$\langle$reception date$\rangle$ $\langle$acception date$\rangle$
$\langle$publication date$\rangle$

11affiliationtext: University of Occupational and Environmental Health, Japan,
1–1 Iseigaoka, Yahata, Kitakyusyu 807–8555, Japan22affiliationtext: Planetary
Exploration Research Center, Chiba Institute of Technology, 2–17–1 Tsudanuma,
Narashino, 275–0016, Chiba, Japan33affiliationtext: Chofu headquarters, Japan
Aerospace Exploration Agency, Jindaiji Higashimachi 7–44–1, Chofu, Tokyo
182–0012, Japan44affiliationtext: Center for Computational Astrophysics,
National Astronomical Observatory of Japan, Osawa 2–21–1, Mitaka, Tokyo
181–8588, Japan55affiliationtext: ISAS, Japan Aerospace Exploration Agency,
Yoshinodai 3–1–1, Sagamihara, Kanagawa 252–0222, Japan66affiliationtext:
Department of Astronomy, University of Science and Technology of China, Hefei
230026, China77affiliationtext: Division of Science, National Astronomical
Observatory of Japan, Osawa 2–21–1, Mitaka, Tokyo 181–8588,
Japan88affiliationtext: College of Science and Engineering, Chubu University,
1200 Matsumoto–cho, Kasugai, 487–8501, Aichi, Japan99affiliationtext:
Southwest Research Institute, 1050 Walnut Street, Boulder, CO 80302,
USA1010affiliationtext: National Research Council of Canada, Herzberg
Astronomy and Astrophysics Research Centre, 5071 W. Saanich Rd., Victoria, BC,
V9E 2E7, Canada1111affiliationtext: Department of Physics and Astronomy,
University of Victoria, Elliott Building, 3800 Finnerty Road, Victoria, BC,
V8P 5C2, Canada1212affiliationtext: Planetary Science Institute, 1700 East
Fort Lowell, Suite 106, Tucson, AZ 85719, USA1313affiliationtext: Department
of Astronomy, University of Virginia, P.O. Box 400325, Charlottesville, VA
22904–4325, USA

Solar System1 — small bodies2 — KBOs3

# A deep analysis for New Horizons’ KBO search images

Fumi Yoshida1,2,∗<EMAIL_ADDRESS>Toshifumi Yanagisawa3 Takashi
Ito4,2,8 Hirohisa Kurosaki3 Makoto Yoshikawa5 Kohki Kamiya3 Ji-an Jiang6,7
Alan Stern9 Wesley C. Fraser10,11 Susan D. Benecchi12 Anne J. Verbiscer13,9

###### Abstract

Observation datasets acquired by the Hyper Suprime-Cam (HSC) on the Subaru
Telescope for NASA’s New Horizons mission target search were analyzed through
a method devised by JAXA. The method makes use of Field Programmable Gate
arrays and was originally used to detect fast-moving objects such as space
debris or near-Earth asteroids. Here we present an application of the method
to detect slow-moving Kuiper Belt Objects (KBOs) in the New Horizons target
search observations. A cadence that takes continuous images of one HSC field
of view for half a night fits the method well. The observations for the New
Horizons Kuiper Belt Extended Mission (NH/KEM) using HSC began in May 2020,
and are ongoing. Here we show our result of the analysis of the dataset
acquired from May 2020 through June 2021 that have already passed the
proprietary period and are open to the public. We detected 84 KBO candidates
in the June 2020 and June 2021 datasets, when the observation field was close
to opposition.

##  1 Introduction

Trans-Neptunian Objects (TNOs) are a diverse group of small bodies that exist
beyond Neptune’s orbit. The first discovered TNO was Pluto. Since the
discovery of other objects in this region (Jewitt and Luu, 1993), people began
to realize the existence of a swarm of small bodies and dwarf planets in a
belt-like zone beyond Neptune’s orbit. The objects in this belt with semimajor
axis $\sim$30–50 au are called the Kuiper Belt Objects (KBOs). KBOs are
roughly divided into four distinct dynamical groups: resonant KBOs, classical
KBOs, scattered (or scattering) disk objects, and detached objects. The
resonant KBOs are trapped in various mean motion resonances with Neptune. The
classical KBOs are not in strong mean motion resonance with Neptune, and their
orbits are overall nearly circular. KBOs with perihelion distances larger than
30 au and with large eccentricities are the scattered disk objects (SDOs). The
detached objects are those that have larger perihelion distances than the SDOs
and are less affected by Neptune’s gravity. For additional details on the
classification and nomenclature of KBOs, consult Gladman et al. (2008).

To unravel the dynamical structure of the solar system beyond Neptune and to
understand the physical processes that formed this structure, it is crucial to
discover more objects and to better characterize their physical properties.
For this purpose, both ground-based observations and spacecraft exploration
are necessary; they are complementary to each other. Only a few spacecraft
have traversed the Kuiper Belt; however, NASA’s New Horizons is the only
spacecraft that has directly observed KBOs at close range and is currently
operational.

New Horizons is a mission designed for flyby exploration of Pluto and other
KBOs (Stern and Spencer, 2003). The spacecraft was launched in January 2006,
reached the Pluto system in July 2015, and then made a close flyby of one of
the classical KBOs, (486958) Arrokoth, in January 2019 (Stern et al., 2015,
2019). After the Pluto flyby, the mission entered extended phases during which
the spacecraft observed many KBOs as point sources (Porter et al., 2016;
Verbiscer et al., 2019, 2022) while the team searched for a flyby target to
follow Arrokoth (Stern et al., 2018). From 2004–2014, the science team used
the Subaru Telescope’s Suprime-Cam (SC: the predecessor of HSC; Miyazaki et
al. (2002)) to search for the initial post-Pluto flyby candidate (Buie et al.,
2024). In May 2020, the science team resumed the ground-based survey
observations to search for the next flyby target as well as for KBOs
observable from the spacecraft. One of the major facilities used for this
purpose is the Subaru Telescope (https://subarutelescope.org/en/), and it was
at this time when members from Japan participated in the science team and
began playing roles. One of the main objectives of the science team, in
particular with the participation of Japan, is to exploit the Subaru Telescope
and its wide field camera, Hyper Suprime-Cam (hereafter HSC), to discover more
KBOs for the spacecraft to observe, either as point sources or at high
resolution during a close flyby.

