more spurious clusters which must be checked with the
full Keplerian fitting, drastically slowing the search. The most
efficient trade off is a function of the number density of
detections on the sky. Similarly, the threshold for the number
of objects required to be within a cluster before full Keplerian
orbit fitting is a critical parameter. Requiring a large number of
objects to be clustered before considering the cluster for
Keplerian fitting greatly speeds the processing speed at the
expense of potentially missing the real Planet Nine if it is
detected a smaller number of times. A lower threshold, in
contrast, is computationally intensive and also leads to larger
numbers of false positive linkages.
In all cases we choose these parameters in the same manner
as they were chosen in BB22, by simulating different spacing
for our assumed distances and different sizes for our angular
velocity cluster box sizes in an attempt to minimize the
processing time. While BB22 recalculated parameters at each
location on the sky, here we simplify the analysis and use a
single angular velocity cluster box width of 0 052 day
−1
in
longitude and 0 026 day
−1
in latitude along with a constant
spacing of our assumed distances in inverse heliocentric
distance of Δ(1/r)=10
−5
au
−1
. We empirically find that these
parameters come close to optimizing processing time while, as
will be demonstrated below, also finding all possible linkages.
The final parameter to be selected is the minimum number of
transient detections to be required to be considered a linked
Keplerian orbit. In BB22 we required seven detections over a
3 yr period. The PS1 data has a significantly higher number
density of (mostly spurious)objects on the sky, such that if we
require only 7 detections we are overwhelmed with false
linkages. We find that requiring nine detections both improves
the processing speed and brings the number of false positives to
an acceptably low number.
With these parameters in place we can now visualize
approximate limiting magnitudes for the possible detection of
P9 in the PS1 survey. We use our asteroid database to calculate
the magnitude at which an object would be detected on 9 or
more distinct dates in a HEALPix grid cell 95% of the time
(Figure 1). Assuming that our processing can efficiently link all
such objects, the median magnitude limit for the detection of
Planet Nine for a region outside of the galactic plane is
V=21.0. The northern galactic plane region has some area of
coverage but has considerably worse limits, while the southern
galactic plane region has almost no usable data.
5. Results
The processing of the data was performed in 3°×3°blocks
across the sky. Enough overlap was included among the blocks
to account for the fastest potential motion of P9 across the 5 yr
period of the data. Even with approximately 50 blocks running
in parallel the full data set required several months of
continuous processing time.
The catalog of simulated reference population detections was
included in the processing, which had no knowledge whether
the detection was from the real PS1 data or an artificially
injected member of the reference population. Of the 69,082
members of the reference population which had 9 or more
detections, 68,550 are correctly linked, for a success rate of
99.2%. We consider this a strong demonstration that, if a Planet
Figure 1. The V-band magnitude at which there is a 95% or higher probability that a moving object would be detected 9 or more times in the portion of the PS1 data
that intersects the predicted locations of P9. The data are shown in a Mollweide equal area projection in equatorial coordinates. R.A. of 360 is on the left with 180 in
the middle and 0 on the right. The ecliptic is indicated by a line, as well as galactic latitudes of ±15°.
4
The Astronomical Journal, 167:146 (7pp), 2024 April Brown, Holman, & Batygin
Nine candidate consistent with the predicted parameters
of BB21 existed in the PS1 data set and was detected nine
times or more, it would be efficiently discovered by our
algorithm.
In the full data set, 909 additional linkages are made. Each
one of them is a chance linkage between multiple members of
the reference population and a small number of fortuitously
placed real PS1 detections (note that the algorithm allows
multiple linkages between objects, so in each case the true
linkage of the objects belonging to the reference population is
still correctly made).
We conclude that no object with the predicted characteristics
of P9 was detected nine or more times in the PS1 data set. The
magnitude limits shown in Figure 1provide a good approx-
imation to the search limit, with the caveat that the search is
only sensitive to objects with orbital characteristics similar to
those predicted by BB21.
The reference population provides an effective method for
combining the limits from the PS1 data with those already
achieved by ZTF and DES. Of the 69,802 members of the
reference population detected, 17,054 were unique to PS1. The
total fraction of the reference population that has been ruled out
by the combination of ZTF, DES, and PS1 is 78%. In Figure 2
we show an approximation of the full magnitude limit of the
three combined surveys by examining each HEALPix grid
point that contains four or more members of the reference
population and setting the limit equal to the faintest object
detected brighter than the brightest nondetection. When all
objects in the grid point are detected we show the magnitude of
the faintest object. Each of the surveys has regions of unique
contribution. PS1 uniquely detects 17% of the reference
population, mostly at high galactic latitudes at depths fainter
than ZTF. ZTF uniquely detects 6%, predominantly in the
northern galactic plane where PS1 coverage is poor. And DES
uniquely detects 3% of the population at magnitudes fainter
than PS1 covers. The remaining 52% of the detected population
is detected by two or more surveys, most often ZTF+PS1 for
the brighter objects at high galactic latitudes.
As is apparent, the combined surveys have a step-wise
efficiency with magnitude (Figure 3). The combined survey is
about 94% efficient for objects brighter than V=20.5, falling
to a 50% efficient at V=21.5. A large tail of detectable objects
as faint as V=23.5, exclusively from the DES observations,
extends at ∼20% efficiency. Many of the missing bright objects
are in the unobserved low decl. regions or the poorly observed
southern galactic plane. If these regions are excluded, the bright
object efficiency increases to 97% and 99.7%, respectively.
