# The Near-Earth Object Surveyor Mission

A. K. Mainzer University of Arizona, 1629 E University Blvd, Tucson, AZ
85721-0092, USA  Joseph R. Masiero Caltech/IPAC, Pasadena, CA, USA  Paul A.
Abell NASA Johnson Space Center, 2101 NASA Parkway, Houston TX 77058  J. M.
Bauer Dept. of Astronomy, Univ. of Maryland, College Park, MD  William Bottke
Southwest Research Institute, Boulder, CO, USA  Bonnie J. Buratti Jet
Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Sean J. Carey IPAC, California Institute of Technology, 1200 E. California
Blvd, Pasadena, CA 91125, USA  D. Cotto-Figueroa Department of Physics and
Electronics, University of Puerto Rico at Humacao, Call Box 860, Humacao, PR
00792, USA  R. M. Cutri  IPAC, Mail Code 100-22, California Institute of
Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA  D. Dahlen IPAC,
Mail Code 100-22, California Institute of Technology, 1200 E. California
Blvd., Pasadena, CA 91125, USA  Peter R. M. Eisenhardt  Jet Propulsion
Laboratory, California Institute of Technology, Pasadena, CA, USA  Y. R.
Fernandez University of Central Florida, Orlando, FL, USA  Roberto Furfaro
University of Arizona, 1629 E University Blvd, Tucson, AZ 85721-0092, USA
Tommy Grav University of Arizona, 1629 E University Blvd, Tucson, AZ
85721-0092, USA  T. L. Hoffman  Jet Propulsion Laboratory, California
Institute of Technology, Pasadena, CA, USA  Michael S. Kelley Planetary
Defense Coordination Office, NASA Headquarters, 300 E Street SW, Washington,
DC 20546  Yoonyoung Kim University of Arizona, 1629 E University Blvd, Tucson,
AZ 85721-0092, USA  J. Davy Kirkpatrick IPAC, Mail Code 100-22, California
Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
Christopher R. Lawler  Jet Propulsion Laboratory/California Institute of
Technology, Pasadena, CA, USA  Eva Lilly Planetary Science Institute, Tucson
AZ, USA  X. Liu  IPAC, Mail Code 100-22, California Institute of Technology,
1200 E. California Blvd., Pasadena, CA 91125, USA  Federico Marocco IPAC,
California Institute of Technology, 1200 E. California Blvd, Pasadena, CA
91125, USA  K. A. Marsh IPAC, California Institute of Technology, 1200 E.
California Blvd, Pasadena, CA 91125, USA  Frank J. Masci IPAC, California
Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
Craig W. McMurtry Physics and Astronomy Dept., University of Rochester, New
York 14627-0171 USA  Milad Pourrahmani IPAC, California Institute of
Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA  Lennon Reinhart
Space Dynamics Laboratory, University of Utah, Logan, UT, USA  Michael E.
Ressler Jet Propulsion Laboratory/Caltech, Pasadena, CA, USA  Akash Satpathy
University of Arizona, Tucson, AZ, USA  C. A. Schambeau Florida Space
Institute, University of Central Florida, Orlando, FL, USA; Department of
Physics, University of Central Florida, Orlando, FL, USA  S. Sonnett Planetary
Science Institute, Tucson AZ, USA  Timothy B. Spahr  NEO Sciences LLC,
Marlborough, MA, USA  Jason A. Surace IPAC, California Institute of
Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA  Mar Vaquero  Jet
Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
E. L. Wright University of California, Los Angeles, Los Angeles, CA, USA
Gregory R. Zengilowski University of Arizona, Tucson, AZ, USA

(Accepted October 16, 2023)

###### Abstract

The Near-Earth Object (NEO) Surveyor mission is a NASA observatory designed to
discover and characterize near-Earth asteroids and comets. The mission’s
primary objective is to find the majority of objects large enough to cause
severe regional impact damage ($>$140 m in effective spherical diameter)
within its five-year baseline survey. Operating at the Sun-Earth L1 Lagrange
point, the mission will survey to within 45 degrees of the Sun in an effort to
find the objects in the most Earth-like orbits. The survey cadence is
optimized to provide observational arcs long enough to reliably distinguish
near-Earth objects from more distant small bodies that cannot pose an impact
hazard. Over the course of its survey, NEO Surveyor will discover
$\sim$200,000 - 300,000 new NEOs down to sizes as small as $\sim$10 m and
thousands of comets, significantly improving our understanding of the
probability of an Earth impact over the next century.

††facilities: WISE, NEOWISE, IRSA, MPC††software: astropy (Astropy
Collaboration et al., 2022), scipy (Virtanen et al., 2020), numpy (Harris et
al., 2020), SPICE (Acton, 1996), spiceypy (Annex et al., 2020), Blackbird
(Lawler et al., 2020)

##  1 Introduction

Asteroid and comets have impacted the Earth for billions of years and will
continue to do so. The present-day impact flux is dominated by a supply of
asteroidal material that predominantly originates from the main belt between
Mars and Jupiter, typically migrating into near-Earth space via thermal drifts
that push the objects into gravitational resonances capable of changing orbits
on timescales of millions- to tens-of-millions of years (Bottke et al., 2002;
Granvik et al., 2018). Once objects evolve into orbits with perihelia less
than 1.3 au, they are classified as
NEOs.111https://cneos.jpl.nasa.gov/about/neo_groups.html Comets, both long-
and short-period, constitute a smaller fraction of NEOs, estimated to be
between 5-15% of the total (Wetherill, 1987, 1988; Bottke et al., 2002;
Fernández et al., 2005; DeMeo & Binzel, 2008; Kim et al., 2014; Bauer et al.,
2017; Granvik et al., 2018). The size distribution is such that material that
collides with Earth is dominated by tiny dust particles that produce harmless
“shooting stars”; roughly 100 tons of such material (thought to be primarily
cometary in origin) falls on Earth each day (Jenniskens, 2015; Nesvorný et
al., 2010).222https://cneos.jpl.nasa.gov/about/target_earth.html

Larger objects impact our planet infrequently. While such impacts are rare,
they are capable of causing significant damage locally (e.g. the fireball
explosion over Chelyabinsk, Russia in 2013 and the 1908 Tunguska blast; Brown
et al., 2013; Jenniskens et al., 2009) or globally (e.g. the Chixulub impact
66 Myr ago; Alvarez et al., 1980; Renne et al., 2013). The impact frequency as
a function of impactor size is reasonably well-understood on astronomical or
geological timescales, based on studies of the cratering records of the Earth
and Moon, as well as telescopic studies of the NEO population. Earth impacts
capable of causing global destruction are thought to occur every $\sim$100
million years; impacts capable of causing severe regional destruction might
occur every few thousand to tens of thousands of years; and impactors capable
of causing damage to a city occur perhaps 0.1 to several times each century
(Shoemaker et al., 1979; Shoemaker, 1983; Rabinowitz et al., 2000; Stuart &
Binzel, 2004; Mainzer et al., 2011b; Trilling et al., 2017; Granvik et al.,
2018; Harris & Chodas, 2021). Yet to predict what _will_ occur on human
timescales (over the next century or two) requires discovery of individual
objects, with their orbits determined to sufficient precision to assess
whether or not an impact is likely over that timescale.

Much of what we know about the population of NEOs derives from systematic
surveys for them, beginning with photographic plate surveys such as the
Palomar Planet-Crossing Asteroid Survey (Helin & Shoemaker, 1979). As charged-
coupled devices matured and were incorporated into asteroid searches, the pace
of NEO discovery increased due to the efforts of projects such as the
Spacewatch survey (McMillan, 2007), the Near-Earth Asteroid Tracking Program
(Helin et al., 1997; Pravdo et al., 1999), the Lowell Observatory Near-Earth
Object Survey (Koehn & Bowell, 2000), and the Lincoln Near-Earth Asteroid
Research Program (Stokes et al., 2000). Current NASA-supported NEO surveys
include the Catalina Sky Survey (Larson, 2007), PanSTARRS (Chambers et al.,
2016), the Zwicky Transient Facility (Masci et al., 2019), the Near-Earth
Object Wide-field Infrared Survey Explorer (NEOWISE) survey (Cutri et al.,
2012; Mainzer et al., 2011c, 2014), and the Asteroid Terrestrial-impact Last
Alert System (Tonry et al., 2018).

As a result of the community’s efforts, more than 90% of the population of 1
km and larger NEAs were believed to have been discovered (Mainzer et al.,
2011b; Granvik et al., 2018) by the 2010-2011 timeframe, fulfilling the
“Spaceguard” goal (Morrison, 1992) of finding the majority of these large
asteroids capable of causing global extinction events. The Spaceguard
objective did not cover comets.

