# The DECam Ecliptic Exploration Project (DEEP) II. Observational Strategy and
Design

Chadwick A. Trujillo Department of Astronomy and Planetary Science, Northern
Arizona University,
PO Box 6010, Flagstaff, AZ 86011, USA  Cesar Fuentes Departamento de
Astronomía, Universidad de Chile,
Camino del Observatorio 1515, Las Condes, Santiago, Chile  David W. Gerdes
Department of Physics, University of Michigan,
Ann Arbor, MI 48109, USA  Department of Astronomy, University of Michigan,
Ann Arbor, MI 48109, USA  Larissa Markwardt Department of Physics, University
of Michigan,
Ann Arbor, MI 48109, USA  Scott S. Sheppard Earth and Planets Laboratory,
Carnegie Institution for Science, Washington, DC 20015  Ryder Strauss
Department of Astronomy and Planetary Science, Northern Arizona University,
PO Box 6010, Flagstaff, AZ 86011, USA  Colin Orion Chandler DiRAC Institute
and the Department of Astronomy, University of Washington, Seattle, USA  LSST
Interdisciplinary Network for Collaboration and Computing, 933 N. Cherry
Avenue, Tucson AZ 85721  Department of Astronomy and Planetary Science,
Northern Arizona University,
PO Box 6010, Flagstaff, AZ 86011, USA  William J. Oldroyd Department of
Astronomy and Planetary Science, Northern Arizona University,
PO Box 6010, Flagstaff, AZ 86011, USA  David E. Trilling Department of
Astronomy and Planetary Science, Northern Arizona University,
PO Box 6010, Flagstaff, AZ 86011, USA  Hsing Wen Lin ( 林省文 )  Department of
Physics, University of Michigan,
Ann Arbor, MI 48109, USA  Fred C. Adams Department of Physics, University of
Michigan,
Ann Arbor, MI 48109, USA  Department of Astronomy, University of Michigan,
Ann Arbor, MI 48109, USA  Pedro H. Bernardinelli DiRAC Institute and the
Department of Astronomy, University of Washington, Seattle, USA  Matthew J.
Holman Harvard-Smithsonian Center for Astrophysics,
60 Garden St., MS 51, Cambridge, MA 02138, USA  Mario Jurić DiRAC Institute
and the Department of Astronomy, University of Washington, Seattle, USA
Andrew McNeill  Department of Astronomy and Planetary Science, Northern
Arizona University,
PO Box 6010, Flagstaff, AZ 86011, USA  Department of Physics, Lehigh
University, 16 Memorial Drive East, Bethlehem, PA, 18015, USA  Michael Mommert
School of Computer Science, University of St. Gallen,
Rosenbergstrasse 30, CH-9000 St. Gallen, Switzerland  Kevin J. Napier
Department of Physics, University of Michigan,
Ann Arbor, MI 48109, USA  Matthew J. Payne Harvard-Smithsonian Center for
Astrophysics,
60 Garden St., MS 51, Cambridge, MA 02138, USA  Darin Ragozzine Department of
Physics and Astronomy, Brigham Young University, Provo, UT 84602, USA  Andrew
S. Rivkin Applied Physics Lab, Johns Hopkins University,
11100 Johns Hopkins Road, Laurel, Maryland 20723, USA  Hilke Schlichting
Department of Earth, Planetary and Space Sciences, University of California
Los Angeles, 595 Charles E. Young Dr. East, Los Angeles, CA 90095, USA  Hayden
Smotherman DiRAC Institute and the Department of Astronomy, University of
Washington, Seattle, USA

###### Abstract

We present the DECam Ecliptic Exploration Project (DEEP) survey strategy
including observing cadence for orbit determination, exposure times, field
pointings and filter choices. The overall goal of the survey is to discover
and characterize the orbits of a few thousand Trans-Neptunian Objects (TNOs)
using the Dark Energy Camera (DECam) on the Cerro Tololo Inter-American
Observatory (CTIO) Blanco 4 meter telescope. The experiment is designed to
collect a very deep series of exposures totaling a few hours on sky for each
of several 2.7 square degree DECam fields-of-view to achieve magnitude $\sim
26.2$ using a wide $V\\!R$ filter which encompasses both the $V$ and $R$
bandpasses. In the first year, several nights were combined to achieve a sky
area of about 34 square degrees. In subsequent years, the fields have been re-
visited to allow TNOs to be tracked for orbit determination. When complete,
DEEP will be the largest survey of the outer solar system ever undertaken in
terms of newly discovered object numbers, and the most prolific at producing
multi-year orbital information for the population of minor planets beyond
Neptune at 30 au.

Kuiper Belt (893), Small Solar System Bodies (1469), Trans-Neptunian Objects
(1705), Surveys (1671), Solar System (1528)

##  1 Introduction

For decades, the properties of the Kuiper Belt and Trans-Neptunian region have
been, and still remain to this day, observationally limited. The only way to
improve this situation is by discovering more objects and characterizing their
orbital and physical properties. As of June 1, 2023, there are 2,429 multi-
opposition Trans-Neptunian Objects (TNOs) listed by the Minor Planet Center.
Survey completion rates for the brightest objects are higher than for other
TNO populations, with perhaps $\gtrsim 75\%$ of the dwarf planet-sized objects
known in the Northern hemisphere (Schwamb et al., 2014) and a similar
efficiency in the Southern hemisphere (Sheppard et al., 2011; Rabinowitz et
al., 2012) out to heliocentric distances of $\sim 100$ au. These large bodies
have diameters $D\gtrsim 450$ km (Tancredi & Favre, 2008; Pinilla-Alonso et
al., 2020) and typically are brighter than red magnitude $m_{R}\lesssim 20$
interior to heliocentric distances of $R\lesssim 40$ au. However, since there
are far more smaller TNOs ($D\lesssim 100$ km, $m_{R}\gtrsim 23$) than large
TNOs, the known sample of $D\sim 100$ km TNOs is a small fraction of the
inferred population. Estimates of the small population vary, but recent
ground-based surveys with hundred of detections find the number density of
TNOs near the ecliptic at $m_{R}\simeq 24.5$ (about $D\lesssim 25$ km for
albedos of 15%) to be more than 5 objects per square degree (Bannister et al.,
2018) suggesting that there are tens of thousands of such bodies which remain
undiscovered, a population size that has been refined, but not changed in
overall magnitude in decades (Trujillo et al., 2001). It is clear that our
knowledge of the number of TNOs is far from complete — likely more incomplete
than any other stable solar system population except for the extremely distant
Extreme Trans-Neptunian Objects (ETNOs) beyond 50 au (Sheppard et al., 2019).

It is this main deficiency of our knowledge of the TNOs that was the impetus
for our project. The main science goals and how they impact our observational
design are discussed in more detail in Section 2. Although these science goals
have been previously studied by other surveys, none have done so in as great
detail as in this survey. In the Dark Energy Camera (DECam) Ecliptic
Exploration Project (DEEP), we expect to discover a few thousand new TNOs and
trace their orbits over several years. In this work, we discuss the
observational choices that were made to achieve this goal and to achieve the
scientific goals of our project. Other works discuss the methods used to
discover new bodies (Gerdes et al., 2022; Jurić et al., 2019) and
investigations of other populations such as the main belt asteroids (Trilling
et al., 2021).

We expect that our survey, when complete, will be an improvement on prior
surveys in terms of discovery statistics by factors of a few. Prior works can
largely be classified into three categories: (1) very wide-area ($>1,000$
square degrees) but shallow ($m_{R}<21$) surveys which are designed to
maximize the discovery rate of dwarf planet sized objects (Trujillo & Brown,
2003; Schwamb et al., 2009, 2010; Sheppard et al., 2011; Rabinowitz et al.,
2012), (2) moderately wide-area surveys ($\sim 100$ square degrees)
investigating somewhat fainter magnitudes ($m_{R}\sim 24$) (Trujillo et al.,
2001; Elliot et al., 2005; Petit et al., 2011; Bannister et al., 2018;
Sheppard et al., 2019; Bernardinelli et al., 2020, 2022) and (3) very deep
($m_{R}>24.5$) but small-area surveys ($<50$ sq deg) probing the faintest TNOs
(Gladman et al., 1998; Chiang & Brown, 1999; Bernstein et al., 2004; Parker &
Kavelaars, 2010). It is beyond the scope of this work to summarize these (and
many other) surveys — for a more comprehensive work on these surveys and many
other aspects of the TNOs, see Prialnik et al. (2020).

One of our primary survey goals was to maximize total number of objects
discovered with orbital information. This simple choice created several
constraints on our survey in terms of optimization: (1) a survey duration of
1–3 years was required to understand TNO orbits, which tend to have orbital
periods of order $\sim 200$ years for bodies at 50 au and nearer, (2) since
the size distribution of the TNOs strongly favors faint bodies, a large
telescope ($\gtrsim 4$ m) was needed to reach magnitudes of $m_{R}>24$, and
(3) since total number of bodies was important, and the TNOs are known to have
an ecliptic plane density of about 1 body per square degree for magnitude
$m_{R}\sim 24$, our telescope needed to have a wide field of view given its
size. With these parameters in mind, our instrument of choice was the DECam
instrument mounted at prime focus on the Víctor M. Blanco 4 m telescope atop
Cerro Tololo, Chile. These choices also affected our observing strategy, which
was to maximize survey depth, meaning that we would observe for typically
$\sim 4$ hours on sky for any given field.

The DECam instrument on the Blanco 4 m is an excellent instrument for this
study due to its large field of view ($\sim 2.7$ square degrees) on a large
telescope ($D\sim 4$ m) with $\sim 1\arcsec$ full-width-at-half-maximum seeing
at a site relatively protected from light pollution, which compares well with
other survey instruments in terms of lightgrasp (Trujillo, 2008). Our overall
survey expectations (discussed in more detail in Section 3) are that we should
be able to probe to limiting magnitude $m_{R}\sim 26.2$ over 32 – 108 square
degrees over a few years, which will generate the discovery of a few thousand
new TNOs and allow orbital properties to be constrained for many of them.

This total number of objects should significantly exceed prior works, which
for any given work have discovered $<1,000$ total objects and in many cases
far fewer. Our survey is complementary to the expected data characteristics to
be produced by the Vera C. Rubin Observatory Legacy Survey of Space and Time
(LSST). The LSST has an exposure time of about 30 s on a 6.7 m effective
aperture telescope, resulting in a survey depth of about $m_{R}\sim 24.5$
(LSST Science Collaboration et al., 2009; Vera C. Rubin Observatory LSST Solar
System Science Collaboration et al., 2021). Since it will survey the entire
sky available from Cerro Pachón, which includes most of the ecliptic, it
should find some $\sim 40,000$ TNOs brighter than $m_{R}\sim 24.5$. Although
the total number of TNOs discovered by the LSST, which is a billion dollar
project planned to span about a decade of operations, will far exceed our
survey, our survey is deeper, $m_{R}\sim 26.2$, which corresponds to smaller
objects ($\sim 2.5$ smaller diameter) and will be complete before the LSST
begins full survey operations.

##  2 Guiding Principles of the Survey

To constrain our overall survey design, we adopted principles derived from our
science goals. The main science goals are discussed in more detail by Trilling
et al. (2019) but in summary are: (1) measure the size distribution of the
TNOs down to 25 km, (2) measure the colors of about one thousand TNOs, (3)
derive the shape distribution of TNOs from partial lightcurves, and (4)
measure colors, size distribution, and shapes as a function of dynamical class
and size.

Additional secondary science goals are related to other populations such as
the main belt asteroids, Centaurs and Trojan asteroids.

The guiding principles derived from these science goals follow:

  * •

Survey depth is more important than measuring color. That is because the
survey’s main goal is to probe small sizes and discover large numbers of
objects.

  * •

Uniform depth per observing run per field is the most important feature of the
survey. This will allow the ability to track objects consistently throughout
the survey.

  * •

Colors will only be measured after uniform depth is reached in the $V\\!R$
filter. This is because of the prior two principles and also because if
objects’ orbits are well-known, colors can be obtained in later epochs if need
be.

  * •

We want to observe each object twice in the first year, once near opposition
and once a month from opposition. Each object will then be observed in years 2
and 3 as well. The inclusion of the off-opposition observation allows for a
better orbit solution than purely opposition observations, with a factor 10
lower astrometric error after the survey is completed (see Figure 2).

  * •

We will track objects over the multi-year course of the survey, so we must
expand our sky coverage as objects with different orbits disperse from their
initial Year 1 discovery locations (Figure 2).