Currently, the spacecraft is beyond the so-called Kuiper cliff (the outer edge
of the Kuiper Belt) which has been proposed to be located at $\sim 50$ au from
the Sun (e.g. Chiang and Brown, 1999; de la Fuente Marcos and de la Fuente
Marcos, 2024). The ground-based observations that started in May 2020 have not
yet found any objects that the New Horizons spacecraft can reach for a close
flyby. However, the data obtained from the ground-based observations to date
is by no means useless because the images contain many KBOs with substantial
scientific importance. The purpose of the present paper lies here — we
describe our effort to exploit the ground-based observational data and present
our initial detections as well as our methods. In general, detecting KBOs from
ground-based observations is a formidable task because of their faintness and
the complicating effects of background star confusion. Different analysis
methods can yield somewhat different outcomes even if they are based on the
same observational data. In this sense, our present analysis, which is
independent of but complementary to the effort of the main New Horizons team
in North America, is significant because it provides an independent, objective
view of the dataset and a chance to recover objects missed through other
search efforts.

In Section 2 we give specific information on the ground-based observations
that resulted in the dataset we use for this study. In Section 3 we describe
in detail how we detect moving objects in the dataset. Section 4 is devoted to
describing how we connect the orbits of a moving object over multiple
observation nights. Section 5 provides a discussion and summary of this study.

##  2 Observation dataset and reduction

All of the observation datasets whose results are presented here were obtained
using HSC on the Subaru Telescope. We select two fields of view (hereafter
referred to as F1 and F2) that include New Horizon’s flight path. At each
observation opportunity, one of the fields is imaged continuously for half a
night principally with a constant exposure time of 90 seconds. On the next
night, the other field is imaged in the same manner. This procedure is
followed throughout the entire observation period. After an interval of $\sim
30$ days, whenever possible, another observation is repeated in the same way
to extend the observation arc. This refines and enhances the accuracy of orbit
determination of the detected objects.

In Table LABEL:tbl:obslog, we summarize the details of the observational
datasets obtained for the New Horizons mission since May 2020; these
observations are publicly available as of September 2023. Among them, we
analyzed the 14 sets (marked with $\surd$ in the leftmost column of the
table). This table lists, from the left, which dataset we used ($\surd$),
observation date (UT), reduction ID (red. id), start time of the first and the
last exposures, position of the field of view (RA2000, DEC2000), field id
(either F1 or F2), filter, exposure time (s), number of acquired images, and
associated information on the night condition. Note that the observation
datasets listed in Table LABEL:tbl:obslog are all beyond the proprietary
limits of the original observers and, are publicly available the SMOKA public
archive site. See Baba et al. (2002) for more details on SMOKA or visit their
website, https://smoka.nao.ac.jp/. Note also that Table LABEL:tbl:obslog is
also available in the Excel format as Supplementary Data.

HSC is a huge mosaic CCD camera mounted on the prime focus of the Subaru
Telescope. HSC has 116 CCDs with a pixel size of 15 $\mu$m and pixel scale of
$0.168^{\prime\prime}$. Among the 116 CCDs, 104 of them are used for
scientific observation, 4 are for the auto guider, and 8 are for focus
monitoring (note that one of the 104 chips for scientific observation is not
functional as of 2024 April). HSC is designed to conduct large-scale sky
surveys with significant depth and high precision with a large field of view
of $\sim 1^{\circ}.5$ diameter. The image quality across the field of view has
a median seeing size of $\sim 0.6^{\prime\prime}$ in the $i$-band (Aihara et
al., 2018a, b, 2019, 2022). For technical details of HSC, see Miyazaki et al.
(2018) for its system design, Komiyama et al. (2018) for its camera dewar
design, Kawanomoto et al. (2018) for its filter system, and Furusawa et al.
(2018) for its on-site quality-assurance system. A dedicated website is also
available: https://www.subarutelescope.org/Observing/Instruments/HSC/.

Most of the observations were acquired on half nights; a few were acquired on
quarter nights. We used the $r2$ filter except on 2020 August 26 when we used
the $z$-band filter due to a request from the observatory. The sky condition
described in Table LABEL:tbl:obslog was transcribed from the observation night
log that the Subaru Telescope operators reported. This is included because it
helps us to evaluate the quality of the acquired data.

For the acquired data, standard image reduction procedures including bias,
dark, flat, and fringe corrections, as well as astrometric and photometric
calibrations against the Pan-STARRS1 (PS1) $3\pi$ catalog (Tonry et al., 2012;
Magnier et al., 2013) were carried out using the HSC pipeline, a version of
the Large Synoptic Survey Telescope (LSST) stack (Ivezić et al., 2019; Axelrod
et al., 2010). We refer the reader to Aihara et al. (2018a) and Jiang et al.
(2020) for further details on the HSC data reduction.

This study only used 14 of the 22 datasets listed in Table LABEL:tbl:obslog
for two major reasons. First, although $\sim$100 images of the same field were
continuously taken each night, we did not find a sequence of 32 images with a
constant time interval (e.g. the set with Reduction ID $03068$) which, as
mentioned in Section 3, our detection method requires; therefore, we cannot
apply it to these irregular image sequences. Currently, dealing with this
issue is beyond the scope of this work. There are several possible causes for
the temporal inconsistency in the image sequences, including refocusing the
telescope according to changing weather conditions, or interruptions to the
observation sequence due to unexpected instrumental trouble during an
exposure, resulting in irregular time intervals between images.

Moreover, even if a sequence of 32 images with a constant time interval is
available, if more than half of the images were taken under bad sky conditions
with poor seeing, our detection method fails. This can happen at any time.
However, unlike the first issue (i.e. the inconsistencies due to
considerations of telescope operations), we believe we can address the second
issue in the near future by considering seeing differences in individual image
subtractions. We expect this will come at the cost of a shallower limiting
magnitude, but more of the dataset will be usable. We discuss more about this
modification in Section 5.