Following the achievement of the Spaceguard goal, community consensus studies
(Stokes et al., 2003, 2017; Melosh et al., 2019) determined that asteroids
larger than 140 m in diameter should be the target of the next generation of
surveys because they are capable of causing severe regional destruction with
economic effects that would be felt globally; a 140 m impactor has the
equivalent energy of roughly 200 megatons of TNT. In 2005, the United States
Congress passed the George E. Brown, Jr. Near-Earth Object Survey Act
requiring that NASA detect more than 90% of all NEOs larger than 140 m in
diameter by the year 2020.333https://www.congress.gov/bill/109th-
congress/house-bill/1022/text Completing the survey of 140 m and larger NEOs
is a key component of the United States’ National Near-Earth Object
Preparedness Strategy and Action Plan (OSTP, 2018, 2023). At present, perhaps
40% of all such objects have been discovered with the current suite of surveys
(Mainzer et al., 2011b; Granvik et al., 2018). Discoveries are currently
dominated by the Catalina Sky Survey and PanSTARRS survey, which employ 1-2 m
class telescopes operating at visible wavelengths and discover $\sim$2500-3000
new NEOs per year. The average size of newly discovered NEOs is between 30-100
m; the current rate of discovery of larger objects is limited to $\sim$450 new
objects per year.444https://cneos.jpl.nasa.gov/stats/size.html

Not all NEOs pose equal hazards to the Earth: in particular, those with orbits
that approach within 0.05 au of Earth’s orbit have sufficient uncertainty in
their ephemerides over the next 80-100 years that impacts beyond that
timescale cannot be ruled out (Giorgini et al., 2002; Ostro & Giorgini, 2004).
Such objects are formally designated potentially hazardous asteroids (PHAs) if
they also have absolute magnitude $H<$22 mag (although others have argued for
a size-based definition for objects large enough to create a crater, e.g.
Mainzer et al., 2014). For our purposes in this paper, we include any object
with minimum orbit intersection distance (MOID) $<$0.05 au in the definition
of PHAs. NEOs are divided into different dynamical classes: Atiras are NEOs
with orbits entirely interior to Earth’s, with semi-major axes a$<$1.0 and
aphelia Q$<$0.983 au. Atens are defined as NEOs with a$<$1 au and Q$>$0.983
AU; Apollos have a$>$1 AU and perihelia distances q$<$1.017 AU. Amors spend
all of their time outside the orbit of the Earth, with a$>$1 au and
1.017$<$q$<$1.3 au (Belton et al., 2004). Thus, both Atens and Apollos have
orbits that cross Earth’s, but Atens tend to have the most circular, Earth-
like orbits. Amors generally have the least chance of making close approaches
to Earth. It is important to focus on finding PHAs since they have the
greatest potential for close Earth approaches.

The challenge of finding 90% of the _diameter-limited_ population of NEAs
larger than 140 m is exacerbated by the presence of objects with low-albedo,
carbonaceous material among the population. The survey of such objects
performed by the Wide-field Infrared Survey Explorer (WISE; Wright et al.,
2010) during the fully cryogenic part of its mission, which detected NEOs
based on their 12 $\mu$m thermal emission and was therefore largely
insensitive to variations in albedo, found that the NEO albedo distribution
remains relatively unchanged as a function of size over the range of diameters
probed by WISE ($\sim$200-300m and larger; Mainzer et al., 2011b). Wright et
al. (2016) showed that in order to discover 90% of the 140 m and larger
objects, surveys must reach an equivalent absolute magnitude completeness of
$H<$23 mag instead of the $H<$22 mag assumed using the approximation that all
NEOs have visible geometric albedos of $\sim$0.14. Thus, reaching the 90%
completeness limit specified by the George E. Brown Act requires discovering
the dark portion of the NEO population as well as the brighter objects with
silicate-dominated surfaces.

Finding PHAs well in advance of any potential close approaches is important
because successfully deflecting an impacting object likely requires years to
decades of lead time. The Double Asteroid Redirection Test (DART) mission has
recently succeeded in altering the orbit of an asteroid using the kinetic
impactor technique, following on the success of the Deep Impact (A’Hearn et
al., 2005) and Hayabusa2 (Watanabe et al., 2019) missions. However, such
kinetic deflections typically only work on sub-km-sized objects with
$\sim$5-50 years of warning, and km-sized bodies would require even more time
to deflect them successfully; see Figure 5.5 in NASEM (2010). For example, the
DART mission was able to impart a total velocity change of 3 mm/sec on its
target body, the 160 m diameter Dimorphos (a natural moon of the 765 m NEO
Didymos), using a $\sim$600 kg spacecraft (Cheng et al., 2016). Assuming a
spacecraft of similar mass as DART was sent to deflect an impactor, it would
take decades to deflect a 160 m object by the 6400 km radius of the Earth.
This time could be cut down by impacting with a more massive spacecraft, or
multiple spacecraft, but the time required for a successful kinetic deflection
is still likely to be in excess of 20 years. The mass of a larger object
scales with the cube of the diameter, which dramatically increases the
required deflection time. Therefore, it is essential to complete the survey of
objects large enough to cause at least severe regional damage as quickly as
possible. Maximizing warning time minimizes the energy required to deflect an
object and allows for the time needed to develop and mature the needed
mitigation mission(s) and precursors.

The project completed its Preliminary Design Review in September 2022. NEO
Surveyor was confirmed to enter Phase C in November 2022 and is scheduled for
launch in September 2027 (Hoffman et al., 2022). NEO Surveyor was recommended
as the top priority for planetary defense in the 2022 Planetary Decadal Survey
(NASEM, 2022) and in a previous consensus study paper (Melosh et al., 2019).

##  2 Mission Architecture

The NEO Surveyor mission is a space telescope dedicated to finding,
cataloging, and characterizing the PHAs, including the low albedo objects, as
well as long- and short-period comets. It is designed to make significant,
rapid progress toward the objective specified by the George E. Brown law. The
mission’s primary scientific requirement is to find the majority of PHAs
larger than 140 m within its five-year baseline mission. The observatory
consists of a 50-cm infrared telescope operating at two channels that are
dominated by thermal infrared emission for typical NEOs (Figure 1), which
typically have effective temperatures between 200-300 K throughout the
majority of their orbits (Figure 2). Channel NC1, spanning 4-5.2 $\mu$m, is
designed to detect background stars for obtaining astrometric registration and
calibration as well as for improving constraints on an object’s effective
temperature. Channel NC2 spans 6-10 $\mu$m in an effort to maximize
sensitivity to typical NEO thermal emission.

Figure 1: The NEO Surveyor mission consists of a single infrared instrument
operating at two infrared channels. The instrument is shielded from the Sun by
its sunshade, and the black-painted radiator surfaces on the back of the
instrument enable the mission to achieve its operating temperatures through
passive cooling. The NEO Surveyor mission’s 6.15 m sunshade enables it to look
down to solar elongations of 45∘ in order to more efficiently detect asteroids
in the most circular, Earth-like orbits. The instrument is shown on the right
with its one-time deployable aperture cover still attached.  Figure 2: The
spectral energy distribution for two different 140 m NEOs observed at 90∘
phase angle with visible geometric albedos of 0.03 and 0.17, with the NC1 and
NC2 bandpasses overplotted as the cyan and magenta patches, respectively.

The mission’s driving Level 1 requirement is to find 2/3 of PHAs larger than
140 m within its 5-year baseline mission. The mission has a goal of operating
for 12 years, which would give the best chance of achieving 90% completeness
on such objects, thus fulfilling the George E. Brown law. NEO Surveyor is also
required to constrain the impact frequency of smaller NEOs and the total
population of comets. In addition, the Observatory is required to provide the
capability to stop and collect additional follow up observations of targets of
interest, should an object be discovered with a particularly large chance of
impact. The 3-axis stabilized spacecraft is capable of stopping and
integrating on any individual target of interest in the instrument operable
zone. The mission’s concept of operations is designed to maximize discovery of
PHAs and comets. As described below, after operating for $\sim$10-12 years,
the Observatory has a reasonable chance of reaching 90% completeness for PHAs
$>$140 m, representing $\sim$200,000-300,000 NEOs down to small sizes.

Figure 3: The NEO Surveyor field of regard (gray cones) is shown compared to
that of NEOWISE (pink cones) and the orbit of the Earth (dashed line). The
simulated population of PHAs larger than 50 m at the time of discovery is
shown as blue dots. The NEOWISE field of regard is limited by its relatively
short sunshade and low-Earth orbit, which prohibits it from reaching the near-
Sun regions on the sky where NEOs are more likely to be found. NEO Surveyor’s
sunshade is tall enough to enable it to observe down to 45∘ from the Sun,
subtending a larger portion of the PHAs.

By entering into a halo orbit around the Sun-Earth L1 Lagrange point (SEL1),
the NEO Surveyor mission is able to continuously view a large segment of the
space around Earth’s orbit, particularly the near-Sun regions that are most
difficult to observe from the ground (Figure 3). The SEL1 halo orbit is
preferred over the L2 Lagrange point because the Moon does not impinge on the
field of regard as significantly, and the spacecraft’s communications antenna
does not have to be mounted on the solar panel/sunshade, simplifying the
design. Previous studies (Mainzer et al., 2015; Grav et al., 2016) indicated
that an SEL1 orbit yielded comparable performance to a circulating interior-
to-Earth or Venus-trailing orbits, even assuming that no efficiency losses
were incurred from the several orders-of-magnitude drop in data downlink rate
that would be a consequence of such orbits.