  * •

Objects in the outer solar system, beyond 30 au, are the primary goal, but
science cases involving closer objects, including the main belt asteroids,
will also be probed.

Figure 1: Astrometric uncertainty after our survey is completed for different
classes of TNOs. Simulated orbits were used to create simulated observations
with the addition of Gaussian noise of 0.6 arc-seconds in sky position similar
to or greater than our actual astrometric uncertainty. Orbits were then fit to
the simulated observations using the technique of Bernstein & Khushalani
(2000). Object location predictions after survey completion were then compared
to true simulated observations to determine astrometric uncertainty. Solid
lines mark the median astrometric error while dashed lines bound the 90%
confidence region for each orbit type. Most populations maintain $<30$ arc-
second error a few years after survey completion.  Figure 2: A visualization
of our recovery efficiency is shown. Circles approximate the DECam field
positions. Black fields (rightmost 3 fields) were imaged in Years 1–3, blue
(center 3) in Years 2–3 and red (leftmost fields) in Year 3. Four populations
of TNOs were modeled with simulated discovered objects shown as crosses with
color coding for the year they are imaged. Grey crosses represent objects that
are not imaged in a given year and grey lines connect object positions across
epochs. Recovery efficiency is nearly 90% or more for all classes of objects.
The numbers of plotted objects have been reduced for clarity to depict orbital
motion. In reality many thousands of objects were simulated and are expected
to be discovered in the survey. The black line represents the solar system
invariable plane as defined by Souami & Souchay (2012a).

##  3 Observing Strategy

Following the guiding principles, our observing strategy is summarized here,
including the selection of the instrumentation for the experiment, the
exposure times, filter choices and field locations.

###  3.1 Instrumentation

The basic choice we made for the instrument was the prime focus DECam camera
on the Blanco 4m telescope at Cerro Tololo. The telescope itself is historic,
completed in 1976 as a southern counterpart to the Kitt Peak Mayall 4m
telescope. With the recent upgrade of instrumentation at the Blanco 4m
telescope to the Dark Energy Camera (DECam), the DECam/Blanco instrument
combination became one of the leading survey instruments in the world (Abbott
et al., 2018).

We were granted a large survey program using DECam to survey our region over a
three year period. Given the amount of telescope time devoted to the survey,
$\sim 50$ observation dates, most of which were half-nights, we expect that
the findings of the survey will remain relevant for a decade or longer.

###  3.2 Exposure Times

Exposure times must be relatively short so that asteroids do not trail much
given their apparent rate of motion and the typical image quality, one of our
secondary science goals. Typical asteroid motion at heliocentric distances
from 2 au to 3.5 au at opposition is 43 to 28 arc-seconds/hour (Jewitt et al.,
1996). In the best seeing conditions for the DECam instrument, 0.8 arc-
seconds, the fastest asteroids will not trail with exposure times of 0.8 arc-
seconds / (43 arc-seconds / hour) x 3600 seconds / hour = 67 seconds. Readout
time for DECam is about 20 s, so choosing such an exposure time would equate
to an open shutter efficiency of 77% (0.14 magnitudes loss compared to 100%).
The optimum exposure time is therefore a trade-off between limiting magnitude
for the asteroids, which suggests shorter exposures, compared to open-shutter
efficiency which affects the limiting magnitude for the slower objects, which
suggests longer exposures.

To strike a balance between those two competing factors, we have chosen an
exposure time of 120 seconds. This yields efficiency due to the readout time
of 86% (0.08 magnitude loss for slow objects). In terms of trailing loss
(applicable to the fastest objects) this would be 0.17 to 0 magnitudes
trailing loss for slowest asteroids (28 arc-seconds per hour) in seeing of 0.8
arc-seconds to 1.1 arc-seconds. And this exposure time yields 0.6 to 0.3
magnitudes trailing loss for fastest asteroids (43 arc-seconds per hour) in
similar seeing conditions.

###  3.3 Expected Survey Depth in $V\\!R$ and Predicted Discovery Numbers

We can estimate the survey depth based on the performance achieved during the
Dark Energy Survey (DES) DR1 (Abbott et al., 2018). The bandpass of our chosen
filter, the $V\\!R$ filter, is not specifically studied in Abbott et al.
(2018), but is roughly equivalent in terms of total wavelength breadth and
somewhat superior in overall throughput to that of $g$ and $r$ combined from
the DES DR1 (see Section 3.4). The overall sensitivity of a single 90 second
exposure in $g$ and $r$ was estimated to be $g=23.57$ and $r=23.34$ for
signal-to-noise ratios of 10 ($S/N=10$) for the DES DR1. Since $S/N$ is
proportional to the square root of exposure time $t$, and similarly the square
root of overall bandpass $b_{p}$, as $S/N\sim\sqrt{b_{p}}$, we expect our
limiting magnitude $m_{R}$ from filter change alone to be roughly
$m_{r}\approx 0.5\times(23.34+23.57)+2.5\log(\sqrt{2})=23.83$.

In addition, we expect to take about $\sim 100$ images each of 120 seconds
compared to the 90 second single images discussed in Abbott et al. (2018).
Also, we only need $S/N=6.5$ for detection, as compared to the DES DR1
$S/N=10$. We expect that our combination of images will not be as efficient as
the static sky DES found primarily because we are canvassing many possible
apparent rates of objects in our analysis. Not only this, but since we plan to
subtract a static image template, in some cases residual flux remains from
slowly moving objects. The theoretical magnitude increase for the full DES
survey, which targets about 10 images per sky location, would be
$1.25\log(10)=1.25$ magnitudes. The actual realized performance appeared to be
about 0.5 magnitudes less than this as the mean survey-wide magnitude limits
for $g$ and $r$ are 24.33 and 24.08 respectively (for the co-add magnitude
limit with 1.95 arc-seconds diameter, $S/N=10$), which are 0.76 and 0.74
magnitudes better than the single image depth, a loss of about 0.5 magnitude
over the theoretical maximum. We expect our multiple-image depth to be worse
than this since our objects are moving and are found by combining images
assuming apparent rates for possible objects since we do not a priori know
their apparent motion rates. Assuming that our image combination loss to be
about 0.75 magnitudes versus the DES performance, we expect our limiting
magnitude $m_{r}$ (which is roughly equivalent to $m_{R}$) to follow:

$\displaystyle m_{R}$ $\displaystyle\approx$ $\displaystyle
23.83+1.25\log\frac{120\mbox{ s}}{90\mbox{ s}}+1.25\log\frac{100\mbox{
exposures}}{1\mbox{ exposure}}+2.5\log\frac{10}{6.5}-0.75\mbox{ image
combination correction}$ $\displaystyle m_{R}$ $\displaystyle\approx$
$\displaystyle 26.21$

This image depth of approximately $\sim 26.2$ is well beyond that expected for
the LSST single image depth, as well as any other prior wide-field survey
sensitive to TNOs.

Given the image depth of $\sim 26.2$, we can estimate how many objects we
might we discover at that depth. To date, no surveys have observed this faint
for this amount of sky area so this number is highly uncertain. For this
estimate, we choose a comparison survey that is of similar depth since surveys
that are not as sensitive suffer from extrapolation and surveys that are
fainter tend to have lower number statistics. Perhaps the most relevant
similar survey is that of Fuentes et al. (2009) who used data from the Subaru
8.2 m telescope to detect 82 TNOs to a depth of $R=25.7$ with a sky area of
3.57 square degrees near the ecliptic in a re-analysis of data collected for a
Uranian satellite search (Sheppard et al., 2005). These results yield an
ecliptic density of 23 objects per square degree. Since our survey is about
0.5 magnitude fainter, using the brightness distribution from Fuentes et al.
(2009) with the slope of the faint object apparent magnitude distribution
$\alpha=0.3$ derived from all surveys at the time, this magnitude difference
would result in a correction factor of $10^{0.3\times 0.5}=1.41$ times more
objects, yielding an expected ecliptic detection rate of 32 objects per square
degree.

Using this number, we can extrapolate to our survey area. Here, we make the
distinction between full-survey objects (that is, objects tracked throughout
the entire survey and which have reliable orbital information) and partial-
survey objects (which, in the final planned year would have distance and
brightness information but uncertain orbits). For the former population, we
imaged 4 patches of sky throughout the year for each of 3 fields. For the
latter population, we imaged 4 patches of sky throughout the year for each of
10 fields. Since the DECam camera is about 2.7 square degrees, this is an
effective sky area of between 32 and 108 square degrees. Thus, we expect to
discover about 1,000 objects with well-determined orbits and 3,500 objects
with enough information to measure apparent magnitude and radial
distributions.

###  3.4 Optimal Filters for TNO Colors

In order to assess the number of objects for which we could obtain color
information, we again compared our survey to the results of the DES DR1
(Abbott et al., 2018). From DR1, we could determine that $g$ goes deeper than
$r$ by 0.23 magnitudes and $g$ probes deeper than $i$ by 0.79 magnitudes. Here
we consider $g-V\\!R$, $r-V\\!R$ and $V\\!R-i$ as well as the more traditional
$g-r$. From the DES DR1 filter performance and color information of TNOs, we
can assess the optimal filter system to use in terms of number of objects for
which color information can be obtained. Here, we report our observational
choices primarily for future works. Although we planned to estimate TNO colors
using DECam, weather loss and cancellation of observing runs due to COVID-19
resulted in fewer observational nights realized compared to our plans.
Therefore, in actuality, although we planned our survey to measure TNO colors
from DECam itself, we did not implement this plan as our primary goal was to
achieve very deep $V\\!R$ depth.

####  3.4.1 Colors with $g-V\\!R$ versus $g-r$

The first question is whether $g-V\\!R$ is superior to $g-r$. The $V\\!R$
filter is not examined in the DES DR1 paper because its total throughput with
the telescope is not measured with the analysis tools used for DES DR1.
However, we believe the $V\\!R$ bandpass can be estimated from the DR1 paper
sensitivity. A summary of filter bandpass in terms of approximate mean
wavelengths and throughputs is described in the DES DR1 paper and the $V\\!R$
filter bandpass information is found on the DECam webpages
111https://noirlab.edu/science/programs/ctio/filters/Dark-Energy-Camera/VR-
filter:

  * •

$g$ central wavelength = 482.0 nm (416.5 nm - 547.5 nm, average peak 50%
throughput)

  * •

$r$ central wavelength = 641.25 nm (567.0 nm - 715.5 nm, average peak 80%
throughput)

  * •

$V\\!R$ central wavelength = 626.5 nm (496.5 nm - 756.5 nm, estimated average
peak 80% throughput)

  * •

$i$ central wavelength = 782 nm (708.0 nm - 856 nm, average peak 95%
throughput)

The throughput and filter bandpass information gives us an approximate measure
of the total number of photons falling on the detector. Multiplying bandpass
(nm) by throughput (a unitless fraction) yields $g$ = 65 nm, $r$ = 118 nm,
$V\\!R$ = 208 nm, and $i$ = 140 nm. So achieving the same signal-to-noise in
$r$ compared to $V\\!R$ would require about 208 nm / 118 nm = 1.76 times the
exposure time (assuming similar sky background which seems reasonable since
they have the same central wavelength). This overhead would result in a
magnitude loss of about $2.5\log\sqrt{1.76}$ = 0.31 magnitudes. The faint TNO
size distribution of Bernstein et al. (2004) is roughly $10^{\alpha\Delta m}$
where $\alpha=0.32$ and $\Delta m$ is the magnitude limit between two sets of
observations, so this 0.31 magnitude loss corresponds to 25% fewer objects. It
might be tempting to still choose $r$ over $V\\!R$ to improve color
measurements, but this is not necessary as this effect is marginal since the
central wavelength difference between $g-V\\!R$ (147.5 nm) and $g-r$ (170 nm)
is very close. In fact, $g$ measurements are nearly decoupled from $V\\!R$ as
they only overlap by about 50 nm, less than one third of the $V\\!R$ bandpass.
Thus, $g-V\\!R$ is comparable to $g-r$ in terms of object color sensitivity
due to central wavelength differences, but takes less time to execute at the
telescope given the wide bandpass of $V\\!R$ compared to $r$.

####  3.4.2 Colors with $V\\!R-i$ versus $g-V\\!R$

Given that $g-V\\!R$ appears operationally superior to $g-r$, we then explored
whether the TNOs are red enough to chose $V\\!R-i$ rather than $g-V\\!R$ for
color measurements.