Table 1:  Description of the observational datasets obtained for the New Horizons mission conducted at the Subaru Telescope with HSC from May 2020 through June 2021. Among the 22 datasets, we analyzed 14 sets identified by $\surd$ in the leftmost column. This table is also available in the Excel format as Supplementary Data. | DATEOBS | red. | start time | start time | RA2000 | DEC2000 | field | filter | exp | # |
---|---|---|---|---|---|---|---|---|---|---|---
| (UT) | id | of the first | of the last | (deg) | (deg) | id |  | time | of | night condition
|  |  | exposure | exposure |  |  |  |  | (s) | images |
| 2020/05/26 | 03068 | 10:37:11.962 | 13:19:52.429 | 287.376 | $-20.227$ | F1 | $r2$ | 90 | 80 | (Sky) cloudy, (Seeing) 0.7–1.2$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 5.7–7.0C, (Wind) 1.0–7.4 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 0.5–19.9%
$\surd$ | 2020/05/28 | 03070 | 10:42:33.966 | 13:22:08.568 | 288.740 | $-20.584$ | F2 | $r2$ | 90 | 78 | (Sky) cloudy, (Seeing) 0.7–0.9$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 5.4–6.3C, (Wind) 1.9–13.2 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 3.7–14.0%
$\surd$ | 2020/05/29 | 03071 | 10:42:54.138 | 14:50:41.399 | 287.338 | $-20.127$ | F1 | $r2$ | 90 | 124 | (Sky) clear, (Seeing) 0.5–0.9$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 4.8–5.4C, (Wind) 1.9–15.4 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 6.9–31.5%
$\surd$ | 2020/05/30 | 03072 | 10:31:55.756 | 14:50:04.288 | 288.714 | $-20.380$ | F2 | $r2$ | 90 | 128 | (Sky) clear, (Seeing) 0.7–1.2$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 3.6–5.9C, (Wind) 2.5–15.4 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 17.5–28.5%
$\surd$ | 2020/05/31 | 03073 | 10:34:52.068 | 14:49:22.538 | 287.312 | $-20.129$ | F1 | $r2$ | 90 | 128 | (Sky) clear, (Seeing) 0.6–0.9$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 3.4–5.5C, (Wind) 2.7–12.5 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 7.4–17.2%
| 2020/06/01 | 03074 | 10:22:19.511 | 14:37:33.060 | 288.688 | $-20.382$ | F2 | $r2$ | 90 | 62 | (Sky) clear, (Seeing) 0.55–0.9$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 3.0–6.2C, (Wind) 2.5–17.8 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 29.2–85.6%
| 2020/06/19 | 03092 | 10:31:59.346 | 14:56:30.976 | 287.145 | $-20.042$ | F1 | $r2$ | 90 | 131 | (Sky) cloudy, (Seeing) 0.9–1.6$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 2.0–3.6C, (Wind) 2.2–19.2 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 5.3–9.7%
$\surd$ | 2020/06/20 | 03093 | 10:55:39.175 | 14:55:24.839 | 288.662 | $-20.049$ | F2 | $r2$ | 90 | 118 | (Sky) high clouds, (Seeing) 0.7–1.1$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 6.9–7.6C, (Wind) 1.9–7.7 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 2.2–24.4%
$\surd$ | 2020/06/21 | 03094 | 10:52:52.432 | 14:41:24.850 | 287.112 | $-20.059$ | F1 | $r2$ | 90 | 114 | (Sky) clear, (Seeing) 0.7–1.0$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 5.2–6.8C, (Wind) 6.8–21.6m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 2.6–4.8%
$\surd$ | 2020/06/22 | 03095 | 10:46:30.525 | 14:10:33.646 | 288.629 | $-20.054$ | F2 | $r2$ | 90 | 102 | (Sky) thin cirrus, (Seeing) 0.56–0.7$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 6.2–7.4C, (Wind) 5.5–13.7m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 1.5–3.3%
$\surd$ | 2020/06/24 | 03097 | 10:26:47.483 | 14:47:51.861 | 287.062 | $-20.064$ | F1 | $r2$ | 90 | 130 | (Sky) clear, (Seeing) 0.66–0.8$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 3.3–6.2C, (Wind) 1.9–13.7 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 3.6–8.0%
$\surd$ | 2020/06/25 | 03098 | 10:44:42.581 | 14:44:40.567 | 288.578 | $-20.060$ | F2 | $r2$ | 90 | 120 | (Sky) clear, (Seeing) 0.65–2.0$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 3.1–6.4C, (Wind) 2.0–12.4 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 2.3–12.9%
$\surd$ | 2020/08/12 | 03146 | 06:23:26.824 | 10:17:44.506 | 286.251 | $-20.165$ | F1 | $r2$ | 90 | 117 | (Sky) clear, (Seeing) 0.7–1.1$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 1.4–4.6C, (Wind) 1.9–6.6 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 13.5–28.6%
$\surd$ | 2020/08/13 | 03147 | 05:49:38.806 | 10:08:23.645 | 287.676 | $-20.123$ | F2 | $r2$ | 90 | 129 | (Sky) clear, (Seeing) 0.7–1.1$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 1.8–4.7C, (Wind) 1.9–13.6 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 27.8–58.8%
$\surd$ | 2020/08/14 | 03148 | 05:47:57.363 | 10:05:59.358 | 286.223 | $-20.169$ | F1 | $r2$ | 90 | 128 | (Sky) clear, (Seeing) 0.5–1.2$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 2.9–3.2C, (Wind) 2.2–6.7 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 39.9–43.9%
| 2020/08/15 | 03149 | 05:49:37.187 | 10:05:39.643 | 287.743 | $-20.164$ | F2 | $r2$ | 90 | 122 | (Sky) clear, (Seeing) 0.6–1.3$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 3.2–4.6C, (Wind) 1.9–9.0 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 17.0–35.7%
| 2020/08/25 | 03159 | 05:48:22.166 | 10:01:32.822 | 286.085 | $-20.177$ | F1 | $r2$ | 90 | 124 | (Sky) clear, (Seeing) 1.0–1.5$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 1.1–2.7C, (Wind) 5.1–18.0 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 5.3–32.1%
| 2020/08/26 | 03160 | 05:41:09.488 | 10:13:26.988 | 286.073 | $-20.179$ | F1 | $z$ | 90 | 134 | (Sky) clear, (Seeing) 1.4–3.5$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 3.0–3.9C, (Wind) 1.9–16.9 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 12.2–17.0%
| 2020/10/10 | 03205 | 05:04:30.794 | 06:45:04.382 | 287.226 | $-20.105$ | F2 | $r2$ | 90 | 51 | (Sky) cirrus, (Seeing) 0.4–1.2$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 1.6–4.0C, (Wind) 2.0–15.7 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 7.0–76.6%
| 2020/10/12 | 03207 | 04:53:27.086 | 06:55:31.830 | 287.227 | $-20.105$ | F2 | $r2$ | 90 | 61 | (Sky) clear to overcast, (Seeing) 0.6–1.1$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 6.0–6.8C, (Wind) 3.8–12.4 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 5.7–7.8%
$\surd$ | 2021/06/09 | 03447 | 10:35:44.164 | 14:43:46.423 | 288.550 | $-21.100$ | F2 | r2 | 90 | 122 | (Sky) clear, (Seeing) 0.58–1.15$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) 1.2–3.7C, (Wind) 6.9–16.9m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 9.0–21.5%
$\surd$ | 2021/06/17 | 03455 | 10:27:09.891 | 13:53:45.662 | 288.450 | $-21.100$ | F2 | r2 | 90 | 93 | (Sky) clear to overcast, (Seeing) 0.28–2.91$\arcsec$
|  |  |  |  |  |  |  |  |  |  | (Temp) $-2.7$–$-0.6$C, (Wind) 2.0–10.8 m/s
|  |  |  |  |  |  |  |  |  |  | (Humid) 51.8–90.5%

##  3 Detection of moving objects

The datasets described in Section 2 are well-suited for the moving object
detection method that our group has developed (e.g. Yanagisawa et al., 2005,
2021), the JAXA (Japan Aerospace Exploration Agency) detection method. The
JAXA detection method superimposes images based on an assumed motion of moving
objects possibly embedded in the images. When a moving object is embedded in a
sequence of images, and if its motion vector is consistent with the assumed
trajectory, we can make the object appear much brighter and more visible (i.e.
increasing the signal to noise ratio) by superimposing images together with
median filtering.