At SEL1, the Earth and Moon are sufficiently distant that their thermal
radiation onto the spacecraft can be managed purely passively. Combined with
the other thermal protections, this design allows the NEO Surveyor instrument
performance to be limited by the natural astrophysical background in the NC2
channel (predominantly zodiacal dust emission; Wright, 1998; Leinert et al.,
1998), with the telescope required to remain below 57 K. The NC2 detectors
must be maintained below 40 K (McMurtry et al., 2013; Dorn et al., 2016;
Zengilowski et al., 2021, 2022) to ensure that their dark currents do not
dominate over the minimum expected zodiacal background signal over the field
of regard. The detectors are nonlinearly sensitive to temperature, and the
temperature requirements are designed to limit heat input into the arrays. The
multi-stage radiator system includes shields to intercept heat from the solar
panel and spacecraft. The observatory’s sunshade blocks sunlight and carries
the solar panels. A shield underneath the instrument intercepts heat emitted
from the $\sim$300 K spacecraft bus, and three radiators coated with high-
emissivity black paint affixed to the back of the instrument reject heat from
the thermal shields, readout electronics, and detectors. The passive thermal
system does not require expendable cryogens or cryocoolers (Figure 1). The
thermal system employs industry-standard thermal margins and design practices
to stabilize the telescope, baffles, and focal plane temperatures (Hoffman et
al., 2022) and derives heritage from the Spitzer Space Telescope (Finley et
al., 2004; Finley, 2005; Lawrence & Finley, 2004).

From SEL1, it is possible to downlink at 150 Mbps using NASA’s 35-m Deep Space
Network dishes. This high-rate data link allows for the transmission of the
individual 30-second Exposures collected by the instrument in both NC1 and NC2
channels simultaneously that are used to construct each 180-second
observation.

The telescope optical system consists of a three-mirror anastigmat design that
provides a field of view of 1.68∘ x 7.08∘ with no central obscuration, thus
maximizing sensitivity and eliminating diffraction spikes due to the secondary
mirror support structure. There is no focus mechanism; focus is set through a
series of measurements made on the ground at the operating temperature that
account for the deformation of the optical system as it cools down. The same
field of view is imaged simultaneously by the two channels using a germanium
beamsplitter, with NC1 being reflected and NC2 transmitted through the
beamsplitter. Both channels utilize transmission filters to define their
bandpasses.

Each focal plane consists of a 4x1 array of 2048x2048-pixel HgCdTe detectors
that are closely packed together to minimize the gaps between individual
arrays that can cause loss of moving object detections and thus break links
between detections. The gap spacing between arrays is 2.4 mm for both
channels, with a plate scale of $\sim$3 arcsec per 18 $\mu$m pixel.

The instrument can safely point anywhere from 45-125∘ in elongation from the
Sun including both ecliptic poles; this is defined as the instrument operable
zone. The field of regard of the survey is a subset of this area, spanning
45-120∘ in longitudinal distance from the Sun and stopping at $\pm$40∘
ecliptic latitude (Figure 3). Calibration fields are measured at the ecliptic
poles once per week, since a large set of well-characterized calibration
targets are found in these regions (Reach et al., 2005; Jarrett et al., 2011;
Carey et al., 2012).

Calibration for NEO Surveyor focuses on two high level objectives, astrometric
accuracy of $<$0.5 arcseconds (1 sigma rms) for sources of SNR$\geq$20 and
photometric accuracy of better than 7% for bright (non photon-noise limited),
unsaturated point sources. These requirements help to ensure that the survey
produces data of sufficient quality to accurately measure orbits and diameters
of the population of near-Earth objects greater than 140 meters in size. Using
photometric calibration methods demonstrated with Spitzer and WISE and
astrometric calibration based on the Gaia astrometric frame of reference, the
absolute photometric calibration is expected to be better than 4% and the
astrometric accuracy for SNR$\geq$20 sources to be better than 0.3 arcsec per
detection.

The sensitivity of the Observatory varies over the sky due to the wide range
of solar longitudes covered by the field of regard. In both NC1 and NC2, the
sensitivity is dominated by the natural zodiacal background, which varies by
roughly a factor of 20 and 10 in NC1 and NC2, respectively. From a zodiacal
background model based on DIRBE measurements, we estimate the zodiacal
background to range from $\sim$220-4200 nWm-2sr-1 in NC1 and from
$\sim$1700-22,000 nWm-2sr-1 in NC2 across the NEO Surveyor field of regard
(Wright, 1998; Leinert et al., 1998). Since we plan to extract point sources
at a signal-to-noise ratio of 5 or greater, we quantify sensitivity as five
times the noise equivalent spectral irradiance (denoted $NESI_{5}$). The
sensitivity calculation takes into account the sampling of the NEO Surveyor
instrument’s point spread function, the integration times in both channels,
the telescope entrance pupil area, the thermal self-emission, stray light,
dark current, and other parameters. The $NESI_{5}$ is computed across the full
field of regard and is required to be within 65-120 $\mu$Jy in the NC1 channel
and 110-280 $\mu$Jy in the NC2 channel.

##  3 Concept of Operations

The mission’s science operations consist of a highly repetitive survey pattern
that is optimized for potentially hazardous object detection and is
continuously executed throughout the baseline mission. Each day, the survey
pattern is executed continuously, breaking only to downlink for a total of
2.25 hours/day, to perform calibrations, and occasionally to perform station-
keeping and momentum management maneuvers.

The essential unit of the survey is the _Visit_ , which consists of a set of
six dithered individual _Exposures_ , each 30 sec long and collected in a
roughly hexagonal pattern with a $\sim$10 arcsec step length (Figure 4). The
dither step size and number of steps is a configurable table that can be
adjusted in flight if needed. The six _Exposures_ are co-added together to
form each _Visit_ , and in order to measure the photometry of individual
sources, the stack of _Exposures_ is fit simultaneously as appropriate for
data taken with an undersampled system. A total of three minutes are allocated
per _Visit_ , with an integration time of $\sim$145 s in both bands. All
individual _Exposures_ are downlinked each day, facilitating improved artifact
and noise rejection, searches for very-fast-moving NEOs, and for so-called
“precovery” detections of moving objects discovered at a later time and
subsequently recovered in the NEO Surveyor images.

Figure 4: Overview of the survey pattern. (a) Each NEO Surveyor _Visit_
consists of six _Exposures_ collected in a hexagonal dither pattern. (b)
_Visits_ are repeated in a _Loop_ four times to make a _Quad_. (c) _Quads_ are
then tiled into _Stacks_ , then _Sides_ of the Sun (Orange line). This figure
shows two full _Sides_ during the survey, made up of three _Stacks_ each;
darker colors indicate the first _Quad_ in each stack. This pattern is then
repeated.

Within each _Exposure_ , the NC1 and NC2 arrays each have their own unique
clocking and readout schemes that are optimized to the astrophysical
background level anticipated in each channel (including zodiacal light
emission). NC1 collects 18 samples over the course of 26.6 sec in a “sample-
up-the-ramp” mode with a 25.1 sec total net integration time for the ramp; the
slope that is fitted to the samples is downlinked, forming a single
_Exposure_. Saturation detection is provided by the Zemcov et al. (2016)
algorithm that fits the slope to non-saturated portions of the ramp. For NC2,
eight individual correlated double sample (CDS) pairs of samples are collected
in 27 sec (using 2.90 sec integration time per CDS pair). The eight CDS pairs
are co-added using a single-sided outlier rejection algorithm to provide
resilience against cosmic rays, forming the _Exposure_ for that channel which
is downlinked. A single dither step is executed after each _Exposure_. For one
_Exposure_ in each dither sequence, the initial NC2 ‘pedestal’ frame
(nominally taken 50 msec after detector reset) of the first CDS pair is also
downlinked to provide a constraint on the photometry of sources that saturate
within 2.90 sec.

Each patch of sky in the field of regard is covered a total of four times over
$\sim$6-9 hours. The Observatory steps in the short dimension of the 4x1
detector array to maximize efficiency for a total of 17 _Visits_ before
stepping up or down in ecliptic latitude and moving across to form a _Loop_.
This looping pattern is repeated until four _Visits_ are collected at each
pointing on the sky (four _Loops_ are denoted a _Quad_). The pairs of
positions and times collected from detections harvested from _Visits_ in each
_Quad_ are linked together to form a “tracklet”. Next, the survey steps in
ecliptic latitude collecting successive _Quads_ until the full range from -40∘
to +40∘ is covered. This set of _Quads_ forms a _Stack_ ; two additional
_Stacks_ are collected before the Observatory flips over to the opposite side
of the sky. Each set of three _Stacks_ is called a _Side_ (see Figure 4). In
the event that spurious sources interfere with reliable linking of legitimate
detections of small bodies, it is possible to increase the number of _Loops_
performed to five with minimal impact to survey completeness for NEOs $>$140
m. All data processing to reduce the data, including extracting sources and
linking tracklets, is performed on the ground.

The detectors collect photons for 56.7% of the total time, with camera readout
overhead taking 11.7%, slews between dither steps taking 4.9%, slews between
_Visits_ taking 15.7%, and all other activities (including daily downlinks,
momentum management, and calibration pointings) taking 10.8% of the total
time. Over the course of the 5-year baseline mission, roughly 630,000 _Visits_
are collected.