The blue TNOs have $R-I\sim 0.45$ and $V-R\sim 0.47$ while the red TNOs have
$R-I\sim 0.65$ and $V-R\sim 0.65$, so $V-I\sim 0.9$ and $1.3$ magnitudes for
blue and red TNOs respectively (Figure 2, Doressoundiram et al., 2008). The
color measurement of $V-I$ spans about 332 nm between the central wavelengths
of the two filters. The difference between $g$ and $i$ central wavelengths is
about the same as the difference in $V-I$. The sky is also brighter in $i$
since the DR1 paper shows that with similar exposure time, the depth in $i$ is
0.8 magnitudes worse than $g$. Therefore, for blue objects it does not matter
if we use $g$ or $i$. For the red objects, we are 0.5 magnitudes better off in
$i$, which yields $10^{\alpha\Delta m}$ times more objects, where
$\alpha=0.32$, so 45% more objects (Bernstein et al., 2004). The overlap in
wavelength of $i$ with $V\\!R$ is similar to $g$ and $V\\!R$, so there is no
exacerbating issue that the two color measurements might have different
sensitivities due to bandpass overlap.

Although $i$ seems favored over $g$ for colors when paired with the $V\\!R$
filter, there are more difficulties with calibration in $i$ due to fringing.
However, the DES analysis pipeline has been extensively tested with mitigating
fringing so we expect that the public pipeline could be used for this
calibration of the data if needed. The use of $i$ has additional advantages
over $g$ — it is less sensitive to the moon and seeing is generally better in
$i$.

Given the above factors, for this project we planned to measure colors using
$V\\!R$ \- $i$. The choice of $V\\!R$ over $r$ was made because of bandpass.
The choice of $i$ over $g$ was made due to the redness of the TNOs, which
favors $i$ wavelengths. Although unconventional, the $V\\!R$ \- $i$ color
measurement should take about half the exposure time compared to a more
traditional $r$ \- $i$ measurements because of the much improved throughput of
the $V\\!R$ filter. This assumes that color measurements would take place
during survey time epochs, which are primarily performed with the $V\\!R$
filter. If color measurements were to take place in a separate epoch, it could
be efficient to use $g-r$ or $r-i$ in a single epoch to minimize issues
related with lightcurves, which might preclude the linking of prior epoch
$V\\!R$ colors with future epoch color measurements.

###  3.5 Field Layout and Orbit Determination

One of the primary science goals of this study is to determine the orbits of
discovered objects by following their sky location over the course of a few
years. In observational terms, the key issue for our survey is correctly
linking the position of a particular object throughout the observations
without confusion from other objects that happen to be nearby as well. Each
DECam field is about 2 degrees in diameter, and not quite circular, for a sky
area of about 2.7 square degrees. We expect in the first year to cover 3 of
these fields, extending to 10 fields by the final years of this survey. Our
overall discovery rate is expected to be at best a few thousand objects and
our sky area to be covered is about 8.1 square degrees per 3 field patch in
the first year, of which there are four patches distributed about the
ecliptic. To design the survey to perform for the most difficult case, which
is a larger than expected number of objects, we adopted a working value of
3,000 objects discovered in the first year, which is a more difficult case
than the expected value of $\sim 1,000$ objects in Year 1 (Section 3.3). This
suggests a maximum object sky density of 8.1 square degrees x
$60\arcmin/\arcdeg\times 60\arcmin/\arcdeg\times 4$ patches / 3,000 objects
$\sim$ 39 square arc-minutes per object. Although the base number, $\sim
3,000$ objects, may not have been realized, this was a best-case scenario upon
which we based our recovery plan on and is what follows here. Although there
is some overlap among the Year 1 fields, it is less than 10% of each field and
varied from patch to patch due to the ecliptic angle with respect to celestial
north so we did not consider the effect of the overlap at the planning stage.

Data processing is expected to be done at the epoch level — that is, objects
will be identified in each field pointing each year using a shift-and-stack
methodology to identify objects much fainter than the single image depth
threshold (Jurić et al., 2019; Gerdes et al., 2022). These discoveries will
then be linked to objects found in subsequent years of observation, to be
identified in the same manner. One of the primary issues for our survey is
that of confusion – the sky density of newly discovered objects is high enough
that they can be confused with other objects in year-to-year observations. The
sky density of objects is about 1 per 39 square arc-minutes (see above). This
suggests that object locations must be predicted to an accuracy of about
$\sqrt{39\rm{sqarcmin}}\approx 6\arcmin$ to avoid major confusion issues.
Another method to reduce confusion might be to split the $\sim 4$ hours of
observations we collected each night across two nights. We did not
specifically explore or execute this possibility because it would make the
survey more sensitive to night-to-night weather differences and would increase
the computational burden of stacking this longer baseline of images to find
objects over a 24 hour timebase rather than the 4 hour timebase we executed.

The exact accuracy of object predictions from year-to-year is a function of
many unknowns including object orbital properties (which are not known as this
is one of the goals of the survey), specific observing windows allocated to
the project on a semester-by-semester basis, and survey depth achieved based
on actual local conditions at the time of observation. Simulating these
largely unpredictable effects on the survey is very difficult because of the
large number of free parameters. Instead, to determine an adequate observation
cadence, we studied the more simplistic question of whether object locations
could be identified in years subsequent to the original 3 year survey duration
and to what accuracy, which implicitly assumes that the objects can be traced
through all epochs of observations relevant to each object.

####  3.5.1 Field Layout

To optimize potential survey effectiveness, we simulated the apparent motion
of 4 populations of objects over the original 3 year course of the survey:
Cold (low inclination) Classical Trans-Neptunian Objects, Hot (higher
inclination) Classical TNOs, Scattered TNOs, and high perihelion TNOs such as
Sedna. The optimal survey pattern for following these objects over the course
of a few years appeared to be increasing the sky area each year to follow the
fastest objects found in Year 1 while also retaining slower objects which
never move beyond the original three field Year 1 pattern. The circles in
Figure 2 show our survey plan with approximate DECam field positions. Black
fields were imaged in Years 1–3, blue in Years 2–3 and red Year 3. Our survey
was extended into Year 4, partly due to the COVID-19 pandemic, which resulted
in the decision to include an additional five fields at the wide end of the
“fan” pattern that were not originally studied in our simulations.

We show four of the populations of TNOs with simulated discovered objects in
Figure 2 with recovery efficiency shown in each title and is nearly 90% or
more for all classes of objects. The numbers of detected objects in the plots
are representative only in order to depict orbital motion — thousands of TNOs
are expected to be discovered in the survey and many more were simulated to
assess survey strategy.

####  3.5.2 Orbit Determination

We simulated typical expected orbital parameters for Cold (low inclination)
Classical Trans-Neptunian Objects, Hot (higher inclination) Classical TNOs,
Scattered TNOs, and high perihelion TNOs. We then computed the positions of
each of these objects through a field observation pattern that included both
opposition and quadrature observations in the first year and only opposition
observations in the second and third years. Noise was added at typical
centroid uncertainties of 0.6 arc-seconds for each of the positions computed.
Then, these noise-added simulated observations were used to fit an orbit using
the orbit determination method of Bernstein & Khushalani (2000). This new
orbit based on noise-added simulated observations was then predicted for
future years (0 – 10 years after survey completion) and compared to the true
simulated orbit. The difference for the prediction from orbits of the noise-
added simulated observations compared to the true simulated orbit predictions
was then used to estimate the accuracy for the populations of interest. Full
orbital parameters simulated are given in Table 1.

Overall, we found that using the optimal cadence of one observation near
quadrature and one near opposition the first year, then only opposition
observations in years 2 and 3 resulted in median sky-plane position accuracies
of 10 arc-seconds or better for most populations about 2 years after the
conclusion of the survey and better than 90 arc-second astrometric uncertainty
about 4 years after the survey conclusion. The addition of the single epoch of
quadrature observations the first year was critical — this inclusion lowered
astrometric uncertainty by about a factor of 10. This is expected as for
opposition observations both parallactic motion and intrinsic orbital motion
contribute to apparent sky-plane velocity and quadrature observations break
this degeneracy since parallactic motion is minimized (Bernstein & Khushalani,
2000). This is well within the nominal accuracy required to combat confusion
described in Section 3.5 of 6 arcminutes, so we expect that confusion will not
be an issue for the DEEP survey.

Table 1: Population parameters used for orbital simulations. These were used to characterize the behavior of known sub-populations of the TNOs to design the survey. Inclination parameters $\sigma$ and $\mu$ are described in Gulbis et al. (2010). The primary parameters of importance in survey design are the semi-major axis distributions of the populations, which constrain year-to-year motion of the objects, and the inclination distributions, which constrain the width of the “fan”-shaped sky area covered. Parameter | Value | Population | Description
---|---|---|---
|  | Orbital Parameters for Orbital Simulations |
$a_{\rm min}$ | 42 au | Cold Classicals | Minimum semi-major axis
$a_{\rm max}$ | 45 au | Cold Classicals | Maximum semi-major axis
$q_{\rm min}$ | 42 au | Cold Classicals | Minimum perihelion
$q_{\rm max}$ | 45 au | Cold Classicals | Maximum perihelion
$e_{\rm min}$ | 0.0 | Cold Classicals | Minimum eccentricity
$i_{\sigma}$ | 2.0° | Cold Classicals | Inclination width $\sigma$
$i_{\mu}$ | 0.0° | Cold Classicals | Inclination mean $\mu$
$a_{\rm min}$ | 42 au | Hot Classicals | Minimum semi-major axis
$a_{\rm max}$ | 45 au | Hot Classicals | Maximum semi-major axis
$q_{\rm min}$ | 42 au | Hot Classicals | Minimum perihelion
$q_{\rm max}$ | 45 au | Hot Classicals | Maximum perihelion
$e_{\rm min}$ | 0.0 | Hot Classicals | Minimum eccentricity
$i_{\sigma}$ | 8.1° | Hot Classicals | Inclination width $\sigma$
$i_{\mu}$ | 0.0° | Hot Classicals | Inclination mean $\mu$
$a_{\rm min}$ | 250 au | Extreme TNOs | Minimum semi-major axis
$a_{\rm max}$ | 1250 au | Extreme TNOs | Maximum semi-major axis
$q_{\rm min}$ | 50 au | Extreme TNOs | Minimum perihelion
$q_{\rm max}$ | 500 au | Extreme TNOs | Maximum perihelion
$e_{\rm min}$ | 0.65 | Extreme TNOs | Minimum eccentricity
$i_{\sigma}$ | 6.9° | Extreme TNOs | Inclination width $\sigma$
$i_{\mu}$ | 19.1° | Extreme TNOs | Inclination mean $\mu$
$a_{\rm min}$ | 50 au | Scattered TNOs | Minimum semi-major axis
$a_{\rm max}$ | 150 au | Scattered TNOs | Maximum semi-major axis
$q_{\rm min}$ | 30 au | Scattered TNOs | Minimum perihelion
$q_{\rm max}$ | 40 au | Scattered TNOs | Maximum perihelion
$e_{\rm min}$ | 0.65 | Scattered TNOs | Minimum eccentricity
$i_{\sigma}$ | 6.9° | Scattered TNOs | Inclination width $\sigma$
$i_{\mu}$ | 19.1° | Scattered TNOs | Inclination mean $\mu$

Orbits will be well-determined by our plan. Using our simulation, we also
computed expected fractional accuracy for orbital elements from our observing
cadence and duration using noise-added astrometric positions as described
above, and are presented in Table 2. These were computed by measuring the
absolute magnitude of the difference between the true orbital quantity and the
estimated value determined by the method of Bernstein & Khushalani (2000).
This difference was then divided by the true quantity to yield fractional
error. For instance, for semi-major axis with true and estimated values
$a_{\rm true}$ and $a_{\rm est}$, the fractional accuracy was $|a_{\rm
true}-a_{\rm est}|/a_{\rm true}$, which in our simulations was 0.4% (Table 2).

We note that these are predicted orbital accuracies as actual orbital
accuracies can only be done on and object-by-object basis by running dynamical
simulations of many clones for millions of years — a process that is not
tractable for a large suite of simulated objects as we study here. Such an
analysis will be done at a later time when full knowledge of our discovered
objects is known. Our predicted accuracies are similar to, but a factor of a
few worse than what was reported by Bannister et al. (2018) with a semi-major
accuracy of $\delta a/a\lesssim 0.1\%$ for the vast majority of their
classical non-resonant objects. This value was determined for their objects
after they were discovered and extensive simulations were run. We may yet
achieve that accuracy as our original survey was extended to a timebase of 3
years (rather than our original 2 which is considered here) due to the
COVID-19 pandemic. In addition, the $1\sigma$ astrometric uncertainty given to
observations was 0.6 arcseconds, which was a deliberate overestimate of our
estimated astrometric uncertainty used for planning purposes.