This method has mainly been used for detecting space debris around the Earth
and faint near-Earth objects that are not visible in a single image (e.g.
Yanagisawa and Kurosaki, 2012; Yanagisawa et al., 2015). Detection of such
objects is difficult not only because many of these are faint, but also
because their on-sky velocity is sometimes very large (e.g. Ito and Malhotra,
2010). On the other hand, in this study, we attempt to apply this detection
method to find KBOs whose motion is much slower than that of space debris or
near-Earth objects. The efficiency of this method has recently been improved
by implementing binarization of images and hard-coding the algorithm on an
FPGA board. This method employs 32 images of the same field taken at a
constant, equal time interval. The series of observational datasets acquired
for New Horizons described in Section 2 largely fulfills the condition. Note
that in this study, we define binarization as an image processing technique
that converts a grayscale image into an image containing only two pixel
values.

In what follows we briefly describe how this method works. We summarized the
flow of the tasks in Figure 1 with the supporting illustration in Figures 2
and 3. From the series of images acquired on a single night, we select a set
of 32 images obtained with a constant time interval. Note that we select the
32 images out of the entire $\sim 100$ images per night so that the time
length covered by the selected images is as long as possible during the
observation night while maintaining a fixed time interval. Then we carry out
the following tasks (1–11).

  1. 1.

The HSC pipeline (Bosch et al., 2018) carries out bias subtraction, flat
correction, and sky subtraction.

  2. 2.

Since the pointing of the telescope varies slightly (or is off) from image to
image, we align the images to each other.

  3. 3.

We create a median image of the 32 aligned images. We subtract the median
image from each of the 32 images.

  4. 4.

We mask each image with the mask patterns created from the median image. We
set the masking threshold at 10 times higher than the average noise values.

  5. 5.

We create the self-flat images through the $\sigma$-clipping procedure
(https://www.gnu.org/software/gnuastro/manual/html_node/Sigma-clipping.html)
as well as by smoothing each image whose mask pattern has been corrected. We
then divide each image by its self-flat image. This task removes the sky level
pattern that varies with atmospheric turbulence.

  6. 6.

We search for moving object candidates in the masked images. For this task and
the next, we introduce a parameter named the shape parameter (hereafter
denoted as $P_{\mathrm{shape}}$). In a $3\times 3$ pixel set that contains a
light source, $P_{\mathrm{shape}}$ is defined as the value obtained through an
operation of dividing the sum of the values of the $3\times 3=9$ pixels around
the peak of the light source (including the peak itself) by the peak pixel
value (see Figure 2 and the explanation about it below). When
$P_{\mathrm{shape}}\sim 1$, only the central pixel has an exceptionally high
value (the so-called “hot pixel”). This type of light source is probably
caused by dead pixels, noise, or cosmic rays. When $P_{\mathrm{shape}}\gg 1$,
we regard it as an object candidate at the position of an extended light
source (i.e. a light source with the PSF having a finite extension).

Here is the specific procedure to search for moving object candidates. There
are a large number of light sources in the images. Among them, we extract
about 60,000 light sources with the shape parameters greater than 2 (i.e.
$P_{\mathrm{shape}}>2$). Figure 2a is a schematic illustration of one of these
light sources centered at a $3\times 3$ pixel set. Let us assume that these
nine pixels have the three-dimensional coordinates, $(x,y,z)$, where $(x,y)$
are the coordinates of each pixel in the image, and $z$ contains their pixel
values. Figure 2a illustrates a $3\times 3$ pixel set on a single image that
has $P_{\mathrm{shape}}>2$ (i.e. a moving object candidate). There are many
other light sources like this, and in this way we collect $\sim$60,000 of
$3\times 3$ pixel sets in each of the 32 image sets. The number 60,000 is
empirically, but carefully, selected to maximize the efficiency of the JAXA
detection method (Yanagisawa et al., 2021). If the number is too small, we
would detect only bright moving objects. If the number is too large, we would
detect many false positives. Then we sort the $\sim$60,000 light sources by
their central pixel value (i.e. the value of the pixel no. 5 in Figure 2b),
and record their $(x,y)$ coordinates. Most of the light sources are noise that
random pattern of the sky background creates, but there may be true moving
objects embedded among them.

  7. 7.

Using the $\sim$60,000 moving object candidates selected in the previous task,
we create binarized images for each of the 32 image sets. Specifically, for
each of the $\sim$60,000 candidates, we calculate the sum value of each
$2\times 2$ pixel set that contains the pixel of the object candidate. As
shown in Figure 2b, there are four $2\times 2$ pixel sets for each of the
object candidates. Then we locate which of the $2\times 2$ pixel set has the
largest sum value, and set the values of its $2\times 2=4$ pixels to 1. As a
result, the above procedure sets the pixel value of all the $2\times 2$ pixel
sets of the $\sim$60,000 moving object candidates to 1. In other words, about
$2\times 2\times 60,000=240,000$ pixels have the value of 1 in a single image.
This completes the binarization of a single image. All subsequent tasks use a
set of 32 images binarized in this way to detect moving objects.

Figure 2 is a schematic illustration of how we binarize a $2\times 2$ pixel
set. We calculate the sum of the pixel values in the four $2\times 2$ pixel
sets: $\left({1\;2}\atop{4\;5}\right)$, $\left({2\;3}\atop{5\;6}\right)$,
$\left({4\;5}\atop{7\;8}\right)$, and $\left({5\;6}\atop{8\;9}\right)$. If the
sum of the pixel values of the $2\times 2$ pixel set
$\left({1\;2}\atop{4\;5}\right)$ is the highest among them, we set their pixel
values to 1. We repeat this operation for all the $\sim$60,000 candidates on
an FPGA board.

  8. 8.

Assuming a motion vector of the moving object population we want to detect
(such as KBOs, main belt asteroids, near-Earth objects, and so on), we
superimpose the binarized images with many shift patterns with $dx$ and $dy$
where ($x,y)$ are the coordinates on the images. Both $dx$ and $dy$ range from
$-256,-255,\ldots,+256$, and in total we have $(256+1+256)^{2}=263,169$
different shifts. This operation is done in parallel on an FPGA board.