NEO Surveyor will also be capable of executing targeted follow-up observations
(TFOs) in order to obtain more information on an object of special interest,
including refined astrometry and improved photometric characterization. The
TFOs will use the same hexagonal pattern for each _Visit_ , as in the nominal
survey mode, but the _Visit_ pattern will be repeated on a single field for
much longer to obtain up to $\sim$20x more exposures relative to a nominal
survey _Visit_. To avoid impacting the overall time available for the survey,
the use of TFOs is minimized, since each TFO expends propellant reserves and
observing time at the expense of survey progress. Based on the current numbers
of objects on the Center for Near Earth Object Studies Sentry table of virtual
impactors555https://cneos.jpl.nasa.gov/sentry/vi.html, TFOs are not
anticipated to take more than 1% of total available survey time.

##  4 Survey Data System

Data processing and archiving for the NEO Surveyor mission is carried out by
the NEO Surveyor Survey Data System (NSDS) that is developed and operated at
IPAC/Caltech. The NSDS is a highly automated, high-throughput hardware,
software, and operations system that is optimized to identify candidate moving
objects. Elements of the NSDS are based closely on the data systems developed
and operated for WISE/NEOWISE and the Zwicky Transient Facility (Cutri et al.
2012; Masci et al. 2019).

The NSDS will deliver moving object tracklets to the IAU Minor Planet Center
(MPC) approximately two to three times daily, typically following processing
and quality assurance of a _Quad_. Tracklets are reported on average within 72
hours following the observation time of the final detection of the last moving
object in a set of delivered tracklets. Included in the 72 hours is the fact
that downlinks are conducted once per 24 hours; the $\sim$2 hours needed to
downlink; the time needed to transmit the data from ground stations to IPAC;
and the time needed to process the data to the point of extracting tracklets.
The calibrated _Exposure_ and _Visit_ images and extracted source data will be
publicly released semi-annually via the NASA/IPAC Infrared Science Archive
(IRSA).666https://irsa.ipac.caltech.edu/ Along with positions and times,
estimated visual magnitudes will be delivered based on an average albedo, as
is currently being provided by the NEOWISE mission. These estimated visual
magnitudes, while having large uncertainties, are nonetheless intended to
provide a guide for observers performing follow up observations using visible
light telescopes.

The NSDS is comprised of a series of pipelines that convert raw NEO Surveyor
data and telemetry into the mission’s photometrically and astrometrically
calibrated data products. The high level NSDS pipelines and Subsystems are as
follows:

  * •

The Ingest/Raw Image Preparation Subsystem receives raw image data packets,
engineering and housekeeping telemetry, spacecraft navigation data, and
constructs raw image products with metadata required for downstream
processing.

  * •

The Exposure Subsystem first removes instrumental signatures from the raw
detector images, masks bad pixels, derives astrometric calibration solutions
with respect to reference sources drawn from Gaia, gathers photometric
calibration solutions, and then attaches these to the image metadata. It then
detects sources on the calibrated images on both bands simultaneously and
performs profile-fitting to measure source photometry in each NEO Surveyor
band along with positions in the International Celestial Reference System.
Sources identified as instrumental artifacts or contaminated by artifacts are
tagged in the output source list.

  * •

The Visit Pipeline combines (by co-adding) the six dithered calibrated
_Exposures_ in each _Visit_ with pixel-outlier masking included. This
suppresses transient artifacts such as radiation hits and noisy pixels. This
pipeline then performs source detection on both bands simultaneously. Profile-
fitting and aperture photometry are then performed on each detection on the
deeper combined set of six images in each band. Astrometric positions are also
solved for during profile-fitting. As done in the Exposure Pipeline, sources
identified as instrumental artifacts or contaminated by artifacts are tagged
in the output source list.

  * •

The Differencing Pipeline first performs image differencing by subtracting a
sky reference image from both a _Visit_ image (co-add) and all overlapping
_Exposure_ (detector) images therein. Reference images are constructed for
each target _Visit_ by co-adding _Exposure_ images in all other _Visits_ that
overlap the target _Visit_ on a _Quad_ pointing. The target _Visit’s_
_Exposures_ are omitted from the reference image co-add so as to not suppress
moving-object candidates identified in the target _Visit_. Sources are then
detected on the combined set of difference images per _Visit_ on both bands
simultaneously. Profile-fitting and aperture photometry are then performed
with astrometric positions derived from the profile-fit solutions. The
detection list is then filtered to remove spurious sources using a machine-
learned (ML) classifier. This classifier uses a combination of a convolutional
neural network applied to image data and a transformer-based model applied to
extracted source features. These are then fed to a multilayer perceptron to
infer the class of the detected source. This filtering results in a “purified”
list of candidate moving objects and includes objects resolved with respect to
the point spread function (PSF). The latter constitute probable comets. This
list is now ready for tracklet generation (see below).

  * •

The Moving Object Detection Pipeline (MODP) first constructs tracklets by
linking sources across the purified detection lists for every _Visit_ in a
_Quad_ generated by the Differencing Pipeline (see above). This linking is
performed by the NEO Surveyor Moving Object Detection Engine (NMODE), a
variant of which is being used for ZTF (Masci et al., 2019). This first pass
finds the majority of solar system objects, and predominately main belt
asteroids (MBAs) within its restricted velocity-search thresholds (0.008 -
8.000∘/day). Following filtering of tracklet detections from this first pass,
a “slow NEO” search is performed by running NMODE with loosened velocity
tolerances. Following this, a search for “fast NEOs” (primarily small objects
very close to the spacecraft) is performed using velocity estimates provided
by profile-fitting from the Differencing Pipeline. These velocities are used
to construct synthetic detections (time-tagged positions) within individual
_Visits_ and then fed to NMODE to construct additional tracklets using further
relaxed velocity-match thresholds. Tracklet quality metrics and “moving co-
add” images (in the co-moving frame of each candidate object), along with
“collapsed photometry” metrics are then computed. The list of tracklets,
accompanying quality metrics, moving co-add images, and ML-based scores for
every detection from the Differencing Pipeline are then fed into the Automated
Tracklet Classification Subsystem (ATC). This subsystem attaches an overall
reliability score to each tracklet as well as a score to indicate if it is
associated with a probable comet based on extendedness metrics derived from
moving co-adds and other contextual metadata.

  * •

The Survey Data Quality Assurance (SDQA) Subsystem ingests QA metrics, images,
tracklet metadata and scores assigned by the automated classifiers from all
processing steps upstream for a _Quad_. These products, along with a subset of
tracklets, are human reviewed and manually scored. Any updates to the
automated quality scores (for either tracklets, detections therein, or both)
are fed back to the automated-classifier training frameworks to improve their
performance. A list of tracklets with associated metadata, including
associations to known objects and reliability scores from both automated
classification and human vetting, is delivered to the MPC. The SDQA Subsystem
also trends select quality metrics as the survey proceeds. Key metrics are
included in reports and used to monitor survey performance.

  * •

The Archive and Database Preparation Pipeline collects the location of all
image products, source lists, and metrics from the Pipeline Operations system,
along with quality scores following human vetting and prepares these into
“load scripts” for ingestion into the NASA/IPAC Infrared Science Archive
(IRSA).777https:irsa.ipac.caltech.edu Source lists and _Visit_ images are
delivered and made publicly available through IRSA every six months.

  * •

The Static Sky Atlas Image Pipeline is executed every 12 months to construct
deep co-adds mosaicked on $\sim$14∘ x 14∘ sky footprints using all good
quality survey _Exposures_ processed during the preceding 12 months. These co-
adds will have an average depth of $\sim$160 _Exposures_ or $\sim$2.8
magnitudes deeper than a single _Exposure_ image. Atlas Images are publicly
released through IRSA every 12 months.

  * •

The Image Simulation Subsystem uses the current best estimates for detector
and telescope performance along with catalogs of solar system and
astrophysical sources to construct high-fidelity simulations of the image data
that will be collected by NEO Surveyor. These simulated images are used to
support development, testing, design decisions, explore expected survey
performance, and verify key functionality prior to launch.

##  5 Performance Modeling

To derive the requirements for the NEO Surveyor mission and to model its
performance, a simulator was developed that is capable of replicating the
properties of the Observatory, its survey cadence and operational
characteristics, and the capabilities of the ground software used to extract
detections of moving object candidates and link them together. The survey
simulation includes a model representation of the solar system’s small bodies,
including the targets of the NEO Surveyor mission, near-Earth asteroids and
comets, as well as the dominant source of moving background confusion, the
main belt asteroids. This model, known as the Reference Small Body Population
Model (RSBPM), is being used throughout the mission as a stable “yardstick”
for assessing survey completeness for the NEOs, comets, and their sub-
populations.

The steps of the survey simulation are as follows:

  1. 1.

Create a reference population model of target small body populations (NEOs and
comets) and background populations (main belt asteroids).

  2. 2.

Generate a list of survey fields, taking into account the required number and
temporal spacing of detections for orbit determination, the Observatory’s
keep-out zones, and requirements for non-survey activities such as daily
downlinks and momentum management.

  3. 3.