Table 2: Orbit accuracy based on our simulated object classes for our survey
cadence and duration (observations in Year 1 at opposition and quadrature,
then opposition in Years 2 and 3). Fractional error is computed by calculating
the absolute magnitude of the difference between the true quantity and the
estimated orbital quantity and dividing by the true quantity, as described in
the text. Orbital quantities are semi-major axis (in au), eccentricity and
inclination (in degrees), denoted by $a$, $e$, and $i$.

Class | quantity | median fractional error | median value
---|---|---|---
Cold Classical | $a$ | 0.0039 | 43.40
Cold Classical | $e$ | 0.018aafootnotemark:  | 0.012
Cold Classical | $i$ | 0.0044aafootnotemark:  | 1.66
Hot Classical | $a$ | 0.0043 | 43.33
Hot Classical | $e$ | 0.014aafootnotemark:  | 0.011
Hot Classical | $i$ | 0.0027 | 5.78
Scattered | $a$ | 0.059 | 81.61
Scattered | $e$ | 0.068 | 0.569
Scattered | $i$ | 0.0037 | 19.60
ETNO | $a$ | 0.43 | 479.16
ETNO | $e$ | 0.11 | 0.801
ETNO | $i$ | 0.0038 | 20.32
aafootnotemark:

For these quantities, the true value nears 0 for the simulated population, so
the fractional error is poorly defined and instead the absolute error is
reported, which is the absolute magnitude of the difference between the true
and estimated values.

###  3.6 Field Considerations for A Semester

Our initial observing windows in 2019A, the first semester of the project,
were UT Dates April 2–4, (second halves of the nights), May 5–8 (full nights),
and June 2–4 (first halves of the nights). Although we also had observing
dates scheduled for July 7–9 (second halves), these are late enough in the
year that they would be accessing the B Semester fields, which are described
in Section 3.7. According to Horizons ephemeris estimates for the Sun position
(Giorgini et al., 1996), the opposition point of the A semester observing
dates were as follows (in J2000 RA and Dec): April 2 12:43, -4:38; May 5
14:46, -16:02; June 2 16:37, -22:05. From the CTIO ephemeris page the sidereal
times between twilight were as follows: April 2 08:00–17:37, May 5
09:39–20:05, June 2 11:18–22:09.

Since our only full nights were scheduled in May, and most of our objects of
interest have fairly uniform distributions in ecliptic longitude, it made
sense to choose the A semester fields based on the allocated May 2019A time.
Ideally, these would be within $\pm 1$ hour of opposition. This is close
enough to opposition that the apparent motion is quite high and then in the
month preceding, the -1 hour from opposition fields will be at quadrature and
the +1 hour from opposition fields will also be at quadrature. Thus, the two
groups of fields should ideally be around 13:46 and 15:46. Pairing both
quadrature and opposition observations in the same year results in superior
constraints on orbits as the opposition observations probe heliocentric
distance from parallactic motion which are combined with orbital motion while
the quadrature observations probe primarily orbital motion (Bernstein &
Khushalani, 2000), as discussed in Section 3.5.

Minor adjustments were made to the above based on the fact that the ecliptic
is not parallel to the celestial equator, so the first sets of fields in a
full night don’t rise as high in the sky as the second sets of fields. Also,
the first 2 hours of the nights in May were scheduled for a different program
so we did not consider those hours for execution of our program. The goal was
to equalize the depth of the fields during each of the two visits. To do this,
we chose positions to equalize the time each field was above 1.4 airmasses and
when the Sun was below -15 degrees (halfway between nautical and astronomical
twilight boundaries).

Most of the populations of interest are thought to be relatively uniform in
ecliptic longitude (classical TNOs, Scattered TNOs, Non-Hilda Asteroids,
Centaurs). Exceptions to this are the Resonant TNOs, Trojan Asteroids and the
Extreme TNOs which may be affected by the action of a distant massive planet
(Trujillo & Sheppard, 2014). The four most populated Neptune resonances, in
order of total number of observed objects in each resonance, are the 3:2, 2:1,
5:2 and 4:3. Examples of orbits of objects in the 3:2, 2:1 and 4:3 resonances
can be seen in (Figures 3–10, Malhotra, 1996).

The most important point of the longitudinal distributions of the resonant
objects is that the 3:2 objects, which are by far the most numerous, librate
with their perihelia about 90 degrees from Neptune, with a spread of about
$\pm 15$ degrees. The 2:1 objects can be found at many longitudes without
introducing significant bias and the 4:3 come to perihelion 60 degrees from
Neptune. So ideal field locations would probe the area about 90 degrees from
Neptune. This would allow enhanced sensitivity to the smallest TNOs which
would be the Neptune-crossing 3:2 objects near perihelion. A secondary field
could be done 60 degrees from Neptune which would allow the 4:3 objects to be
probed and provide some sensitivity to Neptune Trojans. The benefit of
catching the 3:2 objects at perihelion versus aphelion is, for an object with
eccentricity $e$, in terms of magnitude is
$2.5\log(\frac{1+e}{1-e})^{4}=10\log\frac{1+e}{1-e}$. So for a 3:2 TNO with a
typical eccentricity of 0.23, the gain is $\Delta m$ = 2.0 magnitudes in
brightness. In terms of object numbers, for shallow size distributions such as
the Bernstein et al. (2004) faint Hubble-detected objects, the total number of
objects is proportional to $10^{\alpha\Delta m}$ where $\alpha=0.32$, so this
is about a factor 4 in terms of object numbers. This also means we can probe
objects that are about a factor 2.5 smaller in size using this methodology.
That said, eccentric bodies do spend more time at aphelion than perihelion, so
there will be an effect on survey sensitivity due to this issue, which we have
not considered here.

Since the 3:2 TNOs have perihelion $90\pm 15$ degrees ($6\pm 1$ hours) from
Neptune and the 4:3 TNOs are $60\pm 15$ and $180\pm 15$ degrees ($4\pm 1$ and
$12\pm 1$ hours) from Neptune, this would suggest the optimum field locations
should be about 05:15 and 17:15 for the 3:2 objects and 04:15, 11:15 and 19:15
for the 4:3 objects. For the 2019A semester, it was difficult to reach these
resonant locations at opposition, but we could image one of the 3:2 locations.
The 2019B semester provided a better opportunity although, in the end, we
chose to optimize fields based on observing date and opposition proximity in
the B semester (Section 3.7). The Extreme TNOs (Sheppard et al., 2019) appear
to come to perihelion in the 3 to 6 RA hour range, so they were unavailable
until mid-2019B. Additionally, our main science goals are related to the
general TNO population which has very large expected discovery numbers,
without an undue focus on the Extreme TNOs, which are much more difficult to
detect (Trujillo & Sheppard, 2014), so we decided to not alter our survey
fields just for the few Extreme TNOs that we might find in the survey fields.

In summary, the actual field locations were chosen to lie along the invariable
plane as defined by Souami & Souchay (2012b) and designed such that equal
amounts of time could be spent on each field during the on-opposition and off-
opposition observations given our 2019 A semester observation dates. A similar
methodology was followed for the 2019 B field choices. Subsequent years after
2019 were solely based on our initial 2019 field choices.

####  3.6.1 Actual Field Coordinates Selected for 2019 A Semester

Based on the approximate field centers above, we examined star locations from
the Gaia catalog to select fields minimizing the number of stars brighter than
$G<8$ (Gaia Collaboration et al., 2016, 2018) and in particular the especially
bright stars with $V<5$. We have examined the final year 4 field configuration
which is 15 full fields. The sky plots of the fields are pictured in Figure 3
and 4. All fields were moved less than 0.5 hours along the invariable plane to
avoid the brightest stars. We used the invariable plane defined by Souami &
Souchay (2012a) ($i=23.008861\arcdeg$ and $\Omega=3.8526111\arcdeg$ with
respect to the celestial ICRF plane and $i=1.5786944\arcdeg$ and
$\Omega=107.58222\arcdeg$ with respect to the J2000 ecliptic). Volk & Malhotra
(2017) found that the classical TNOs had $1\sigma$ limits of
$i=1.2$–$2.2\arcdeg$ and $\Omega=$63–$95\arcdeg$, which is less than the
maximum deviation from the invariable plane of our field locations especially
considering the cold classical TNOs have an inclination distribution that is
$\sin(i)$ times a Gaussian with $1\sigma=2\arcdeg$ Gulbis et al. (2010). More
recently, Matheson & Malhotra (2023) provided improved estimates of the Kuiper
Belt mean plane, which for many heliocentric distances, fall within our
original design criteria. The actual field coordinates are given here for our
2019 observing epoch. Note that at each observation epoch in subsequent years,
field coordinates were adjusted based on the day of the year of observations
and the expected apparent motion of TNOs to minimize the loss of objects
moving off of field edges due to apparent motion caused by the Earth. Any
orbital motion of the TNOs was not accounted for in this manner because the
overall field “fan shape” was selected to adjust for the orbital motion
expected for the objects.

Figure 3: The “A” semester fields, observed in the first half of each year of
the survey. In year 1 the black fields (A0a-c and A1a-c) were observed. In
year 2, the black fields were repeated and the blue fields (A0d-f and A1d-f)
were added. In year 3, all year 2 fields in addition to the red fields (A0g-j
and A1g-j) were observed. The year 4 fields (A0k-o and A1k-o), in green, were
not originally anticipated as part of the survey but were added later as
mitigation for telescope time losses due to operational shutdowns related to
the COVID-19 pandemic. Figure 4: The “B” semester fields, observed in the
second half of each year of the survey. In year 1 the black fields (B0a-c and
B1a-c) were observed. In year 2, the black fields were repeated and the blue
fields (B0d-f and B1d-f) were added. In year 3, all year 2 fields in addition
to the red fields (B0g-j and B1g-j) were observed. The year 4 fields (B0k-o
and B1k-o), in green, were not originally anticipated as part of the survey
but were added later as mitigation for telescope time losses due to
operational shutdowns related to the COVID-19 pandemic. Neptune’s location
though our 2022B allocated observing window is marked with an N and a line.

In 2019, we initially planned for 3 fields where all detected objects would be
further followed in our years-long survey. As the actual fields observed each
year were adjusted based on typical TNO apparent motion, and the total
duration of the survey was extended due to COVID, the final field selection
was not set until the 2022 observations. These field coordinates are shown for
the 2022A semester in Table 4.

###  3.7 Actual Field Coordinates Selected for 2019 B Semester

The choice of B semester fields was largely based on our allocation of
telescope time in the first semester of observations following the same
methodology of the A semester observations. Given that we were allocated
telescope time in 2019 in late August and late September, we planned our
fields so that both the opposition and quadrature observations were available
during those time periods — for the early fields, opposition in late August
and quadrature in late September, and for the late fields, quadrature in late
August and opposition in late September. Given these constraints, plus the
goal of avoiding bright stars and the fact that the full field pattern of 15
fields wasn’t determined until 2022, we report the 2022 epoch fields in Table
5 for the B semester.