A slightly more specific description as to what is done in this task is
illustrated in Figure 3. From each of the 32 images (from $i=1$ to 32 in
Figure 3), we crop a region of a certain size to fit the assumed position and
velocity of a moving object and produce an intermediate image as shown in the
middle panels. We stack the 32 intermediate images and create their median
image. If the 32 intermediate images were shifted and cropped along the exact
position and velocity of a moving object, the position of the moving object in
the each intermediate image remains at the same place of the cropped images.
Therefore, if we stack all 32 intermediate images and create their median
image, only the moving object will remain in the median image (rightmost
cloumn of Figure 3).

  9. 9.

In the previous task, some moving objects are detected more than once with
different (but similar) shift patterns, in particular when the objects are
bright. We think this is caused by bright objects tend to have extended shapes
on the image. The peak pixel of a bright object is often surrounded by the
pixels whose values are also large. This results in ambiguity in determining
the peak location of a bright object. As a result, the motion vector of a
bright object can be estimated in more than one shift pattern.

In such cases, we use the image from which we removed any hot pixels in task 6
(referred to as the hot-pixel-removed image) to determine the true detection
event. More specifically, we examine the peak value in the median image
created using the hot-pixel-removed images for each event and consider the
event with the largest peak value to be the true detection. We interpret the
shift value that causes the true detection event as the amount of the apparent
motion of the detected (real) object.

  10. 10.

By assuming the velocity of a moving object, and by shifting the image in many
directions, the superimposed images generated from task 8 allow us to find a
faint moving object that could not be seen on a single image. The superimposed
images are checked by human eyes to determine whether they contain real
objects or not (“human inspection” or “vetting”). We show a pair of examples
of the moving object detection on actual images in Figure 4 (a clean example)
and in Figure 5 (a false detection example caused by a noise spike; the noise
in each image is superimposed on each other by chance). Clean detections and
false detections caused by noise spikes are generally distinguished as
follows. When an actual moving object is detected as in Figure 4, its light
source is spread out on the image and multiple pixels have large values. On
the other hand, in Figure 5, only the peak pixel has a large value, which is
considered to be a false detection caused by a noise spike, not the light
source of the actual object.

  11. 11.

For each of the detected objects, we calculate its coordinates on the CCD chip
(referred to as the CCD coordinates). Using the matching result with the Pan-
STARRS1 PS1 $3\pi$ catalog described in Section 2, we convert the CCD
coordinates into the equatorial coordinates.

Tasks 7 and 8 are carried out on an FPGA board. Typical analysis of 32 2K
$\times$ 4K images with the 104 CCD chips of HSC (for the science use out of
the total 116 chips) takes about 140 minutes. In this study, we processed 14
datasets listed in Table LABEL:tbl:obslog with a total runtime of $140\times
14=1960$ minutes, or $\sim 1.36$ day.

Figure 1: Flowchart of the object detection procedures. The starting box is
“Taking many images” at the top left. The numbers in blue denote the task
number described in the main text.  Figure 2: Schematic illustration of how we
binarize a $2\times 2$ pixel set in the tasks 6 and 7. Note that this
illustration is just about one light source (i.e. one moving object candidate)
on a single image. Panel a shows the pixel values of the nine pixels around
the light source and their positions in the three-dimensional space,
$(x,y,z)$. $(x,y)$ are the coordinates of the pixels on the image, and $z$
contains their pixel values. Panel b shows the top view of the panel a with
the numbers on each pixel from 1 to 9. We calculate the sum values of each of
the $2\times 2$ pixel sets (shown in the dotted lines; 4 sets in total). Panel
c shows the $2\times 2$ pixel set whose sum of the pixel values are the
largest, and we set their pixel values to 1. We set the value of all other
pixels $(3,6,7,8,9)$ to 0. Figure 3: Schematic illustration of task 8
(cropping and stacking images). Time proceeds from the top to the bottom of
the figure. The index $i$ (from 1 to 32) denotes the number among the 32
images we used for the detection procedure. The coordinate axis $({x},{y})$ at
the middle left of the figure schematically represent the coordinates on the
images.

As previously mentioned, the algorithm described in this section was
originally developed to detect space debris and near-Earth objects. This
algorithm requires a constant time interval between each image acquisition.
When we observe near-Earth objects, the typical exposure time of an image is
relatively short, $\sim$ 20 seconds. Therefore, a set of observations for
several tens of images takes 20–30 minutes. Thus, the use of a constant time
interval throughout an entire set of observations is easily achieved. On the
other hand, the KBO observations at the Subaru Telescope may not continue
image acquisition with a constant time interval because: refocusing is
frequently performed, changes in weather conditions may interrupt the
observations, and so on. The number of images to be superimposed in our method
is currently limited to 32 due to the restriction of the hard-coded algorithm
embedded on FPGA (Yanagisawa et al., 2021). Since we cannot easily modify the
code implemented on the FPGA, we prepare the 32 images with a constant time
interval by deleting or duplicating some images when any irregular events
occurred during the observation.

Table 2 shows the summary of our detections. The number of moving object
candidates detected from the whole dataset described in Table 2 is 6980. Among
them, human vetting excluded 2641 candidates. The remainder is
$6980-2641=4339$ robust candidate detections of moving objects. The detection
limit through our method is 26.0–26.5 magnitudes (see Section 4). The average
detection limit of a single image with the exposure time of 90 seconds using
HSC is 24–25 magnitudes (Aihara et al., 2018b). Therefore we can say that
JAXA’s superposition method is more capable of detecting fainter moving
objects than ordinary detection methods using a single HSC image. In Figure 6
we show the frequency distribution of the apparent magnitude of the actual
moving objects detected in this study. We suspect that the faintest end in the
distribution ($\sim$26.5 magnitude) indicates the detection limit of the
dataset with our current method.

Figure 4: An example set of visual inspection images that includes a moving object with little noise (the images are from the actual observation dataset). The 32 square images in the panel a are part of the original 32 images that include the detected moving objects. Panels b, c, d, and e represent the combination of 4, 8, 16, and 32 images with the median filter, respectively.  Figure 5: An example set of visual inspection images of a false detection caused by noise spike that does not include any moving objects (the images are from the actual observation dataset). See the caption of Figure 4 for the meaning of each panel. The light source at the center of the panel e is probably created by the overlap of high background noise.  Table 2: Observation date (UT), Reduction ID, field, and the ratio between $N_{\mathrm{real}}$ (the number of real objects) and $N_{\mathrm{detec}}$ (the number of detections). Date (UT) | red. id | field | $N_{\mathrm{real}}/N_{\mathrm{detec}}$
---|---|---|---
2020 May 28 | 03070 | F2 | $279/435$
2020 May 29 | 03071 | F1 | $689/1042$
2020 May 30 | 03072 | F2 | $727/918$
2020 May 31 | 03073 | F1 | $316/404$
2020 June 20 | 03093 | F2 | $54/198$
2020 June 21 | 03094 | F1 | $77/207$
2020 June 22 | 03095 | F2 | $286/457$
2020 June 24 | 03097 | F1 | $151/227$
2020 June 25 | 03098 | F2 | $316/459$
2020 August 12 | 03146 | F1 | $251/354$
2020 August 13 | 03147 | F2 | $448/712$
2020 August 14 | 03148 | F1 | $495/1066$
2021 June 09 | 03447 | F2 | $41/91$
2021 June 17 | 03455 | F2 | $209/410$
Figure 6: Apparent magnitude distribution of the moving objects that we
detected in this study over the entire observation period. Note that we found
five objects in the bin between 19.0 and 19.5 magnitude, which are outside of
the plot range.