Propagate the ephemerides of each object to the time of each _Visit_.

  4. 4.

Evaluate whether the object falls within the Observatory’s field of view.

  5. 5.

Evaluate which objects in the field of view are bright enough to be detected
above the SNR threshold (SNR$>$5).

  6. 6.

Evaluate whether an individual object has accumulated the minimum number of
detections to successfully link the detections together reliably.

  7. 7.

Assess whether the detections fall within the limits for apparent rates of
motion (0.008 - 8.000∘/day).

  8. 8.

Tabulate numbers of detected objects and assess survey completeness as a
function of size and subpopulation.

The sections below describe these steps in greater detail.

###  5.1 Reference Population Model Generation

The reference population model contains both NEOs and background objects.
Although NEO Surveyor’s Level 1 requirements focus on the detection of NEAs
and comets, a robust model of the background population is necessary to ensure
that NEOs can be reliably distinguished from more distant small bodies using
the mission’s planned survey cadence. At the sensitivity depth of NEO
Surveyor, MBAs typically outnumber NEOs by a factor of 1000 in Visits taken
near the ecliptic plane. In addition to being used to predict the survey
performance, the RSBPM is also used to generate the synthetic solar system
object detections included in the NSDS simulated images.

####  5.1.1 NEO Model

The first step in generating the NEO population is to create diameters for all
objects in the model. Diameters were generated using the inverse transform
method to randomly sample a cumulative size distribution. Following Stokes et
al. (2017), the cumulative size distribution was taken to have the functional
form of a triple power law with $N>D^{-\alpha}$, where N is the cumulative
number of objects, D is the diameter, and $\alpha$ is the slope of the power
law. A cumulative slope of –2.75 was assumed for diameters D$>$1.5 km, –1.64
for objects with 70 m$<$D$<$1.5 km, and –3.2 for D$<$70 m. To be conservative,
the cumulative size distribution was assumed to be identical for all dynamical
groups of NEOs (Atiras, Atens, Apollos, and Amors), although there is some
evidence that the slopes may vary within these groups (Mainzer et al., 2012a).
The total number of objects is 993$\pm$38 NEAs at 1 km diameter based on
Mainzer et al. (2011b) and Granvik et al. (2018); the above cumulative size
distribution results in a total of 25,000 asteroids larger than 140 m being
generated.

To determine the visible geometric albedos for the synthetic objects, we used
NEOWISE data as the basis of the model. The sample of NEOs detected by the
NEOWISE mission’s automated detection software, the WISE Moving Object
Processing System (WMOPS; Mainzer et al., 2011c), represents our best
understanding of the NEO albedo distribution. This is because WMOPS detects
objects based solely on their thermal infrared fluxes, either at 12 $\mu$m
during the phases of the mission when that channel was operational (Mainzer et
al., 2011a, 2012b), or at 5 $\mu$m during the remaining $\sim$10 years after
the survey to date (Mainzer et al., 2014, 2019; Nugent et al., 2015, 2016;
Masiero et al., 2017). By selecting objects based on their thermal infrared
flux instead of their reflected sunlight, the NEOWISE sample is relatively
unbiased with respect to albedo. To date, $\sim$1380 NEOs have been detected
by WMOPS, giving us insight into the relative fractions of low albedo vs. high
albedo NEOs for Atens, Apollos, and Amors.

The NEOWISE-detected NEO albedo distribution consists of a group of dark
objects with a peak of $\sim$0.03 likely to have carbonaceous compositions and
a group of bright objects with a peak of 0.17, most probably associated with
stony or metallic compositions. Wright et al. (2016) modeled this as a double
Rayleigh distribution with peaks centered at 0.03 and 0.17. The fraction of
bright to dark objects for each subgroup (Atira, Aten, Apollo, Amor) was used
to randomly select whether a given object belonged to the bright or dark
group. Since very little is known about the albedo distribution of the Atiras,
the fraction of bright to dark Atiras was assumed to be identical to that of
the WMOPS-selected Atens. Once a particular object was determined to belong to
the bright or dark group, the albedo was then randomly selected using the
inverse transform of the cumulative distribution of the appropriate Rayleigh
function. Figure 5 shows the probability distribution functions (PDFs) that
were used to generate the albedo distributions of the model compared to the
WMOPS-selected albedo distributions for the various NEA dynamical classes. The
PDFs are not a perfect match to the individual populations’ distributions,
partially because they are limited to fall within 0.015 $<p_{V}<$0.60 and
partially because NEO albedos depend strongly on absolute $H$ magnitudes,
which can have large uncertainties due to the wide range of phase angles at
which NEOs are frequently observed. Nonetheless, we tested the simulation with
a number of variations of the PDFs, and there was no difference within the
statistical uncertainty in the survey performance.

Figure 5: Probability distribution functions (solid gray areas) for the
Visible geometric albedo distributions $p_{V}$ of the model NEAs compared to
the measurements for Atens, Apollos, and Amors detected by the NEOWISE based
on their thermal fluxes.

The NEO Surveyor NC1 channel spans 4.0-5.2 $\mu$m. For most NEOs over the
majority of their orbits, this channel will typically be dominated by thermal
emission, but it is important to compute the fraction of reflected sunlight
that contributes to the measured flux. Previous studies with NEOWISE were able
to fit the albedo at 3.6 $\mu$m (Mainzer et al., 2011a; Masiero et al., 2014),
but only for populations of more distant objects was the albedo at 4.6 $\mu$m
able to be determined separately (Grav et al., 2012b, a). Here, we assumed
that $p_{IR}=p_{3.4\mu m}=p_{4.6\mu m}$. The albedo in channel NC1 ($p_{IR}$)
was generated by selecting a value for the ratio of $p_{IR}/p_{V}$ using a
Gaussian kernel density estimator (KDE) with a PDF drawn from NEOs with fitted
$p_{3.6\mu m}$ values (those with fit code “DVBI”) found in Mainzer et al.
(2019). Fit codes are the values used in Mainzer et al. (2019) to denote which
parameters were fitted versus assumed in thermal modeling performed on
NEOWISE-detected objects. Infrared albedo $p_{IR}$ was limited to
0.015$<p_{IR}<$0.70 (Figure 6a).

Figure 6: (a) Fraction of $p_{IR}/p_{V}$ for the RSBPM model compared to the
$\sim$90 NEOWISE-detected NEOs for which $p_{IR}$ could be measured in Mainzer
et al. (2011b). (b) Distribution of beaming parameters ($\eta$) for the RSBPM
model compared to the $\sim$317 NEOWISE-detected NEOs for which $\eta$ could
be measured. (c) Distribution of absolute visible magnitudes for the RSBPM
model containing only NEAs $\geq$140 m compared to that of the known NEOs to
date; the distributions match well until the point at which the population
becomes significantly less observationally complete.

To facilitate use of the near-Earth asteroid thermal model (NEATM; Harris,
1998) to compute RSBPM small body fluxes, we generated the beaming parameter
$\eta$ used by NEATM for each object. Beaming values were similarly selected
using the KDE with the PDF drawn from the fitted beaming values in Mainzer et
al. (2019, fit code “B”) (Figure 6b).

Absolute magnitude $H$ was calculated using the previously generated diameters
and $p_{V}$ with the relationship

$D(km)=(1329/\sqrt{p_{V}})10^{\frac{-H}{5}}$ (1)

(Fowler & Chillemi, 1992). Figure 6c shows the distribution of $H$ magnitudes
in the RSBPM model for NEAs larger than 140 m compared to those of all known
NEOs that have been discovered to date (roughly 30,000 objects). Figure 7
shows the distributions of $H$ magnitudes broken down by diameter bins.

Figure 7: The distribution of $H$ magnitudes for NEOs is shown for various
diameter bins, illustrating that the canonical assumption that NEAs with
$H<$22 mag are all larger than 140 m in diameter is incorrect; many NEOs
larger than 140 m have $H>$22 mag.

Orbital elements from the Minor Planet Center’s NEO catalog with $H<$20 mag
form the basis of the probability distribution functions sampled by a Gaussian
KDE to obtain the orbital elements for the synthetic NEOs. This method
preserves the observed correlations between orbital elements. At present, more
than 90% of NEOs larger than $\sim$1 km are thought to have been discovered
(Mainzer et al., 2011a; Granvik et al., 2018), corresponding to an object with
$H$=17.75 mag for a visible geometric albedo of 0.14. While the fraction of
known NEOs with $H<$20 mag is not observationally complete, this limit ensures
an adequate number of objects from which to sample orbital elements and
results in a reasonable match to the orbital elements of the observed
population (Figure 8). The relative numbers of Atiras, Atens, Apollos, and
Amors were chosen to be 1.4%, 3.5%, 55.1%, and 40.0% based on the fractions
given in Mainzer et al. (2011b) and Granvik et al. (2018).