Table 3: Summary of field observations for the DEEP project as of 2022B. Sky coordinates are given as right ascension ($\alpha$) and declination ($\delta$). The value $t_{\rm eff}\approx 1$ is an empirical measure of efficiency roughly consistent with seeing full-width-at-half-maximum of 0.95 arc-seconds under clear, moonless skies – optimal conditions with DECam, which is described in detail in Equation 4 of Morganson et al. (2018). $N$ is number of 120 s observations, $\chi$ is mean airmass, $\theta$ is mean seeing full-width-at-half-maximum, $m_{\rm sky}$ is mean sky brightness, and $m_{\rm ext}$ is mean extinction. Computations were performed with algorithms supplied as part of the Dark Energy Survey Image Processing Pipeline (Morganson et al., 2018).  Local Date | Night | UT Time | Field | $\alpha$ | $\delta$ | $N$ | $t_{\rm eff}$ | $\chi$ | $\theta$ | $m_{\rm sky}$ | $m_{\rm ext}$
---|---|---|---|---|---|---|---|---|---|---|---
Start of Night | Fraction | Range | Name | [°] | [°] |  |  |  | [″] |  |
2019/04/01 | second half | 05:13–05:18,09:36–09:41 | A0c | 216.5 | -12.0 | 6 | 2.32 | 1.29 | 1.52 | 0.02 | 0.00
2019/04/01 | second half | 05:21–09:22 | A0a | 214.7 | -12.3 | 98 | 53.33 | 1.14 | 1.37 | 0.00 | 0.00
2019/04/01 | second half | 05:06–05:11,09:25–09:33 | A0b | 215.9 | -13.6 | 7 | 2.78 | 1.28 | 1.49 | 0.01 | 0.07
2019/04/02 | second half | 05:09–09:22 | A0b | 215.9 | -13.6 | 103 | 65.19 | 1.12 | 1.27 | 0.00 | 0.00
2019/04/02 | second half | 04:53–04:58,09:24–09:30 | A0a | 214.7 | -12.3 | 6 | 3.32 | 1.29 | 1.35 | 0.00 | 0.00
2019/04/02 | second half | 05:01–05:06,09:32–09:37 | A0c | 216.5 | -12.0 | 6 | 3.39 | 1.30 | 1.33 | 0.00 | 0.00
2019/04/03 | second half | 05:07–09:24 | A0c | 216.5 | -12.0 | 104 | 31.37 | 1.14 | 1.70 | 0.07 | 0.00
2019/04/03 | second half | 04:47,09:28–09:37 | A0a | 214.7 | -12.3 | 5 | 0.84 | 1.45 | 2.44 | 0.33 | 0.00
2019/04/03 | second half | 05:00–05:05,09:39–10:00 | A0b | 215.9 | -13.6 | 12 | 1.94 | 1.47 | 2.16 | 0.57 | 0.15
2019/05/03 | full night | 01:02–04:38 | A0a | 214.7 | -12.3 | 88 | 51.40 | 1.16 | 1.02 | 0.40 | 0.00
2019/05/03 | full night | 00:47–00:52,04:41–04:46 | A0b | 215.9 | -13.6 | 6 | 3.39 | 1.31 | 1.04 | 0.44 | 0.00
2019/05/03 | full night | 00:55–01:00,04:48–04:53 | A0c | 216.5 | -12.0 | 6 | 3.00 | 1.31 | 1.10 | 0.42 | 0.00
2019/05/03 | full night | 06:15–09:17 | A1a | 251.6 | -21.5 | 74 | 54.28 | 1.07 | 0.93 | 0.37 | 0.00
2019/05/03 | full night | 06:00,09:49–09:54 | A1b | 253.1 | -22.6 | 4 | 1.92 | 1.27 | 1.04 | 0.58 | 0.00
2019/05/03 | full night | 06:07–06:12,09:19–09:46 | A1c | 253.4 | -20.8 | 15 | 6.30 | 1.23 | 1.05 | 0.59 | 0.25
2019/05/04 | full night | 00:39–00:44,04:40–04:45 | A0a | 214.7 | -12.3 | 6 | 2.94 | 1.32 | 1.27 | 0.16 | 0.00
2019/05/04 | full night | 00:47–00:52,04:48–04:53 | A0b | 215.9 | -13.6 | 6 | 2.98 | 1.30 | 1.23 | 0.20 | 0.04
2019/05/04 | full night | 00:54–04:38 | A0c | 216.5 | -12.0 | 91 | 52.84 | 1.18 | 1.13 | 0.10 | 0.00
2019/05/04 | full night | 06:01–06:06,09:38–09:43 | A1a | 251.6 | -21.5 | 5 | 1.70 | 1.19 | 1.29 | 0.29 | 1.23
2019/05/04 | full night | 06:09–06:14,09:46–09:50 | A1b | 253.1 | -22.6 | 6 | 1.54 | 1.18 | 1.26 | 0.42 | 2.34
2019/05/04 | full night | 06:16–09:36 | A1c | 253.4 | -20.8 | 70 | 4.88 | 1.09 | 1.23 | 0.49 | 2.49
2019/05/05 | full night | no data collected | — | — | — | — | — | — | — | — | —
2019/05/06 | full night | 00:52–00:57,04:40–04:45 | A0a | 214.7 | -12.3 | 6 | 5.29 | 1.26 | 1.00 | 0.09 | 0.00
2019/05/06 | full night | 01:07–04:38 | A0b | 215.9 | -13.6 | 86 | 23.68 | 1.13 | 0.99 | 0.22 | 1.22
2019/05/06 | full night | 00:59–01:04,04:48–04:53 | A0c | 216.5 | -12.0 | 6 | 5.82 | 1.26 | 0.97 | 0.07 | 0.00
2019/05/06 | full night | 05:58–06:03,09:40 | A1a | 251.6 | -21.5 | 4 | 0.11 | 1.11 | 1.10 | 0.40 | 3.29
2019/05/06 | full night | 06:13–09:37 | A1b | 253.1 | -22.6 | 79 | 27.93 | 1.09 | 1.03 | 0.37 | 1.09
2019/05/06 | full night | 06:06–06:11,09:47 | A1c | 253.4 | -20.8 | 4 | 0.07 | 1.12 | 1.02 | 0.42 | 3.13
2019/06/01 | first half | 00:09–00:14 | A1a | 251.0 | -21.4 | 3 | 0.69 | 2.12 | 1.59 | 0.47 | 0.00
2019/06/01 | first half | 00:24–04:38 | A1b | 252.5 | -22.5 | 99 | 63.78 | 1.29 | 1.09 | 0.21 | 0.00
2019/06/01 | first half | 00:16–00:21 | A1c | 252.8 | -20.7 | 3 | 0.78 | 2.13 | 1.47 | 0.49 | 0.00
2019/06/02 | first half | 23:25–23:33 | A0a | 213.6 | -11.9 | 4 | 1.40 | 1.35 | 0.92 | 1.10 | 0.00
2019/06/02 | first half | 00:40–04:37 | A1a | 251.0 | -21.4 | 96 | 71.78 | 1.22 | 1.04 | 0.11 | 0.00
2019/06/03 | first half | 23:31–23:58 | A0a | 213.6 | -11.9 | 12 | 7.31 | 1.28 | 0.98 | 0.35 | 0.00
2019/06/03 | first half | 00:04–00:09,04:22–04:29 | A1a | 251.0 | -21.4 | 8 | 2.97 | 1.41 | 1.43 | 0.17 | 0.00
2019/06/03 | first half | 00:14–00:16,04:34–04:39 | A1b | 252.5 | -22.5 | 6 | 2.40 | 1.52 | 1.31 | 0.26 | 0.05
2019/06/03 | first half | 00:19–04:19 | A1c | 252.8 | -20.7 | 97 | 28.39 | 1.31 | 1.46 | 0.21 | 0.41
2019/07/06 | second half | 04:49–07:17 | B0a | 308.8 | -19.2 | 60 | 2.11 | 1.04 | 1.37 | 0.23 | 2.33
2019/07/06 | second half | 04:33–04:39 | B0b | 310.6 | -19.7 | 3 | 1.29 | 1.13 | 1.52 | 0.00 | 0.13
2019/07/06 | second half | 04:41–04:46 | B0c | 310.2 | -18.0 | 3 | 0.58 | 1.11 | 1.78 | 0.01 | 0.44
2019/07/07 | second half | 05:11–08:32 | B0a | 308.8 | -19.2 | 80 | 2.65 | 1.06 | 1.44 | 0.09 | 2.15
2019/07/07 | second half | 04:55–05:01 | B0b | 310.6 | -19.7 | 3 | 0.07 | 1.08 | 1.40 | 0.14 | 1.98
2019/07/07 | second half | 05:03–05:06 | B0c | 310.2 | -18.0 | 2 | 0.00 | 1.08 | 1.54 | 0.24 | 3.26
2019/07/08 | second half | 05:12–10:31 | B0a | 308.8 | -19.2 | 121 | 11.99 | 1.20 | 1.14 | 0.30 | 2.37
2019/07/08 | second half | 04:55–05:01,09:59-10:05 | B0b | 310.6 | -19.7 | 6 | 0.61 | 1.34 | 1.42 | 0.26 | 2.21
2019/07/08 | second half | 05:04–05:10,10:07-10:12 | B0c | 310.2 | -18.0 | 6 | 0.35 | 1.38 | 1.36 | 0.33 | 2.41
2019/08/27 | full night | 23:35–23:40,04:37–04:42 | B0a | 307.8 | -19.4 | 6 | 5.12 | 1.27 | 1.05 | 0.11 | 0.00
2019/08/27 | full night | 23:15–23:32,04:29–04:34 | B0b | 309.6 | -19.9 | 11 | 5.20 | 1.42 | 1.28 | 0.93 | 0.19
2019/08/27 | full night | 23:42–04:26 | B0c | 309.2 | -18.2 | 115 | 98.31 | 1.11 | 1.06 | 0.00 | 0.00
2019/08/27 | full night | 04:57–04:59,09:27–09:32 | B1a | 351.9 | -5.0 | 6 | 5.60 | 1.51 | 1.00 | 0.15 | 0.00
2019/08/27 | full night | 04:46–04:52,09:20–09:25 | B1b | 353.6 | -5.3 | 6 | 5.06 | 1.44 | 1.02 | 0.12 | 0.00
2019/08/27 | full night | 05:02–09:17 | B1c | 352.9 | -3.6 | 103 | 62.55 | 1.27 | 1.17 | 0.06 | 0.06
2019/08/28 | full night | 23:43–04:27 | B0a | 307.8 | -19.4 | 115 | 135.40 | 1.10 | 0.95 | 0.00 | 0.00
2019/08/28 | full night | 23:35–23:40,04:39–04:44 | B0b | 309.6 | -19.9 | 6 | 5.96 | 1.27 | 1.00 | 0.00 | 0.00
2019/08/28 | full night | 23:14–23:33,04:29–04:34 | B0c | 309.2 | -18.2 | 9 | 5.96 | 1.38 | 1.02 | 0.81 | 0.00
2019/08/28 | full night | 05:03–09:15 | B1a | 351.9 | -5.0 | 102 | 53.31 | 1.27 | 1.48 | 0.00 | 0.00
2019/08/28 | full night | 04:45–05:01,09:28–09:33 | B1b | 353.6 | -5.3 | 6 | 4.06 | 1.49 | 1.28 | 0.01 | 0.00
2019/08/28 | full night | 04:48–04:53,09:18–09:25 | B1c | 352.9 | -3.6 | 7 | 4.67 | 1.55 | 1.28 | 0.05 | 0.00
2019/08/29 | full night | 23:17–23:34,04:28–04:35 | B0a | 307.8 | -19.4 | 12 | 6.60 | 1.33 | 1.15 | 0.76 | 0.00
2019/08/29 | full night | 23:45–04:26 | B0b | 309.6 | -19.9 | 114 | 121.76 | 1.10 | 1.00 | 0.00 | 0.00
2019/08/29 | full night | 23:36–23:41,04:38–04:43 | B0c | 309.2 | -18.2 | 6 | 5.65 | 1.27 | 1.01 | 0.00 | 0.00
2019/08/29 | full night | 04:47–04:54,09:18–09:25 | B1a | 351.9 | -5.0 | 8 | 6.57 | 1.51 | 1.17 | 0.00 | 0.00
2019/08/29 | full night | 05:04–09:15 | B1b | 353.6 | -5.3 | 101 | 113.41 | 1.25 | 0.98 | 0.00 | 0.00
2019/08/29 | full night | 04:56–05:01,09:30–09:33 | B1c | 352.9 | -3.6 | 6 | 5.33 | 1.56 | 1.12 | 0.00 | 0.00
2019/09/26 | first half | 00:19–04:16 | B1a | 351.4 | -5.2 | 97 | 57.57 | 1.26 | 1.25 | 0.00 | 0.00
2019/09/26 | first half | 00:04–00:09,04:23–04:28 | B1b | 353.1 | -5.5 | 6 | 1.40 | 1.52 | 1.88 | 0.22 | 0.00
2019/09/26 | first half | 00:11–00:16,04:18–04:21 | B1c | 352.4 | -3.8 | 5 | 1.43 | 1.57 | 1.62 | 0.21 | 0.00
2019/09/27 | first half | 00:01–00:06,04:17–04:22 | B1a | 351.4 | -5.2 | 6 | 1.74 | 1.49 | 1.77 | 0.37 | 0.00
2019/09/27 | first half | 00:16–04:14 | B1b | 353.1 | -5.5 | 97 | 48.07 | 1.28 | 1.28 | 0.22 | 0.00
2019/09/27 | first half | 00:08–00:13,04:24–04:29 | B1c | 352.4 | -3.8 | 6 | 1.86 | 1.50 | 1.69 | 0.38 | 0.00
2019/09/28 | first half | 00:02–00:07,04:17–04:22 | B1a | 351.4 | -5.2 | 6 | 5.25 | 1.46 | 1.07 | 0.10 | 0.00
2019/09/28 | first half | 00:10–00:15,04:24–04:29 | B1b | 353.1 | -5.5 | 6 | 5.14 | 1.46 | 1.10 | 0.08 | 0.00
2019/09/28 | first half | 00:18–04:13 | B1c | 352.4 | -3.8 | 96 | 84.00 | 1.28 | 1.06 | 0.00 | 0.00
2020/04/18 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/04/19 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/04/20 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/04/21 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/04/22 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/04/23 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/05/17 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/05/18 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/05/19 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/05/20 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/05/21 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/05/22 | second half | no data collected | — | — | — | — | — | — | — | — | —
2020/10/15 | first half | 23:58–04:11 | B1b | 352.9 | -5.6 | 103 | 59.94 | 1.17 | 1.38 | 0.00 | 0.00
2020/10/15 | first half | 23:53–23:56,04:14–04:18 | B1c | 352.2 | -3.9 | 6 | 3.38 | 1.34 | 1.16 | 0.39 | 0.00
2020/10/15 | first half | 23:43–23:48,04:21–04:26 | B1e | 353.9 | -4.2 | 6 | 2.57 | 1.37 | 1.28 | 0.99 | 0.00
2020/10/16 | first half | 23:43–23:48,04:12–04:17 | B1b | 352.9 | -5.6 | 6 | 2.84 | 1.33 | 1.08 | 1.17 | 0.00
2020/10/16 | first half | 00:13–04:09 | B1c | 352.2 | -3.9 | 96 | 84.45 | 1.17 | 1.07 | 0.00 | 0.00
2020/10/16 | first half | 23:58–00:03 | B1d | 354.6 | -5.9 | 3 | 1.37 | 1.40 | 1.16 | 0.41 | 0.00
2020/10/16 | first half | 23:51–23:56,04:19–04:24 | B1e | 353.9 | -4.2 | 6 | 3.32 | 1.34 | 1.13 | 0.53 | 0.00
2020/10/16 | first half | 00:06–00:11 | B1f | 353.2 | -2.5 | 3 | 1.87 | 1.39 | 1.11 | 0.15 | 0.00