##  4 Linking orbits

In general, categorizing a moving object into one of the small body
populations (such as KBOs) through their apparent velocity on the celestial
sphere is possible only when the images are obtained near opposition (e.g.
Yoshida et al., 2003). Although our observational datasets range from May 2020
to June 2021, only the datasets acquired in June 2020 and June 2021 are along
the direction of the opposition. We therefore searched for KBO candidates
among the datasets obtained in June 2020 and June 2021. In 2020, we also
obtained datasets in May and August. Thus, we examined whether the KBO
candidates detected in the datasets obtained in June 2020 were also detected
in the datasets obtained in May and August of that year. We did it to improve
the accuracy of our orbit determination by identifying KBO candidates over as
long an observational arc as we could.

In Figure 7 we show the apparent velocity distribution of moving objects
detected in the datasets acquired in June 2020 and June 2021 when the survey
areas were close to opposition. The population of moving objects with a peak
velocity $v$ distribution at $v\sim 13$ arcmin/day are main belt asteroids.
The objects having a velocity peak at 8–9 arcmin/day correspond to the L5
swarm of the Jupiter Trojans. They coincidentally overlapped with our
observation fields in June 2021. The population with the peak velocity
distribution of $v\lesssim 3.2$ arcmin/day (or $v\lesssim 8$ arcsec/hour, e.g.
Yoshida and Nakamura, 2007, their Fig. 5) corresponds to KBOs. In total, 84
KBO candidates were detected. The observation in June 2020 was the closest to
the opposition, so we extended the observation arc backward and forward using
the June 2020 observation as the reference. We have completed the
identification of the KBO candidates for the observation period of May, June,
and August 2020, and June 2021. In Figure 8 we show the apparent magnitude
distribution of the KBO candidates detected in the June 2020 and June 2021
datasets when the survey areas were close to opposition.

Figure 7: Apparent velocity distribution of the moving objects detected in the
observational datasets obtained in June 2020 and June 2021.  Figure 8:
Apparent magnitude distribution of the KBO candidates detected in the June
2020 and June 2021 datasets.

In what follows we describe how we link the orbits of objects detected over
multiple nights. Using the method described in Section 3, we first calculate
the apparent velocity $v$ of all the detected moving objects on the celestial
sphere. The velocities are determined from the values of the shift by the JAXA
detection method. We select KBO candidates from the detected moving objects
using a velocity criterion of $v\lesssim 3.2$ arcmin/day. For each KBO
candidate, we calculate the values of its equatorial coordinates at the
beginning and end of the single-night observation. Using this information, we
estimate their orbits and find objects whose orbits can be linked over
multiple nights. Here we use a software code called StellaHunter Professional.
The StellaHunter Professional is the commercial PC software manufactured by a
Japanese private company, AstroArts Co., Ltd (https://www.astroarts.co.jp/),
mainly for amateur astronomers to detect moving objects in the solar system
such as asteroids or comets from their observed images. This software can
determine the orbits of moving objects and find identical objects detected
over multiple nights. It has also implemented JAXA’s moving object detection
algorithm described in Section 3, and realizes the detection of very faint
objects. JAXA once provided the algorithm of the moving object detection to
AstroArts, and AstroArts realized its implementation and commercialized the
achievement as a software package
(https://www.astroarts.co.jp/products/stlhtp/index-j.shtml, the webpage is
written only in Japanese). However, currently, this software is no longer
available as a commercial product. Readers who are interested in using it may
want to contact the authors individually.

We try many different multi-night combinations for each of the KBO candidates
detected on each observation night and look for objects whose orbits could be
successfully linked. In what follows we itemize the tasks we conducted for
this purpose. See also Figure 9 for a schematic illustration.

  1. 1.

We determine the Keplerian orbit that connects the start position (S1 in image
1 in Figure 9) and the end position (E2 in image 2 in Figure 9) of an object
candidate over the combined nights. This orbit is referred to as orbit 1 in
Figure 9. Position E2 may not belong to this object, but at this point, we
presume it does.

  2. 2.

We derive the deviation (root-mean square, the so-called O–C residual) of the
positions E1 and S2 from orbit 1. We call this residual 1. Here we impose a
condition that residual 1 must be smaller than twice the resolution of HSC
(corresponding to the pixel scale of HSC), i.e. $<0.34^{\prime\prime}$. If
residual 1 is larger than this value, we exclude the possibility that
positions E2 and S2 belong to this particular object candidate and search for
another candidate.

  3. 3.

If there are other object candidates in image 2, we determine the Keplerian
orbit that connects the start position S1 and the end position of this
candidate (E2′ in image 2 in Figure 9) over the combined nights. This orbit is
referred to as orbit 2.

  4. 4.

We derive the O–C residual of the positions E1 and S2′ from Orbit 2. We call
it residual 2. We impose the same condition for the value of residual 2 as
that of residual 1, i.e. its value must be $<0.34^{\prime\prime}$.

  5. 5.

We see whether the residual 1 or residual 2 is larger.

     * •

If residual 1 is smaller than residual 2, we adopt orbit 1 as the orbit of the
object candidate that belongs to the positions S1–E1 (and –S2–E2).

     * •

If residual 1 is larger than residual 2, we adopt orbit 2 as the orbit of the
object candidate that belongs to the position S1–E1 (and –S2′–E2′).

  6. 6.

When we carry out the orbit linking through the method we have described,
sometimes the orbit of a KBO candidate (with low velocity) is linked to the
orbit of an object of a very different population (such as a main belt
asteroid). To avoid this issue, in this study, we adopt another condition for
successful orbit linking: the velocity and the magnitude of the two object
candidates that make up a multi-night combination should be close to each
other. The specific thresholds that we use are as follows: the relative
velocity difference of the two object candidates is less than $\pm 20$%, and
the difference in their apparent magnitudes is less than 2 mag.