The number of unique representations of each population is determined by the
desired uncertainty to be achieved for survey completeness. We can compute the
number of objects required in the population using the binomial population
confidence
interval888https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval,
which computes the probability of success from a set of $n$ success-failure
trials (e.g. a particular asteroid is either detected or not). The number of
trials needed to measure a quantity to 95% confidence is determined by

$\hat{p}\pm z\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}$ (2)

where $\hat{p}$ is the fraction of successes in a Bernoulli trial process with
$n$ trials and $z$ is the probit of the target error rate (equal to 1.96 for
95% confidence). For an expected survey completeness of $\hat{p}=$82% in 5
years (see Section 6), we require $n\sim$7,000 objects in the population to
model survey completeness to $<1$% at 95% confidence. If we want to be able to
divide the population into subgroups (for example, separating out objects by
MOID or dynamical class, or by size), we will want an appropriately larger
number of asteroids. To this end, we have created 25 solar system
representations (SSRs) each with 25,000 NEAs larger than 140 m to assess
survey completeness.

Figure 8: Orbital properties of the model NEOs were derived from the orbital
elements of all known objects with $H<$20 mag.

####  5.1.2 Background Object Model

The main belt asteroids (MBAs) are the dominant source of background objects
for NEO Surveyor; we estimate that MBAs will outnumber NEOs by roughly 1000:1
in every _Visit_ near the ecliptic plane. Therefore, it is important to model
this background population well enough that tracklet linking efficiency and
orbit determination can be evaluated accurately in the presence of such
objects. A model of non-NEO background populations was generated that includes
Mars crossers, inner, middle, and outer MBAs, Hungarias, and more distant
objects. For purposes of this simulation, the “inner MBAs” are defined as
having perihelion distance $q>$1.3 au, semimajor axis $a\leq$2.5 au, and
eccentricity $e<0.95$. “Middle” MBAs are defined as $q>$1.3 au, 2.5$<a\leq
2.82$ au, and eccentricity $e<0.95$. “Outer” MBAs are defined as $q>$1.3 au,
2.82$<a\leq$3.6 au, and eccentricity $e<0.95$. At the sensitivity levels of
NEO Surveyor, the background population is dominated by small Mars crossers
and inner MBAs.

Similar to the method for generating the synthetic NEAs, generation of the
background objects begins with selecting the diameters using the inverse
transform method to randomly sample the cumulative size distributions of the
populations down to a specified minimum size. Like the NEAs, the inner,
middle, and outer MBAs’ size distributions each follow a triple power law of
the form $N>D^{-\alpha}$. Fitting this function to each of the three groups
using data from NEOWISE (Mainzer et al., 2019), which is based on the fits
found in Masiero et al. (2014), results in the slopes and breaks shown in
Table 1. For simplicity, the cumulative size distribution of Mars-crossing
asteroids and Hungarias was assumed to be identical to that of the inner MBAs.

The minimum size for all three populations was determined by assessing the
probability that 90% of objects of a given size would ever be detectable by
NEO Surveyor. This was assessed by placing the objects at their perihelia at
120∘ solar elongation (equivalent to the region where the NEO Surveyor
5-$\sigma$ sensitivity is 50 $\mu$Jy). This corresponds to size limits of 400
m, 500 m, and 750 m in the inner, middle, and outer main belt respectively,
for a total of 12.2 million objects. While it is possible that there is a
break in the size distribution at larger sizes than these limits (c.f. Bottke
et al., 2020), they represent conservative assumptions about the possible
number of background objects that NEO Surveyor could detect for purposes of
understanding the potential for confusion with NEOs. In total, we estimate
that NEO Surveyor will be highly complete for MBAs: completeness is estimated
to be $>$99% and $>$91% for objects down to 750 m in the inner and middle
belt, respectively. Completeness will likely exceed 90% in the outer belt for
objects larger than 1 km.

Visible geometric albedos were selected for each of the populations using a
Gaussian KDE to select from the objects with fitted $p_{V}$ (denoted by fit
code “V”) found in Mainzer et al. (2019). Albedos were limited to
0.015$<p_{V}<$0.70. Beaming parameters were similarly selected using the KDE
from the objects for which beaming could be fitted in the NEOWISE data (fit
codes containing “B” in Mainzer et al., 2019). Albedos at 4.6 $\mu$m were
selected by randomly drawing the ratio $p_{IR}/p_{V}$ from the objects in
Mainzer et al. (2019) with fit codes of “I” and multiplying this ratio by the
object’s $p_{V}$, subject to the condition that 0.015$<p_{IR}<$0.70. Figure 9
shows the comparison of the model’s physical properties elements to data from
the MPC and NEOWISE.

Figure 9: Top row: Distribution of absolute visible magnitudes ($H$) for the
RSBPM model (dashed lines) for inner, middle, and outer MBAs compared to known
objects in the MPC’s holdings (solid lines). The median $H$ magnitude for the
MPC catalog for each population is shown as a dotted vertical line. Known
objects brighter than $H<$17.75, 17.25, and 16.75 mag respectively were used
as the basis for randomly drawing orbital elements for the synthetic objects
in the inner, middle, and outer main belt. Second row from top: Normalized
visible geometric albedo distribution $p_{V}$ of the model inner, middle, and
outer MBAs compared to the measurements of $\sim$130,000 known objects
detected based on their 12- or 5-$\mu$m fluxes by NEOWISE. Third row from top:
Normalized distribution of beaming parameters ($\eta$) for the RSBPM model
compared to the NEOWISE-detected inner, middle, and outer MBAs for which
$\eta$ could be measured. Bottom row: Normalized fraction of $p_{IR}/p_{V}$
for the RSBPM model compared to the NEOWISE-detected inner, middle, and outer
MBAs for which $p_{IR}$ could be measured.

Similar to the NEAs, orbital elements were selected from the MPC’s sample of
objects thought to be observationally complete using a Gaussian KDE.
Observational completeness is defined as the turnover point in the $H$
magnitude distribution of known objects as recorded in the MPC’s catalog.
Figure 9 (top row) shows the completeness limits for the MBAs as a function of
$H$ magnitude, taken to be $H<$17.75, 17.25, and 16.75 mag for the inner,
middle, and outer MBAs respectively.

In order to reduce the data volume of objects in the model, only those
asteroids that have a chance of being detectable by NEO Surveyor were saved.
As described above, an object was determined to be potentially visible by
placing it at its perihelion at 120∘ solar elongation and computing its
thermal flux at NC2 using its diameter, $p_{V}$, and $\eta$. Only objects with
NC2 fluxes $>$50 $\mu$Jy are kept.

Figure 10 shows the comparison of the model’s orbital elements to data from
the MPC and NEOWISE for objects with $H<$15 mag (the population that is
observationally complete throughout the main belt). Figure 11 shows the
comparison of the model to NEOWISE data when $p_{V}$ is taken into account;
while the model lacks the defined albedo structure of collisional families
(Masiero et al., 2014), it faithfully reproduces the overall gradient of
darkening albedo with increasing semimajor axis and produces an appropriate
number of background objects near the NEO Surveyor sensitivity limit.

Figure 10: Orbital properties of the model background objects compared with those of the known objects.  Figure 11: Albedo distribution as a function of orbital elements of (left) the reference model population and (right) NEOWISE-detected objects from Masiero et al. (2013).  Table 1: Slopes and breaks for the triple power laws used to model the inner, middle, and outer MBAs. Population | $\alpha_{0}$ | $\alpha_{1}$ | $\alpha_{2}$ | $D_{0}$ | $D_{1}$ | $N_{1km}$ | $D_{max}$
---|---|---|---|---|---|---|---
|  |  |  | (km) | (km) |  | (km)
Inner MBAs | 2.83855 | 1.06297 | 2.08754 | 12.9908 | 59.6530 | 245917 | 196.371
Middle MBAs | 2.80428 | 1.21062 | 3.96481 | 12.4747 | 103.0874 | 804209.5 | 231.689
Outer MBAs | 2.47882 | 1.34556 | 2.6616 | 19.276 | 75.342 | 1600322 | 453.239

###  5.2 Survey Plan Generation

The simulator includes a survey plan modeling tool (SPMT) that during mission
development generates the survey plan, which consists of a sequence of
_Visits_ which respect the Observatory’s operable orientation zones. The SPMT
creates the successive _Loops_ (which produce _Quads_ of detections that are
linked together to form tracklets of minor planet detections), _Stacks_ , and
_Sides_ for the duration of the 5-year baseline mission; see Figure 4 for a
representation of the survey pattern.

The SPMT first creates a grid of positions in latitude and longitude that are
fixed on the sky, then uses the ephemeris of the telescope in its SEL1 halo
orbit to compute the line of sight with respect to the predicted positions of
the Sun and Moon. The list of pointings is recursively edited to avoid keep
out zones around the Moon and the Sun. Nominally, the survey will maintain a
minimum separation of 20∘ from the Moon to minimize the impacts of scattered
moonlight. _Loops_ with fewer than six _Visits_ are deleted to ensure that
tracklets span at least one hour.

The SPMT also edits the list of pointings to avoid taking _Visits_ during
specified times when the telescope is unavailable due to downlinks, slews,
momentum management, calibrations, and orbit station-keeping maneuvers. During
mission development, these “survey keep-out windows” (SKOWs) are scheduled
algorithmically for the entire mission in advance of SPMT runs by an
adaptation of a high-heritage spacecraft activity planning framework that has
been used in the past to generate Mars 2020 cruise background sequences,
schedule Mars 2020 relay orbiter communication, and create command products
for the Mars _InSight_ lander. The SKOW events are rescheduled periodically as
the concept of operations matures to maximize time available for survey. After
the SPMT runs, branching off from the data flow of the survey performance
simulator described below, the same program that creates the SKOW events reads
back in the SPMT-generated survey plan to create an integrated set of mission
timelines and orientation plans. These derived products are distributed to the
wider NEO Surveyor team, which feeds and improves consistency between various
investigation and engineering analyses.