2020/10/17 | first half | 23:45–23:50,04:14–04:19 | B1b | 352.9 | -5.6 | 6 | 3.48 | 1.32 | 1.17 | 1.02 | 0.00
2020/10/17 | first half | 23:52–23:58,04:21–04:26 | B1c | 352.2 | -3.9 | 6 | 3.26 | 1.34 | 1.16 | 0.40 | 0.00
2020/10/17 | first half | 00:00–00:05 | B1d | 354.6 | -5.9 | 3 | 1.16 | 1.37 | 1.25 | 0.41 | 0.00
2020/10/17 | first half | 00:15–04:12 | B1e | 353.9 | -4.2 | 95 | 88.66 | 1.17 | 1.03 | 0.00 | 0.00
2020/10/17 | first half | 00:08–00:13 | B1f | 353.2 | -2.5 | 3 | 1.50 | 1.37 | 1.23 | 0.16 | 0.00
2020/10/18 | first half | 00:20–04:04 | B1a | 351.1 | -5.3 | 91 | 104.06 | 1.15 | 0.89 | 0.00 | 0.00
2020/10/18 | first half | 00:11–00:16 | B1b | 352.9 | -5.6 | 3 | 3.14 | 1.29 | 0.84 | 0.18 | 0.00
2020/10/18 | first half | 23:48–23:53,04:06–04:11 | B1d | 354.6 | -5.9 | 6 | 2.91 | 1.31 | 1.06 | 0.83 | 0.00
2020/10/18 | first half | 23:56–00:01,04:14–04:18 | B1e | 353.9 | -4.2 | 6 | 3.41 | 1.32 | 1.06 | 0.38 | 0.00
2020/10/18 | first half | 00:03–00:08,04:21–04:26 | B1f | 353.2 | -2.5 | 6 | 4.32 | 1.33 | 1.02 | 0.18 | 0.00
2020/10/19 | first half | 23:49–23:54,04:09–04:17 | B1a | 351.1 | -5.3 | 7 | 1.76 | 1.30 | 1.56 | 0.74 | 0.00
2020/10/19 | first half | 23:56–00:01,04:19–04:24 | B1c | 352.2 | -3.9 | 6 | 2.32 | 1.32 | 1.35 | 0.45 | 0.00
2020/10/19 | first half | 00:04–04:07 | B1d | 354.6 | -5.9 | 99 | 51.48 | 1.16 | 1.24 | 0.16 | 0.00
2020/10/20 | first half | 23:47–23:59,04:11–04:21 | B1b | 352.9 | -5.6 | 6 | 2.61 | 1.30 | 1.27 | 0.90 | 0.00
2020/10/20 | first half | 23:52–00:02,04:14–04:24 | B1e | 353.9 | -4.2 | 6 | 2.87 | 1.32 | 1.24 | 0.69 | 0.00
2020/10/20 | first half | 00:04–04:09 | B1f | 353.2 | -2.5 | 98 | 64.84 | 1.19 | 1.10 | 0.20 | 0.00
2020/10/21 | first half | 23:49–23:59,04:11–04:22 | B1a | 351.1 | -5.3 | 7 | 3.30 | 1.30 | 1.04 | 0.86 | 0.00
2020/10/21 | first half | 23:51–00:01,04:14–04:24 | B1c | 352.2 | -3.9 | 6 | 2.61 | 1.32 | 1.10 | 0.82 | 0.00
2020/10/21 | first half | 00:04–04:08 | B1d | 354.6 | -5.9 | 99 | 75.08 | 1.15 | 0.96 | 0.27 | 0.00
2021/05/03 | first half | 00:28–00:33,04:34–04:39 | A0f | 217.7 | -11.5 | 6 | 1.04 | 1.46 | 2.09 | 0.18 | 0.00
2021/05/03 | first half | 00:14–00:26,04:24–04:31 | A0i | 218.9 | -12.8 | 8 | 1.53 | 1.53 | 2.03 | 0.18 | 0.15
2021/05/03 | first half | 00:36–04:22 | A0j | 219.5 | -11.2 | 92 | 29.16 | 1.30 | 1.83 | 0.00 | 0.05
2021/05/04 | first half | 02:29–04:38 | A0f | 217.7 | -11.5 | 53 | 26.49 | 1.09 | 1.48 | 0.00 | 0.00
2021/05/05 | no time scheduled | no data collected | — | — | — | — | — | — | — | — | —
2021/05/06 | full night | 00:20–01:52 | A0i | 218.9 | -12.8 | 26 | 0.08 | 1.51 | 1.68 | 0.47 | 3.12
2021/05/06 | full night | 02:54–10:30 | A1j | 256.7 | -19.3 | 179 | 22.10 | 1.19 | 1.69 | 0.39 | 1.88
2021/05/07 | full night | 23:53–00:00,05:18–05:24 | A0e | 217.1 | -13.1 | 6 | 0.29 | 1.55 | 1.90 | 0.34 | 2.01
2021/05/07 | full night | 00:04–05:05 | A0i | 218.9 | -12.8 | 121 | 43.06 | 1.26 | 1.54 | 0.05 | 0.26
2021/05/07 | full night | 23:46–23:51,05:09–05:14 | A0j | 219.5 | -11.2 | 6 | 0.42 | 1.72 | 1.68 | 0.33 | 1.14
2021/05/07 | full night | 05:36–05:41 | A1e | 254.7 | -21.9 | 2 | 0.00 | 1.04 | 1.31 | 0.33 | 5.57
2021/05/07 | full night | 05:45–10:28 | A1f | 255.0 | -20.1 | 88 | 0.39 | 1.17 | 1.35 | 0.65 | 3.87
2021/05/07 | full night | 05:28–05:34 | A1i | 256.4 | -21.1 | 3 | 0.00 | 1.06 | 1.42 | 0.42 | 3.41
2021/05/08 | no time scheduled | no data collected | — | — | — | — | — | — | — | — | —
2021/05/09 | full night | 23:55–02:39 | A0g | 217.8 | -16.1 | 66 | 5.03 | 1.37 | 1.33 | 0.40 | 2.07
2021/05/09 | full night | 06:21–06:24,10:20–10:25 | A1e | 254.7 | -21.9 | 5 | 0.33 | 1.39 | 1.07 | 1.52 | 0.86
2021/05/09 | full night | 06:26–10:10 | A1f | 255.0 | -20.1 | 68 | 5.61 | 1.18 | 1.06 | 0.60 | 1.83
2021/05/09 | full night | 06:11–06:16,10:13-10:18 | A1i | 256.4 | -21.1 | 6 | 0.32 | 1.28 | 1.14 | 0.86 | 1.35
2021/05/10 | full night | 23:59–00:10,04:01–04:06 | A0a | 214.1 | -12.1 | 7 | 4.68 | 1.41 | 1.18 | 0.03 | 0.00
2021/05/10 | full night | 00:13–00:18,04:08–04:13 | A0b | 215.3 | -13.4 | 6 | 4.56 | 1.34 | 1.10 | 0.02 | 0.00
2021/05/10 | full night | 00:20–03:53 | A0c | 215.9 | -11.8 | 73 | 58.44 | 1.21 | 1.09 | 0.00 | 0.00
2021/05/10 | full night | 04:17–04:22,10:17–10:22 | A1c | 253.2 | -20.8 | 6 | 2.68 | 1.43 | 1.42 | 0.73 | 0.05
2021/05/10 | full night | 04:35–06:59 | A1f | 255.0 | -20.1 | 40 | 25.38 | 1.04 | 1.15 | 0.11 | 0.14
2021/05/10 | full night | 10:25–10:30 | A1h | 256.2 | -22.9 | 3 | 0.11 | 1.64 | 1.53 | 2.57 | 0.00
2021/05/10 | full night | 08:06–10:15 | A1i | 256.4 | -21.1 | 45 | 12.95 | 1.30 | 1.47 | 0.44 | 0.10
2021/05/11 | no time scheduled | no data collected | — | — | — | — | — | — | — | — | —
2021/05/12 | full night | 23:01–23:03,01:05–01:10 | A0a | 214.1 | -12.1 | 5 | 1.30 | 1.74 | 1.54 | 0.48 | 0.14
2021/05/12 | full night | 01:12–01:15 | A0c | 215.9 | -11.8 | 2 | 1.07 | 1.30 | 1.21 | 0.14 | 0.00
2021/05/12 | full night | 01:20–01:59 | A0e | 217.1 | -13.1 | 17 | 9.22 | 1.21 | 1.06 | 0.13 | 0.35
2021/05/13 | full night | 23:24–23:29 | A0b | 215.3 | -13.4 | 3 | 1.28 | 1.99 | 1.15 | 0.48 | 0.00
2021/05/13 | full night | 23:32–23:37 | A0d | 216.6 | -14.8 | 3 | 1.54 | 1.92 | 1.07 | 0.44 | 0.05
2021/05/13 | full night | 23:39–03:45 | A0e | 217.1 | -13.1 | 100 | 110.09 | 1.28 | 0.91 | 0.01 | 0.00
2021/05/13 | full night | 03:49–07:10 | A1c | 253.2 | -20.8 | 82 | 94.55 | 1.06 | 0.87 | 0.03 | 0.00
2021/05/13 | full night | 07:17–10:30 | A1e | 254.7 | -21.9 | 48 | 28.99 | 1.33 | 1.11 | 0.40 | 0.07
2021/05/14 | no time scheduled | no data collected | — | — | — | — | — | — | — | — | —
2021/05/15 | full night | 02:15–06:33 | A0h | 218.4 | -14.4 | 101 | 66.56 | 1.11 | 1.19 | 0.01 | 0.12
2021/05/15 | full night | 06:37–10:31 | A1h | 256.2 | -22.9 | 74 | 27.91 | 1.27 | 1.33 | 0.40 | 0.21
2021/05/16 | full night | 23:18–03:09 | A0a | 214.1 | -12.1 | 94 | 70.16 | 1.30 | 1.04 | 0.16 | 0.00
2021/05/16 | full night | 03:28–06:48 | A0b | 215.3 | -13.4 | 79 | 72.86 | 1.19 | 0.99 | 0.00 | 0.00
2021/05/16 | full night | 22:48–22:53,03:13–03:18 | A0d | 216.6 | -14.8 | 6 | 2.01 | 1.72 | 1.30 | 1.26 | 0.15
2021/05/16 | full night | 22:56–23:01,03:20–03:25 | A0g | 217.8 | -16.1 | 6 | 2.30 | 1.67 | 1.29 | 0.69 | 0.15
2021/05/16 | full night | 06:52–10:30 | A1a | 251.5 | -21.4 | 89 | 53.49 | 1.39 | 1.07 | 0.43 | 0.00
2021/05/17 | no time scheduled | no data collected | — | — | — | — | — | — | — | — | —
2021/05/18 | second half | 04:38–04:43 | A1a | 251.5 | -21.4 | 3 | 0.56 | 1.04 | 2.00 | 0.00 | 0.44
2021/05/18 | second half | 04:53–04:58 | A1b | 253.0 | -22.6 | 3 | 0.79 | 1.03 | 1.88 | 0.00 | 0.26
2021/05/18 | second half | 05:23–07:43 | A1d | 254.5 | -23.7 | 57 | 2.96 | 1.03 | 1.74 | 0.19 | 1.58
2021/05/18 | second half | 05:01–05:05 | A1e | 254.7 | -21.9 | 3 | 0.23 | 1.03 | 2.23 | 0.00 | 0.76
2021/05/18 | second half | 05:10–05:13 | A1f | 255.0 | -20.1 | 2 | 0.11 | 1.03 | 2.47 | 0.00 | 0.81
2021/05/18 | second half | 04:46–04:51 | A1h | 256.2 | -22.9 | 3 | 0.45 | 1.05 | 2.32 | 0.00 | 0.29
2021/05/18 | second half | 05:15–05:20 | A1j | 256.7 | -19.3 | 3 | 0.08 | 1.03 | 2.55 | 0.02 | 1.05
2021/05/19 | second half | no data collected | — | — | — | — | — | — | — | — | —
2021/09/03 | first half | 23:25–02:07 | B0b | 309.4 | -19.9 | 66 | 31.86 | 1.13 | 1.44 | 0.00 | 0.08
2021/09/03 | first half | 02:11–04:40 | B1f | 353.8 | -2.3 | 61 | 24.51 | 1.34 | 1.66 | 0.00 | 0.00
2021/09/04 | first half | 23:21–02:12 | B0a | 307.6 | -19.4 | 70 | 49.42 | 1.12 | 1.18 | 0.03 | 0.00
2021/09/04 | first half | 02:16–04:40 | B1e | 354.5 | -3.9 | 59 | 35.91 | 1.30 | 1.33 | 0.00 | 0.00
2021/09/05 | first half | 23:21–00:14 | B0a | 307.6 | -19.4 | 12 | 6.06 | 1.22 | 1.11 | 0.56 | 0.00
2021/09/05 | first half | 23:29–02:01 | B0c | 309.0 | -18.3 | 52 | 46.85 | 1.10 | 0.98 | 0.08 | 0.00
2021/09/05 | first half | 02:05–02:25 | B1e | 354.5 | -3.9 | 6 | 3.14 | 1.60 | 1.26 | 0.08 | 0.00
2021/09/05 | first half | 02:12–02:32 | B1f | 353.8 | -2.3 | 6 | 3.61 | 1.56 | 1.18 | 0.05 | 0.00
2021/09/05 | first half | 02:35–04:41 | B1i | 355.5 | -2.5 | 52 | 28.12 | 1.29 | 1.40 | 0.00 | 0.05
2021/09/06 | first half | 23:22–23:27 | B0c | 309.0 | -18.3 | 3 | 0.47 | 1.32 | 1.25 | 1.49 | 0.00
2021/09/06 | first half | 23:29–02:18 | B0f | 310.4 | -17.1 | 69 | 41.17 | 1.12 | 1.23 | 0.01 | 0.05
2021/09/06 | first half | 02:30–04:39 | B1c | 352.8 | -3.7 | 53 | 30.45 | 1.24 | 1.29 | 0.00 | 0.00
2021/09/06 | first half | 02:22–02:27 | B1i | 355.5 | -2.5 | 3 | 1.17 | 1.55 | 1.43 | 0.11 | 0.08
2021/09/07 | first half | 23:25–23:30 | B0a | 307.6 | -19.4 | 3 | 0.03 | 1.26 | 5.13 | 1.18 | 0.19
2021/09/07 | first half | 23:33–02:04 | B0d | 311.3 | -20.4 | 62 | 3.19 | 1.11 | 4.54 | 0.00 | 0.15
2021/09/07 | first half | 02:08–02:13 | B1c | 352.8 | -3.7 | 3 | 0.03 | 1.53 | 7.94 | 0.00 | 0.12
2021/09/07 | first half | 02:16–04:37 | B1h | 356.2 | -4.2 | 58 | 2.02 | 1.29 | 5.83 | 0.00 | 0.00
2021/09/08 | first half | 23:22–23:27 | B0a | 307.6 | -19.4 | 3 | 0.36 | 1.25 | 1.18 | 1.83 | 0.00
2021/09/08 | first half | 23:29–01:11 | B0d | 311.3 | -20.4 | 42 | 15.13 | 1.14 | 1.37 | 0.32 | 0.16
2021/09/08 | first half | 01:36–01:41 | B1c | 352.8 | -3.7 | 3 | 1.24 | 1.73 | 1.25 | 0.34 | 0.02
2021/09/08 | first half | 01:44–04:35 | B1h | 356.2 | -4.2 | 70 | 32.49 | 1.35 | 1.36 | 0.06 | 0.08
2021/09/09 | first half | 23:35–23:50 | B0a | 307.6 | -19.4 | 4 | 4.40 | 1.18 | 0.83 | 0.19 | 0.00
2021/09/09 | first half | 00:00–01:37 | B0c | 309.0 | -18.3 | 40 | 50.56 | 1.08 | 0.83 | 0.02 | 0.00
2021/09/09 | first half | 23:53–23:58 | B0d | 311.3 | -20.4 | 3 | 2.37 | 1.18 | 1.41 | 0.14 | 0.00
2021/09/09 | first half | 23:38–23:43 | B0f | 310.4 | -17.1 | 3 | 2.98 | 1.24 | 0.85 | 0.21 | 0.00
2021/09/09 | first half | 01:57–04:36 | B1a | 351.7 | -5.1 | 65 | 63.82 | 1.24 | 1.08 | 0.00 | 0.00
2021/09/09 | first half | 01:42–01:47 | B1b | 353.5 | -5.4 | 3 | 1.77 | 1.63 | 1.23 | 0.00 | 0.00
2021/09/09 | first half | 01:50–01:55 | B1e | 354.5 | -3.9 | 3 | 2.30 | 1.64 | 1.08 | 0.00 | 0.00
2021/09/10 | first half | 23:19–00:59 | B0a | 307.6 | -19.4 | 41 | 3.93 | 1.12 | 1.08 | 1.08 | 1.95
2021/09/10 | first half | 01:05–04:06 | B1a | 351.7 | -5.1 | 66 | 10.51 | 1.36 | 1.05 | 0.24 | 2.20
2021/09/11 | first half | no data collected | — | — | — | — | — | — | — | — | —
2021/09/12 | first half | 23:24–01:14 | B0e | 310.8 | -18.7 | 45 | 14.32 | 1.12 | 1.33 | 0.68 | 0.01