As a result, we successfully linked six KBO orbits in 12 nights from May 2020
to August 2020. In addition, we successfully linked the orbit of another KBO
in two nights in June 2021. We estimate the orbital elements of these seven
($=6+1$) KBOs, and summarized their properties in Table 4. The meanings of the
numbers in the Detection ID in Table 4 are as follows: The first five digits
(such as 03071) correspond to the Reduction ID in Table LABEL:tbl:obslog. The
next three digits (such as 096) are the chip number of the CCD array of the
HSC. The last two digits (such as 02) are the sequential number of moving
object candidates in order of its brightness on each CCD chip. We numbered the
moving object candidates detected on each CCD chip in order of brightness. The
brightest object is numbered 1, the second brightest is numbered 2, and so on.
The orbit of the object at the bottom with the Detection IDs 03447–027–01 and
03455–027–02 was linked in two nights in June 2021. Here, 03447–027–01 denotes
an object which is the brightest on the CCD chip 027 in the Reduction ID 03447
(which corresponds to the observation on 2021 June 9 as shown in Table 2).
Similarly, 03455–027–02 denotes an object which is the second brightest on the
CCD chip 027 in the Reduction ID 03455 (which corresponds to the observation
on 2021 June 17).  Then we check whether they are newly discovered objects by
using MPChecker https://cgi.minorplanetcenter.net/cgi-bin/checkmp.cgi. We give
MPChecker the following two conditions: search radius = $5^{\prime}$, and
limiting magnitude $V=30.0$. MPChecker returns a report that the orbital
elements of these seven objects are not close to any of the known objects.
Thus, it is likely that they are newly discovered. We report the detected
position coordinates of these seven KBO candidates to the Minor Planet Center
(MPC). Amongst the seven objects, the MPC assigned provisional designations to
two objects that had detections for more than three nights and observation
arcs from May to August: 2020 KJ60 and 2020 KK60. We show the orbital elements
calculated by the MPC for these two objects in Tables 5 and 6. The following
information is also appended to this table: first detection date
($t_{\mathrm{detect}}$), Earth distance at $t_{\mathrm{detect}}$, and Sun
distance at $t_{\mathrm{detect}}$. We computed $t_{\mathrm{detect}}$ using
JPL’s Horizons System, https://ssd.jpl.nasa.gov/.

Figure 9: Schematic illustration of how we link an object’s orbit over multiple nights. We draw just two images $(1,2)$ for simplicity. S1 is the start position of an object candidate on the first night, E1 is its end position on the same night, S2 is the start position of one of the object candidates on the last night, and E2 is its end position on the same night. On the other hand, S2′ is the start position of another object candidate on the last night, and E2′ is its end position on the same night.  Table 3: Orbital properties of the KBOs detected in this study: DATEOBS (UT), Detection ID, Epoch, mean anomaly $M$ (deg), mean motion $n$ (deg/day), argument of perihelion $\omega$ (deg), semimajor axis $a$ (au), longitude of perihelion $\Omega$ (deg), eccentricity $e$, inclination $I$ (deg), orbital period $P$ (year), absolute magnitude $H$. All the values were calculated in this study (not through MPC). See the main text for the definition of Detection ID.  F1 |
---|---
DATEOBS(UT) | Detection ID | Epoch | $M$ | $n$ | $\omega$ | $a$ | $\Omega$ | $e$ | $I$ | $P$ | $H$
2020/05/29 | 03071–096–02 |  |  |  |  |  |  |  |  |  |
2020/06/21 | 03094–092–01 | 2020 May 31.0 | 11.28143 | 0.00220614 | 15.68541 | 58.4404403 | 242.23978 | 0.3964207 | 4.23433 | 446.77 | 8.53
2020/08/12 | 03146–093–03 |  |  |  |  |  |  |  |  |  |
2020/05/29 | 03071–080–02 | 2020 May 31.0 | 359.95345 | 0.00191703 | 90.65816 | 64.1775983 | 195.03697 | 0.3379206 | 2.68321 | 514.15 | 7.71
2020/06/24 | 03097–074–01 |  |  |  |  |  |  |  |  |  |
2020/05/29 | 03071–090–05 | 2020 May 31.0 | 179.82848 | 0.00706483 | 352.26576 | 26.8989165 | 113.28983 | 0.6167589 | 21.27055 | 139.51 | 9.61
2020/06/24 | 03097–085–01 |  |  |  |  |  |  |  |  |  |
2020/05/29 | 03071–094–11 | 2020 May 31.0 | 359.94102 | 0.00242844 | 124.95387 | 54.8173809 | 160.32114 | 0.2339671 | 3.52256 | 405.88 | 9.51
2020/06/24 | 03097–089–01 |  |  |  |  |  |  |  |  |  |
F2 |
2020/05/28 | 03070–066–06 |  |  |  |  |  |  |  |  |  |
2020/05/30 | 03072–050–07 | 2020 May 31.0 | 359.77854 | 0.00298581 | 132.00232 | 47.7633370 | 154.91643 | 0.0822065 | 2.56450 | 330.11 | 9.39
2020/08/13 | 03147–020–06 |  |  |  |  |  |  |  |  |  |
2020/06/25 | 03098–014–04 | 2020 May 31.0 | 359.71567 | 0.00383246 | 20.64859 | 40.4406149 | 266.42684 | 0.0025594 | 5.15591 | 257.18 | 10.19
2020/08/13 | 03147–014–06 |  |  |  |  |  |  |  |  |  |
2021/06/09 | 03447–027–01 | 2021 July 5.0 | 0.03091 | 0.00174529 | 147.31747 | 68.3215293 | 139.02814 | 0.3493611 | 1.83469 | 564.75 | 8.96
2021/06/17 | 03455–027–02 |  |  |  |  |  |  |  |  |  |
Table 5: Properties of 2020 KJ60. The values are taken from the MPC website except for first detection, Earth Distance at $t_{\mathrm{detect}}$, and Sun Distance at $t_{\mathrm{detect}}$ at the bottom. $t_{\mathrm{detect}}$ and $t_{\mathrm{detect}}$ were calculated through JPL’s Horizons System.  epoch | 2023-09-13.0
---|---
epoch JD | 2460200.5
perihelion date | 1984–01–18.13386
perihelion JD | 2445717.634
argument of perihelion (deg) | 69.3508
ascending node (deg) | 156.60392
inclination (deg) | 2.49898
eccentricity | 0.3752396
perihelion distance (au) | 37.2882832
semimajor axis (au) | 59.6841373
mean anomaly (deg) | 30.95781
absolute magnitude | 9.67
phase slope | 0.15
uncertainty | 9
observations used | 6
oppositions | 1
arc length (day) | 77
residual rms (arcsec) | 0.08
first observation date used | 2020–05–28.0
last observation date used | 2020–08–13.0
first detection ($t_{\mathrm{detect}}$, UTC) | 2020–05–28 11:18
Earth Distance at $t_{\mathrm{detect}}$ | 42.542 au
Sun Distance at $t_{\mathrm{detect}}$ | 43.328 au
Table 6: Same as Table 5, but about 2020 KK60.  epoch | 2023-09-13.0
---|---
epoch JD | 2460200.5
perihelion date | 2006–03–16.60277
perihelion JD | 2453811.103
argument of perihelion (deg) | 15.16474
ascending node (deg) | 242.43004
inclination (deg) | 4.24758
eccentricity | 0.3888881
perihelion distance (au) | 35.261981
semimajor axis (au) | 57.7013481
mean anomaly (deg) | 14.36763
absolute magnitude | 8.77
phase slope | 0.15
uncertainty | 9
observations used | 6
oppositions | 1
arc length (day) | 75
residual rms (arcsec) | 0.16
first observation date used | 2020–05–29.0
last observation date used | 2020–08–12.0
first detection ($t_{\mathrm{detect}}$, UTC) | 2020–05–29 10:44
Earth Distance at $t_{\mathrm{detect}}$ | 35.667 au
Sun Distance at $t_{\mathrm{detect}}$ | 36.474 au