###  5.3 Ephemeris Propagation

To eliminate the need to store large files of synthetic object state vectors,
an N-body orbit state propagation code is used to compute the positions of the
synthetic objects in the simulation. The code uses an initial value problem
formulation with a numerical integrator (scipy’s _solve_ivp_ function using
the ‘LSODA’ method), taking the initial positions and velocities of each
object and the major sources of perturbations (the planets and the largest
asteroids) and computing their subsequent positions and velocities using the
sum of the forces from the N bodies acting on them. In the initial value
problem formulation (IVP) method, the IVP can solve for
$\frac{df}{dt}=y(f(t))$, provided the initial condition $f(t_{0})$ is known.
In the case of orbit propagation, the initial positions and velocities of all
planets and bodies are known, and their derivatives with respect to time are
their velocities and accelerations:

$\frac{\delta[p_{0},p_{1},...v_{0},v_{1},...]}{\delta
t}=[v_{0},v_{1},...,a_{0},a_{1},...]$ (3)

where $[p_{0},p_{1},...v_{0},v_{1},...]$ are the positions and velocities of
all perturbing bodies and the particles respectively, and $[a_{0},a_{1},...]$
are the accelerations; these are simply the sum of all forces acting on each
object. The code takes into account the general relativistic force due to the
Sun using

$\mathbf{a}_{REL}=\frac{GM_{\odot}}{c^{2}|\mathbf{r}|^{3}}\Bigl{[}\Bigl{(}\frac{4GM_{\odot}}{|\mathbf{r}|}-|\mathbf{v}^{2}|\Bigr{)}\mathbf{r}+4(\mathbf{r}\cdot\mathbf{v})\mathbf{v}\Bigr{]}$
(4)

following the method recommended in Farnocchia et al. (2015), where $G$ is the
gravitational constant, $c$ is the speed of light, $M_{\odot}$ is the Sun’s
mass, $\mathbf{r}$ is the position of the body, and $\mathbf{v}$ is its
velocity. These initial conditions and forces acting on the bodies are
supplied to the IVP, and it solves for the positions and velocities at the
next time interval.

This N-body propagation method is used to compute positions at specified
intervals (taken here to be one hour) for all objects in the simulation;
positions at the individual _Visits_ between these one-hour intervals are
computed through linear interpolation to improve computational speed.
Comparison of the outputs of the positions computed in this fashion were
compared with Horizons positions of known objects, and they agree to within
0.3 arcsec for 99.95% of _Visits_ over the 5-year duration of the survey (see
Masiero et al. 2023). We require that computed ephemeris uncertainty for 99.9%
of synthetic objects in the survey simulation be less than 0.4 arcsec, so that
the analysis is dominated by the astrometric uncertainty of the positional
measurements rather than computational precision.

###  5.4 Field of View Checks

Once the ephemerides for all bodies in the model have been determined for the
times of all _Visits_ in the survey, a comparison to the NEO Surveyor field of
view is made to determine whether or not each object fell within it in each of
the two channels. The simulator takes into account positional uncertainty due
to the modeled performance of the spacecraft attitude determination and
control system and the gaps between the individual detectors in each focal
plane module. Both NC1 and NC2 fields of view are required to overlap to
within 95% of their projected areas on the sky to maximize the chances of an
object being detected in both bands. The positions and velocities of all
potential detections that fell within a chip are stored in a database.

###  5.5 Detection

The next step in the simulation is to assess whether a potential detection
(defined as an object that appears within the field of view of an individual
chip) would be bright enough to be detected. This requires computation of the
flux from the object and comparison to the sensitivity of the survey, which
varies across the sky as a function of latitude and longitude. The driving
source of background at 8 $\mu$m is the zodiacal background, particularly at
the lowest solar elongation that NEO Surveyor can observe (45∘), while at 4.6
$\mu$m it is a combination of detector read noise and zodiacal background. A
grid of sensitivities is computed across the sky, taking into account the
image quality and throughput of the optical system, detector quantum
efficiency, contributions from the natural astrophysical background, and
thermal emission from the optical system.

The model assumes a probabilistic detection model based on NSDS image
simulation results in which objects with signal-to-noise ratios (SNR) between
5-10 are detected 85% of the time; this increases to 95% detection probability
for SNR=10-20 and 99% for SNR$>$20\. Objects below SNR=5 are not counted as
detected. This is likely to be a conservative estimate based on the
performance of NEOWISE, which currently forms tracklets out of detections made
with SNR$>$4.5 (Mainzer et al.,
2014)999https://wise2.ipac.caltech.edu/docs/release/neowise/expsup/sec4_3.html#Adaptations.
NEO Surveyor uses detections made on differenced images, rather than direct
images, which helps to suppress background sources.

Small body fluxes are modeled using an implementation of the near-Earth
asteroid thermal model (NEATM; Harris, 1998), with an option to use the fast-
rotating model (FRM; Lebofsky et al., 1978). Objects are modeled as spheres
with points placed evenly on them using a variation of the Fibonacci Lattice
algorithm. Phase curve parameter $G$ (from the Bowell et al. (1989) H-G
asteroid phase curve formalism) was assumed to be 0.15 mag for all objects,
and emissivity was set to 0.9. The algorithm was validated through comparisons
to the NEOWISE data for NEOs and MBAs. See Masiero et al. 2023 for more
details on this validation.

Figure 12 shows the distances at which NEOs of different sizes can be detected
for NEO Surveyor, depending on the choice of thermal model (NEATM vs. FRM).

Figure 12: Detectable range for NEO Surveyor at SNR=5 or greater of typical
NEOs at sizes ranging from 25 m to 1 km with $p_{V}$=0.15 computed using the
NEATM model (solid contour lines) and the FRM model (dashed contour lines).
Since NEAs below $\sim$200 m are more likely to be rapid rotators (Pravec et
al., 2008), the FRM model is probably the more appropriate choice for objects
in this size range and lower.

###  5.6 Assembling Tracklets and Tracks

The penultimate step in the survey simulation is to determine which individual
detections of minor planets can be reliably linked into “tracklets”. The NEO
Surveyor image simulator is used to assess the efficiency with which tracklets
can be linked by creating simulated images implanted with synthetic moving
objects and extracting them and linking them together using NMODE. The linking
efficiency is supplied to the survey simulator as a single parameter taken to
be 99% based on historical experience from the performance of linking
tracklets from the NEOWISE mission to known or new objects. NEOWISE is the
closest analog at present, but a robust testing regime is being planned with
the MPC to confirm this assumed value. Moving object velocities must fall
within $0.008-8.000^{\circ}$/day to be counted as a tracklet; the lower limit
is set by the need to be able to distinguish motion in the $\sim 2~{}$hours
between _Visits_ that observe the same area of sky, using the $\sim$3 arcsec
NC2 point spread function. For a bright source, motion of $\sim$0.25 PSFs
between _Visits_ should be detectable. The upper velocity limit is set by the
point at which trailing losses of the source within individual _Exposures_
become significant.

The final step in the simulation of NEO Surveyor’s performance is to determine
which tracklets can be linked into “tracks”, which are sets of at least two
tracklets spanning $\sim$13 days. This step is performed by the MPC. The
linking efficiency is modeled as being 99% based on historical experience from
the performance of linking tracklets from the NEOWISE mission to known or new
objects (as opposed to being sent to the MPC’s Isolated Tracklet File). At
present, NEOWISE is the closest analog, but a robust test plan is being
carried out with the MPC to confirm this assumed value. Once a track is
formed, in most cases an orbit can be computed for the object, leading to it
being formally designated as a discovery by the MPC if the object has not been
previously identified. At this point, an object is counted by the survey
simulator and included in the assessment of survey completeness.

Astrophysical background sources will be removed via image subtraction, so
will not significantly impact tracklet building or linking. There will,
however, likely be confusion in linking together the MBAs observed, especially
at the start of the mission when many will not be previously known. However,
because MBAs do not change rapidly in brightness, we expect to see most MBAs
every 13 days for long periods of time, which will remove much of the linking
ambiguity after the first year or so of operations.

##  6 Performance Predictions

The NEO Surveyor project builds upon the discoveries made by past and present
NEO surveys. To properly account for the objects that they have and will
discover between now and launch, a model of the ground-based NEO surveys has
been developed (Grav et al. 2023). By fitting a curve to the rate of discovery
as a function of sky coverage and sensitivity of the Spacewatch, Catalina Sky
Survey and PanSTARRS telescopes, we estimate that the current completeness for
NEAs larger than 140 m is $\sim$40%. The performance of these surveys can be
projected into the future, and objects in the synthetic survey are marked as
likely to have been found prior to launch. For simplicity, we assumed in the
model that all ground-based surveys stopped at the start of the NEO Surveyor
survey. The results, including the combined total of the NEO Surveyor survey
performance along with the objects that have been or will be found by the
existing surveys are shown for objects larger than 140 m in Figure 13.