2021/09/12 | first half | 01:18–04:09 | B1b | 353.5 | -5.4 | 70 | 20.78 | 1.33 | 1.52 | 0.39 | 0.03
2021/09/27 | first half | 00:16–03:54 | B1d | 354.8 | -5.8 | 89 | 78.75 | 1.30 | 1.03 | 0.00 | 0.00
2021/09/27 | first half | 00:01–00:06,04:04–04:09 | B1g | 356.5 | -6.0 | 6 | 3.78 | 1.58 | 1.22 | 0.11 | 0.00
2021/09/27 | first half | 23:53–00:14,03:57–04:02 | B1j | 354.4 | -1.0 | 9 | 4.39 | 1.80 | 1.25 | 0.27 | 0.00
2021/09/28 | first half | 00:05–00:09,04:00–04:05 | B1d | 354.8 | -5.8 | 6 | 3.81 | 1.49 | 1.30 | 0.13 | 0.24
2021/09/28 | first half | 00:12–03:57 | B1g | 356.5 | -6.0 | 92 | 62.64 | 1.32 | 1.05 | 0.10 | 0.29
2021/09/28 | first half | 23:57–00:02,04:07–04:12 | B1j | 354.4 | -1.0 | 6 | 4.15 | 1.64 | 1.19 | 0.19 | 0.03
2021/09/29 | first half | no data collected | — | — | — | — | — | — | — | — | —
2021/09/30 | first half | 00:11–00:16,03:58–04:05 | B1d | 354.8 | -5.8 | 7 | 1.58 | 1.37 | 2.03 | 0.22 | 0.05
2021/09/30 | first half | 00:20–00:24,04:07–04:12 | B1g | 356.5 | -6.0 | 6 | 1.27 | 1.41 | 1.86 | 0.24 | 0.00
2021/09/30 | first half | 00:27–03:55 | B1j | 354.4 | -1.0 | 85 | 33.11 | 1.31 | 1.46 | 0.22 | 0.00
2021/10/01 | first half | 00:37–03:56 | B1b | 353.1 | -5.5 | 81 | 84.68 | 1.21 | 0.88 | 0.14 | 0.00
2021/10/01 | first half | 00:20–00:27,03:58–04:06 | B1f | 353.4 | -2.4 | 8 | 8.55 | 1.40 | 0.86 | 0.29 | 0.00
2021/10/01 | first half | 00:30–00:35,04:08–04:13 | B1i | 355.1 | -2.7 | 6 | 5.96 | 1.39 | 0.88 | 0.30 | 0.00
2021/10/02 | first half | 23:58–00:05,03:58–04:06 | B1b | 353.1 | -5.5 | 8 | 5.73 | 1.42 | 1.01 | 0.30 | 0.00
2021/10/02 | first half | 00:15–03:56 | B1f | 353.4 | -2.4 | 89 | 84.73 | 1.27 | 0.90 | 0.19 | 0.00
2021/10/02 | first half | 00:08–00:13,04:08–04:13 | B1i | 355.1 | -2.7 | 6 | 4.51 | 1.47 | 0.99 | 0.29 | 0.00
2021/10/03 | first half | 23:28–23:46,04:13 | B1b | 353.1 | -5.5 | 4 | 1.40 | 1.69 | 1.12 | 1.28 | 0.00
2021/10/03 | first half | 23:50–23:58 | B1f | 353.4 | -2.4 | 3 | 0.94 | 1.85 | 1.58 | 0.59 | 0.00
2021/10/03 | first half | 00:02–04:10 | B1i | 355.1 | -2.7 | 100 | 84.57 | 1.30 | 0.99 | 0.10 | 0.00
2021/10/04 | first half | 23:58–04:08 | B1c | 352.4 | -3.9 | 101 | 81.26 | 1.25 | 1.11 | 0.00 | 0.00
2021/10/04 | first half | 23:39–23:48 | B1e | 354.1 | -4.1 | 4 | 0.50 | 1.91 | 1.36 | 1.60 | 0.00
2021/10/04 | first half | 23:50–23:55,04:10–04:15 | B1h | 355.8 | -4.4 | 6 | 5.44 | 1.50 | 1.04 | 0.25 | 0.04
2021/10/05 | first half | 23:44–23:52,04:02–04:07 | B1c | 352.4 | -3.9 | 7 | 3.70 | 1.49 | 1.28 | 0.68 | 0.00
2021/10/05 | first half | 23:54–23:59,04:10–04:15 | B1e | 354.1 | -4.1 | 6 | 4.69 | 1.44 | 1.00 | 0.36 | 0.00
2021/10/05 | first half | 00:02–04:00 | B1h | 355.8 | -4.4 | 97 | 73.04 | 1.27 | 1.02 | 0.17 | 0.01
2021/10/06 | first half | 23:50-23:55,04:05–04:10 | B1c | 352.4 | -3.9 | 5 | 2.51 | 1.48 | 1.17 | 0.53 | 0.00
2021/10/06 | first half | 00:05–04:03 | B1e | 354.1 | -4.1 | 97 | 72.75 | 1.24 | 1.09 | 0.07 | 0.00
2021/10/06 | first half | 23:57–00:02,04:08–04:13 | B1h | 355.8 | -4.4 | 5 | 2.58 | 1.49 | 1.16 | 0.36 | 0.02
2022/05/25 | first half | 23:04–02:05 | A0a | 213.7 | -12.0 | 74 | 79.04 | 1.27 | 0.84 | 0.21 | 0.00
2022/05/25 | first half | 22:50–22:59,04:33–04:38 | A0b | 215.0 | -13.3 | 6 | 4.26 | 1.48 | 0.96 | 1.07 | 0.00
2022/05/25 | first half | 22:52–04:30 | A0f | 217.3 | -11.4 | 61 | 75.58 | 1.12 | 0.77 | 0.04 | 10.87
2022/05/26 | first half | 22:53–23:23 | A0a | 213.7 | -12.0 | 5 | 1.38 | 1.64 | 1.28 | 1.06 | 0.00
2022/05/26 | first half | 23:26–02:19 | A0b | 215.0 | -13.3 | 71 | 79.08 | 1.21 | 0.88 | 0.09 | 0.00
2022/05/26 | first half | 22:51–04:38 | A0c | 215.5 | -11.7 | 61 | 79.44 | 1.14 | 0.86 | 0.03 | 0.00
2022/05/26 | first half | 22:48–23:18 | A0f | 217.3 | -11.4 | 5 | -0.16 | 1.83 | 1.28 | 1.58 | 19.55
2022/05/27 | first half | 22:47–23:09 | A0a | 213.7 | -12.0 | 4 | 0.07 | 1.69 | 0.95 | 1.96 | 24.27
2022/05/27 | first half | 22:49–23:12 | A0b | 215.0 | -13.3 | 4 | 1.33 | 1.68 | 0.92 | 1.66 | 0.00
2022/05/27 | first half | 22:52–23:14 | A0c | 215.5 | -11.7 | 4 | 1.49 | 1.72 | 0.94 | 1.40 | 0.00
2022/05/27 | first half | 23:16–02:12 | A0d | 216.2 | -14.6 | 72 | 83.21 | 1.22 | 0.87 | 0.08 | 0.00
2022/05/27 | first half | 02:14–04:38 | A0e | 216.8 | -13.0 | 59 | 68.77 | 1.08 | 0.86 | 0.05 | 0.00
2022/05/28 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/05/29 | no time scheduled | no data collected | — | — | — | — | — | — | — | — | —
2022/05/30 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/05/31 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/06/01 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/06/02 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/06/03 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/06/04 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/06/05 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/06/06 | first half | no data collected | — | — | — | — | — | — | — | — | —
2022/08/21 | second half | 05:04–07:43 | B1a | 351.9 | -5.0 | 65 | 63.76 | 1.13 | 1.07 | 0.00 | 0.00
2022/08/21 | second half | 04:49–04:59 | B1d | 355.4 | -5.5 | 3 | 3.18 | 1.19 | 1.04 | 0.00 | 0.00
2022/08/21 | second half | 07:46–10:17 | B1h | 356.5 | -4.1 | 62 | 21.60 | 1.47 | 1.43 | 0.45 | 0.24
2022/08/21 | second half | 04:52–05:02 | B1m | 357.5 | -2.7 | 3 | 3.10 | 1.23 | 1.05 | 0.00 | 0.00
2022/08/22 | second half | 04:50–05:01,10:05–10:10 | B1a | 351.9 | -5.0 | 5 | 2.80 | 1.55 | 1.38 | 0.77 | 0.00
2022/08/22 | second half | 05:06–07:37 | B1d | 355.4 | -5.5 | 62 | 72.37 | 1.12 | 0.96 | 0.00 | 0.00
2022/08/22 | second half | 07:41–10:02 | B1g | 357.2 | -5.8 | 58 | 41.13 | 1.39 | 1.19 | 0.01 | 0.00
2022/08/22 | second half | 04:52–05:03,10:07–10:13 | B1m | 357.5 | -2.7 | 5 | 2.38 | 1.52 | 1.33 | 0.94 | 0.00
2022/08/23 | second half | 04:51–05:07,10:00–10:08 | B1a | 351.9 | -5.0 | 5 | 2.86 | 1.55 | 1.13 | 0.59 | 0.00
2022/08/23 | second half | 04:54–05:09,10:03–10:10 | B1d | 355.4 | -5.5 | 5 | 2.73 | 1.49 | 1.13 | 0.73 | 0.03
2022/08/23 | second half | 04:48–05:04,09:57–10:13 | B1g | 357.2 | -5.8 | 6 | 2.62 | 1.53 | 1.17 | 0.89 | 0.03
2022/08/23 | second half | 05:12–09:55 | B1m | 357.5 | -2.7 | 115 | 95.50 | 1.28 | 1.09 | 0.00 | 0.00
2022/08/24 | no time scheduled | no data collected | — | — | — | — | — | — | — | — | —
2022/08/25 | second half | 05:07–05:12 | B1b | 353.7 | -5.3 | 3 | 2.87 | 1.13 | 0.80 | 0.02 | 0.40
2022/08/25 | second half | 05:14–09:30 | B1e | 354.7 | -3.9 | 91 | 40.35 | 1.23 | 0.92 | 0.00 | 1.57
2022/08/25 | second half | 05:00–05:04 | B1g | 357.2 | -5.8 | 3 | 3.58 | 1.15 | 0.83 | 0.02 | 0.16
2022/08/25 | second half | 04:49–04:57 | B1k | 358.9 | -6.0 | 3 | 1.91 | 1.18 | 1.10 | 0.03 | 0.16
2022/08/26 | second half | 09:47–09:52 | B1b | 353.7 | -5.3 | 3 | 1.64 | 2.00 | 1.07 | 0.38 | 0.00
2022/08/26 | second half | 09:39–09:44 | B1e | 354.7 | -3.9 | 3 | 2.31 | 1.90 | 0.98 | 0.18 | 0.00
2022/08/26 | second half | 09:54–09:59 | B1i | 355.8 | -2.4 | 3 | 0.92 | 2.08 | 1.12 | 0.93 | 0.00
2022/08/26 | second half | 07:03–09:37 | B1k | 358.9 | -6.0 | 63 | 87.65 | 1.30 | 0.85 | 0.00 | 0.00
2022/08/26 | second half | 04:35–07:01 | B1l | 358.2 | -4.4 | 60 | 94.09 | 1.14 | 0.85 | 0.00 | 0.00
2022/08/27 | second half | 04:45–07:12 | B1b | 353.7 | -5.3 | 60 | 60.63 | 1.12 | 0.96 | 0.00 | 0.00
2022/08/27 | second half | 07:14–09:53 | B1i | 355.8 | -2.4 | 65 | 52.04 | 1.49 | 0.96 | 0.25 | 0.00
2022/08/27 | second half | 10:03–10:08 | B1k | 358.9 | -6.0 | 3 | 0.24 | 1.96 | 1.11 | 2.46 | 0.00
2022/08/27 | second half | 09:55–10:00 | B1l | 358.2 | -4.4 | 3 | 0.63 | 1.96 | 1.10 | 1.37 | 0.00
Table 4: Field coordinates for observations in the 2022 A Semester (J2000) for 1 June 2022 UT. Field coordinates for observation at other epochs were precessed based on the date of the year to account for parallax due to the Earth’s motion. There were no adjustments made for year-to-year orbital motion because the increasing size of the fan pattern was intended to account for year-to-year orbital motion. Field Name | RA | Dec
---|---|---
A0a | 14:14:59.71 | -11:57:03.44
A0b | 14:19:49.09 | -13:17:36.23
A0c | 14:22:11.42 | -11:39:26.43
A0d | 14:24:41.45 | -14:37:49.69
A0e | 14:27:02.70 | -12:59:30.33
A0f | 14:29:22.09 | -11:21:06.26
A0g | 14:29:36.95 | -15:57:42.78
A0h | 14:31:57.11 | -14:19:13.73
A0i | 14:34:15.23 | -12:40:39.61
A0j | 14:36:31.58 | -11:02:01.10
A0k | 14:34:35.73 | -17:17:14.45
A0l | 14:36:54.80 | -15:38:35.57
A0m | 14:39:11.66 | -13:59:51.30
A0n | 14:41:26.57 | -12:21:02.30
A0o | 14:43:39.79 | -10:42:09.21
A1a | 16:44:20.49 | -21:23:01.75
A1b | 16:50:10.37 | -22:30:03.56
A1c | 16:51:16.46 | -20:42:20.34
A1d | 16:56:05.70 | -23:36:19.26
A1e | 16:57:07.87 | -21:48:27.59
A1f | 16:58:08.51 | -20:00:34.51
A1g | 17:02:06.59 | -24:41:47.35
A1h | 17:03:04.71 | -22:53:47.66
A1i | 17:04:01.31 | -21:05:46.68
A1j | 17:04:56.55 | -19:17:44.56
A1k | 17:08:13.16 | -25:46:26.34
A1l | 17:09:07.07 | -23:58:19.08
A1m | 17:09:59.48 | -22:10:10.67
A1n | 17:10:50.57 | -20:22:01.24
A1o | 17:11:40.48 | -18:33:50.91
Table 5: Field coordinates for the 2022 B Semester (J2000) for 25 Aug 2022 UT. Field coordinates for observation at other epochs were precessed based on the date of the year to account for parallax due to the Earth’s motion. There were no adjustments made for year-to-year orbital motion because the increasing size of the fan pattern was intended to account for year-to-year orbital motion. Field Name | RA | Dec
---|---|---
B0a | 20:31:30.33 | -19:23:23.82
B0b | 20:38:47.25 | -19:53:11.52
B0c | 20:37:06.89 | -18:11:47.89
B0d | 20:46:06.72 | -20:21:55.07
B0e | 20:44:21.65 | -18:40:46.41
B0f | 20:42:38.65 | -16:59:34.17
B0g | 20:53:28.60 | -20:49:35.23
B0h | 20:51:38.85 | -19:08:42.02
B0i | 20:49:51.31 | -17:27:44.81
B0j | 20:48:05.73 | -15:46:44.07
B0k | 21:00:52.74 | -21:16:12.90
B0l | 20:58:58.34 | -19:35:35.57
B0m | 20:57:06.29 | -17:54:53.82
B0n | 20:55:16.36 | -16:14:08.14
B0o | 20:53:28.28 | -14:33:18.99
B1a | 23:27:45.53 | -05:00:35.01
B1b | 23:34:43.47 | -05:16:15.90
B1c | 23:31:55.69 | -03:35:48.69
B1d | 23:41:41.65 | -05:31:42.47
B1e | 23:38:52.92 | -03:51:20.81
B1f | 23:36:04.86 | -02:10:57.08
B1g | 23:48:39.98 | -05:46:56.96
B1h | 23:45:50.41 | -04:06:40.19
B1i | 23:43:01.55 | -02:26:21.18
B1j | 23:40:13.11 | -00:46:00.86
B1k | 23:55:38.35 | -06:02:01.66
B1l | 23:52:48.05 | -04:21:49.08
B1m | 23:49:58.50 | -02:41:34.11
B1n | 23:47:09.42 | -01:01:17.68
B1o | 23:44:20.52 | +00:38:59.32