There is reason to believe that P9 would not be found at these
locations: a full fit of planetary ephemerides and search for
gravitational perturbations due to P9 suggests that a P9 near
Figure 3. The fraction of the BB22 Planet Nine reference population that
would have been detected by the ZTF, DES, and PS1 surveys, as a function of
V-band magnitude. The combined surveys are extremely efficient for objects
fainter than V∼21 and reach 50% efficiency at V=21.5, with a large tail of
fainter detections from the deep but narrow DES survey.
Figure 2. The combined V-band magnitude limits of the ZTF, DES, and PS1 suerveys for Planet Nine, reconstructed from detections of the synthetic reference
population. The geometry is the same as Figure 1. Note that the sky area is smaller than in Figure 1because we require a minimum of 4 members of the reference
population to be present to estimate a limit. The deep DES survey on the far right has magnitude limits that extend as far as 24. The areas in dark blue remain
unsurveyed.
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The Astronomical Journal, 167:146 (7pp), 2024 April Brown, Holman, & Batygin
perihelion—as these southern hemisphere positions are—
would have already been detected (Fienga et al. 2020). How
best to combine these constraints with those derived here
remains uncertain, however.
6. Discussion
The combination of the ZTF, DES, and PS1 surveys rules
out 78% of the BB22 P9 reference population. The sky
positions of the remaining members of the Reference
Population are shown in Figure 4. Using the members of the
Reference Population yet to be ruled out, we update our
predictions for P9 parameters. We report the median with the
15.8 and 84.1 percentile values as the uncertainties. Our newly
updated estimates include a semimajor axis of -
+
5
00 120
170 au, a
mass of -
+
Å
M6.6 1.7
2.6 , an aphelion distance of -
+
630 170
290 au, a
current distance of -
+
5
50 180
250 au, and a Vmagnitude of -
+
2
2.0 1.4
1.1.
The other predicted parameters remain generally unchanged.
All parameters and distributions of the Planet Nine reference
population, including flags for whether the objects would have
been found by ZTF, by DES, or by PS1, can be found
permanently archived
6
.
The remaining areas of P9 parameter space that remain
unexplored, shown in Figure 4, include a large swath near the
northern galactic plane where P9 is near aphelion and thus
would be fainter than the current limits and a smaller region in
the part of the southern galactic plane that remains poorly
covered as well as a strip below a decl. of −30°. Much of this
remaining parameter space will be covered by the upcoming
Vera Rubin Observatory survey, which will be sensitive to all
but the faintest and most northern predicted positions.
While a large majority of the phase space for Planet Nine
predicted by BB21 has now been ruled out, significant regions
still remain unobserved to the needed depth. Nonetheless, it is
worth considering potential reasons why P9 was not found in
the first 78% of parameter space surveyed. An obvious
possibility, of course, is that Planet Nine does not exist. Such
an explanation would require new explanations for multiple
phenomena observed in the outer solar system. Until such
explanations are available, we continue to regard Planet Nine
as the most likely hypothesis. Belyakov et al. (2022)explore
the effects of different assumed colors, albedos, and radii for
Planet Nine, and show that different choices of these
parameters can change the amount of phase space covered by
only of order ∼10%. A potentially much larger effect would be
a change in source region of the objects which become
clustered by Planet Nine. In BB21, the assumption is made that
the objects being observed are sourced from an early extended
scattered disk. Batygin & Brown (2021)instead consider the
effects of Planet Nine on objects pulled in from the inner Oort
cloud and conclude that a similar clustering is observed for
these objects but that the width of the cluster is broader.
In BB21, the breadth of the cluster is directly related to the
mass of Planet Nine and the broad cluster observed in the
distant solar system is used to infer a lower mass, lower
semimajor axis Planet Nine. If, instead, the observed breadth is
caused by an inner Oort cloud source population for the
clustered objects, the true Planet Nine could be more massive
and more distant, making it potentially much fainter and harder
to find. More work is required to explore this alternative
version of the Planet Nine hypothesis.
Acknowledgments
The Pan-STARRS1 Surveys (PS1)andthePS1public
science archive have been made possible through contribu-
tions by the Institute for Astronomy, the University of
Hawaii, the Pan-STARRS Project Office, the Max Planck
Society and its participating institutes, the Max Planck
Institute for Astronomy, Heidelberg, the Max Planck Institute
for Extraterrestrial Physics, Garching, The Johns Hopkins
University, Durham University, the University of Edinburgh,
the Queen’s University Belfast, the Harvard-Smithsonian
Center for Astrophysics, the Las Cumbres Observatory
Figure 4. The probability density function of on-sky location of the BB22 Planet Nine reference population that would remain undetected after the ZTF, DES, and
PS1 surveys. The geometry is the same as Figure 1.
6
https://data.caltech.edu/records/8fjad-x7y61
6
The Astronomical Journal, 167:146 (7pp), 2024 April Brown, Holman, & Batygin
Global Telescope Network Incorporated, the National Central
University of Taiwan, the Space Telescope Science Institute,
the National Aeronautics and Space Administration under
grant No. NNX08AR22G issued through the Planetary
Science Division of the NASA Science Mission Directorate,
the National Science Foundation grant No. AST-1238877,
the University of Maryland, Eotvos Lorand University
(ELTE), the Los Alamos National Laboratory, and the
Gordon and Betty Moore Foundation.
ORCID iDs
Michael E. Brown https://orcid.org/0000-0002-8255-0545
Matthew J. Holman https://orcid.org/0000-0002-1139-4880
Konstantin Batygin https://orcid.org/0000-0002-7094-7908
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