Figure 13: Top: Survey completeness as a function of time for potentially
hazardous asteroids larger than 140 m in diameter (solid blue line). These
simulations show that by the time the mission completes its baseline five year
survey, NEO Surveyor will have exceeded the mission’s Level 1 requirement of
2/3 survey completeness for PHAs $\geq$140 m in diameter (dashed gray line).
The PHA catalog will reach 90% completeness within 10 years of the launch of
Surveyor, fulfilling the Congressional mandate to NASA. The plot does not
include possible contributions from Rubin. Survey completeness for Atiras,
Atens, Apollos, and Amors larger than 140 m are shown as dashed lines. Bottom:
Survey completeness as a function of MOID.

NEO Surveyor is particularly sensitive to PHAs due to the combination of its
sensitivity and its field of regard (Figure 3). NEO Surveyor’s view of the
regions close to the Sun increases its probability of detecting objects in the
most circular, Earth-like orbits, which tend to have lower MOIDs (Figure 13).
NEO Surveyor is well-suited to detecting Atens, given their circular orbits,
complementing ground-based surveys that search near opposition, which will
tend to be more sensitive to Apollos near aphelion and Amors. Figure 14 (left)
shows the distribution of detections of PHAs as a function of sky position for
NEO Surveyor; the density of detections is highest at the lowest solar
elongations, illustrating the utility of surveying in these near-Sun regions.
This can be compared with the distribution of MBAs (Figure 14, right), which
are preferentially distributed at higher elongations on the ecliptic.

Figure 14: (Left) NEO Surveyor detections of PHAs larger than 140 m for five
years of observations as a function of sky position. The density of detections
is greatest in the direction of the Sun. (Right) Detections of MBAs for five
years of observations. In both figures, the bands of enhanced detections are
the result of small overlaps between _Stacks_.

We have not attempted to incorporate a model of the Vera C. Rubin Observatory
(Ivezić et al., 2019) in the performance predictions of NEO Surveyor. Rubin
collects observations on a different cadence from NEO Surveyor, and its
science objectives include an array of other topics. The number of NEOs it
will discover depends on the details of its observational cadence and
tracklet/track linking efficiency (Grav et al., 2016; Vereš & Chesley, 2017;
Jones et al., 2018). If Rubin discovers many NEOs prior to the NEO Surveyor
launch, it will benefit the current goal of planetary defense, which is to
come as close as possible to finding and cataloging $>$90% of PHAs $>$140 m.
In addition, we seek to obtain basic physical characterization of these
objects and find as many smaller NEOs and comets as possible. Rubin and NEO
Surveyor are thus highly complementary, and both projects are likely to be
necessary to achieve these difficult objectives.

Rubin should help significantly in reducing a major source of confusion, main
belt asteroids, as it should discover and catalog a large number of distant
objects that can cause broken tracklets or false linkages. In addition, we
seek to obtain visible geometric albedos ($p_{V}$) for as many objects as
possible, as albedo has links to composition and taxonomy (Stuart & Binzel,
2004; Mainzer et al., 2012b). Rubin will provide measurements of phase curves
and visible magnitudes, which in turn will result in well-constrained albedos
when combined with thermal measurements from NEO Surveyor. The combination of
Rubin and NEO Surveyor data should allow for derivation of albedos for
millions of asteroids, providing a significant improvement in our
understanding of the distributions of collisional family members as well as
NEO origins.

Because they can be seen at a greater range of distances, larger objects
receive many detections spanning a wide range of phase angles. Their mean
observational phase is lower than that of smaller objects, which tend to be
seen when they are closer to the observatory. Figure 15 (top) shows that the
phase angles for most objects larger than 140 m are typically less than 60∘,
facilitating thermal modeling with NEATM (Mommert et al., 2018). Smaller
objects are more often observed at higher phase angles. Most objects larger
than 140 m are observed with up to dozens or even hundreds of detections
(Figure 15, bottom), supporting the use of more detailed thermophysical models
(e.g. Delbó et al., 2007; Delbó & Tanga, 2009; Alí-Lagoa et al., 2014; Koren
et al., 2015; Hung et al., 2022; Satpathy et al., 2022).

Figure 15: Top: Distribution of phase angles for NEAs detected over the 5-year
baseline mission, grouped by size. Bottom: Number of detections per NEA as a
function of size.  Figure 16: Top: Distribution of track lengths for NEAs
$\geq$140 m found by NEO Surveyor. Bottom: Distribution of lengths of
individual tracklets for these objects.  Figure 17: Arc length of NEOs
discovered by the NEOWISE mission at the time that the MPC issued a Minor
Planet Electronic Circular, which we use as a proxy for the object having
sufficient data to constrain an orbit. Vertical dotted lines indicate 14 and
28 days. In total, 89% of the NEOs discovered by NEOWISE had arcs less than 14
days, while 99% had arcs less than 28 days.

The default NEO Surveyor cadence is designed for self follow up; objects
should not in general become lost. The NEO Surveyor survey cadence results in
observational arcs that span more than 10 days for the majority of $>$140 m
objects after two years of surveying (Figure 16). Most large objects are
observed on multiple epochs spanning months to years. This is consistent or
better than the distribution of observational arc lengths currently available
in the existing catalog of known NEOs discovered by NEOWISE (Figure 17), which
observes at a similar geometry. Figure 16 shows the distribution of tracklet
and track lengths for tracklets corresponding to objects that form tracks and
are therefore counted as found. The minimum track length is set by the minimum
observational arc needed by the MPC to confirm and designate an orbit for a
new discovery, based on experience with NEOWISE (Figure 17). Similarly, the
minimum length of individual tracklets within each track is set by the desire
to have an observational arc long enough to ensure that a tracklet can be
successfully linked to a second tracklet for an object up to $\sim$28 days
later. The cadence should ease the burden of follow up so that resources can
be reserved for objects of unusual interest or to obtain characterization
measurements such as spectroscopy, optical phase curves, or more detailed
light curves.

##  7 Conclusion

NEO Surveyor will make significant and rapid progress toward the objective of
finding and cataloging $>$90% of PHAs larger than 140 m, in addition to
providing measurements of their sizes and in many cases albedos. The results
of the mission will significantly improve our ability to mount a successful
NEO deflection campaign, should it become necessary. NEO Surveyor will
additionally provide diameters for each cataloged object, and will generate a
survey that is size-limited survey as opposed to $H$-limited. Its thermal
wavelengths will make it especially effective at discovering dark C-type NEOs,
which constitute a not-insignificant fraction of the NEO population.

The impact frequency from smaller NEOs will be determined by computing the
size and orbital element distributions of the objects (after accounting for
survey biases), then taking these distributions and propagating them forward
and backward over the next several thousand years using a suitable numerical
integrator to determine the average timescale between impacts. By determining
the size-frequency distribution for small NEOs using diameters instead of $H$,
it will be possible to significantly improve our understanding of the
frequency of Earth impacts in the $\sim$20-140 m size range.

Moreover, by providing two-channel thermal infrared data on millions of main
belt asteroids, thousands of comets, and hundreds of thousands of NEOs, NEO
Surveyor will support a host of scientific studies of these bodies. Detailed
probes of the size-frequency distribution of these populations will help to
improve constraints on their origins as well as impact probabilities. In
addition, the project will complement multi-wavelength optical data expected
to become available from the Rubin Observatory. The publicly-released and
archived data products of the NEO Surveyor mission will enable not only the
discovery of objects hazardous to the Earth, but also studies of objects both
known and yet-to-be found. It will also generate a vast catalog of images of
the sky at thermal wavelengths spanning multiple epochs that will support a
wide array of astrophysical analyses. In the process of determining the
probability of impacts from NEOs in the next century, we will learn a great
deal more about the solar system’s contents and origins.

##  8 Acknowledgements

This publication makes use of data products from the NEO Surveyor, which is a
joint project of the University of Arizona and the Jet Propulsion
Laboratory/California Institute of Technology, funded by the National
Aeronautics and Space Administration. We gratefully acknowledge the services
and support of the International Astronomical Union’s Minor Planet Center.
This publication makes use of data products from the Wide-field Infrared
Survey Explorer, which is a joint project of the University of California, Los
Angeles, and the Jet Propulsion Laboratory/California Institute of Technology,
funded by the National Aeronautics and Space Administration. This publication
also makes use of data products from NEOWISE, which is a project of the Jet
Propulsion Laboratory/University of Arizona, funded by the Planetary Science
Division of the National Aeronautics and Space Administration. This research
has made use of the NASA/IPAC Infrared Science Archive, which is funded by the
National Aeronautics and Space Administration and operated by the California
Institute of Technology.

We are extremely grateful to our anonymous referees; their comments have
materially improved the clarity and quality of this manuscript.

Dataset usage:

https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA152 (catalog WISE All-Sky
4-band Single-Exposure Images)

https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA1 (catalog AllWISE Source
Catalog)

https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA144 (catalog NEOWISE-R
Single Exposure (L1b) Source Table)

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