##  5 Discussion

In this study, we analyzed the observational dataset originally acquired for
the New Horizons target search at the Subaru Telescope using JAXA’s moving
object detection method. Our analysis covers the datasets obtained between May
2020 and June 2021. However, the categorization of a moving object into a
unique solar system population using solely apparent velocity is only possible
when observations are executed near opposition. Therefore we paid particular
attention to the datasets acquired in June 2020 and in June 2021 when the
observation field was close to the opposition. Then we found 84 KBO
candidates.

The series of observations for New Horizons/KEM started in May 2020, and is
on-going. In this study, we only analyzed the datasets obtained between May
2020 and June 2021 because they have past the proprietary period and are
publicly available (note that the data proprietary period for the Subaru
Telescope is 18 months). More datasets acquired in this series of observations
will be released to the public in due course as the proprietary period
expires. We will continue to analyze additional data as they become available
in the archive. The primary goal of this effort is, of course, to find the
next in-situ observational candidate for the New Horizons spacecraft. Yet it
simultaneously leads to a more detailed understanding of the dynamical
structure of the deepest region of the solar system, particularly the orbital
distribution of the small bodies dominated by diverse resonances (e.g.
Saillenfest et al., 2019; Ito and Ohtsuka, 2019; Malhotra, 2019) and the
distribution of cometary objects in the inner part of the Oort Cloud (e.g.
Dones et al., 2015; Fouchard et al., 2018, 2023).

One of the unique perspectives of observing KBOs from a spacecraft flying
through the Kuiper Belt is that they can be observed with a significantly
larger solar phase angle, and from a much closer distance, compared with
ground-based or Earth-based observation. For example, the New Horizons
spacecraft observed the large classical KBO (50000) Quaoar at solar phase
angles of $51^{\circ}$, $66^{\circ}$, $84^{\circ}$ and $94^{\circ}$ (Verbiscer
et al., 2022). Ground-based observation can only provide data at small solar
phase angles ($\lesssim 2^{\circ}$). The combination of observations at large
and small solar phase angles provides us with knowledge of the surface
reflectance of the object and enables us to infer information about a KBO’s
surface properties in detail (e.g. Porter et al., 2016; Verbiscer et al.,
2018, 2019, 2022).

As we continue our work, we are aware that several issues need to be
addressed. First, the number of KBOs detected with our method as reported this
time may not be optimal, and it could have been even larger using the same
dataset. More specifically, we suspect the mask patterns that we used in this
study can be improved in the future. As explained in Section 3, we subtract
the median image from each image created across all the images. This process
should remove all the field stars, ideally leaving only moving objects.
However, in reality, the effect of incomplete subtraction remains due to the
difference in the seeing, mainly caused by atmospheric turbulence. To deal
with this problem, we applied the mask patterns to remove the effect of
incomplete subtraction. The values of the masked regions are set to 0, which
means the regions are useless for detecting moving objects. Since the observed
sky regions (F1 and F2) are crowded with field stars, the regions of the mask
patterns occupy almost 20% of the entire images. This could eliminate the
detection of KBOs that exist near those field stars. In the near future,
although it will require significant computational resources, we plan to use
the subtracted images and consider seeing differences in each image, and apply
them to our detection method. Partly for this purpose, we plan to execute our
detection method on a GPU cluster, which we expect will reduce the turn-around
time of the data analysis to $\sim 1/5$ or less compared with the present
(note that this is based on a test analysis obtained using Nvidia Quadro
RTX8000 $\times 4$).

The second issue is related to the efficiency of our object detection
procedure. As discussed in Section 3, our detection method still relies
heavily on visual inspection of the events at the end of the procedures
(“human vetting”, the task no. 10 in the flowchart in Figure 1). This becomes
a formidable task as the amount of data increases, and it is obvious that
sooner or later the workload from this task will exceed the limit of human
capability. Recently, use of machine learning has become commonplace in the
field of astronomy, in particular for detection of moving objects in the solar
system (e.g. Lin et al., 2018; Lee et al., 2022; Jia et al., 2023). We have
begun preparation to exploit the advantage of machine learning and will bring
a machine-learning framework into JAXA’s detection method in the near future
as a substitute for human vetting.

The third issue is about the number of superimposed images that JAXA’s
detection method uses. Currently, the method uses 32 images as a set (see
Section 3). This number can be increased to 64 by improving our software. The
result would be the ability to reach a much fainter (deeper) level; we are
preparing for this improvement now.

##  6 Acknowledgments

This research is based on data collected at the Subaru Telescope operated by
the National Astronomical Observatory of Japan (NAOJ), and the data is
obtained from SMOKA operated by Astronomy Data Center (ADC), NAOJ. We are
honored and grateful for the opportunity of observing the Universe from
Maunakea, which has the cultural, historical, and natural significance in
Hawaii. The observations were supported in part by NASA for the New Horizons
mission. In particular, NASA Keck exchange time was used to acquire the data
on 17 June 2021 (S21A–TE216–K). F. Y. acknowledges support from the Japan
Society for the Promotion of Science (JSPS) Kakenhi grant, 20H04617. J. J.
acknowledges support from the Japan Society for the Promotion of Science
(JSPS) Kakenhi grants, JP19K23456 and JP22K14069. A. V. acknowledges support
from JPL/RSA contract no. 1659537. StellaHunter Professional is a trademark of
AstroArts. This study has made use of NASA’s Astrophysics Data System (ADS)
Bibliographic Services.

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