###  3.8 Dithering

We have considered dithering to remove object losses due to the chip gaps,
however we found that this increases efficiency only marginally at best. Even
if an object is in a chip gap in a single epoch, it is very unlikely to be in
a chip gap in the second epoch. The chip gaps range from 153 pixels (short
edge) to 201 pixels (long edge) for the chips which are 2048 x 4096 pixels.
Thus, the gaps cover about 201x4096 + 153x2048 pixels, about 1 million pixels,
which is about 12% of the field. The odds of an object falling in a gap twice
in 2 visits is thus about 1.4%. With a $\sim 5000$ object expected sample, on
average we would expect 71 objects to fall into a gap twice in 2 years. We
consider this acceptable loss since dithering would significantly complicate
image processing and asteroid identification as we expect that pairwise image
subtraction may be optimal for asteroid identification. Thus no dithering was
performed during each field’s $\sim 4$ hour observational sequence.

##  4 Summary

We summarize our survey’s observational design as follows:

  * •

We have constructed a survey that covers between 10 and 45 square degrees of
sky to about magnitude $m_{R}\sim 26.2$ and will measure orbital information
for distant objects over a timebase of a few years. This will lead to the
discovery of a few thousand TNOs and a determination of about a thousand of
their orbits, surpassing all prior surveys in terms of object numbers.

  * •

We will use the CTIO DECam mounted at prime focus of the Blanco 4m telescope
in Cerro Tololo, Chile as it is one of the most powerful survey instruments
available to the astronomical community.

  * •

We have aimed to collect about 120 second exposures for about 4 hours for
every field on each half-night of observations. This should allow us to
achieve depths of about $m_{R}\sim 26.2$ for typical seeing conditions at
Cerro Tololo.

  * •

Our overall cadence of observations is to collect both quadrature and
opposition observations the first year, and thereafter only opposition
observations in subsequent years. This achieves an astrometric uncertainty of
about $\sim 30$ arc-seconds or better for most objects for a few years after
the survey.

  * •

We have chosen our primary filter to be the $V\\!R$ filter, which encompasses
the bandpasses of both the traditional Johnson Kron-Cousins $V$ and $R$
filters and has a high efficiency from about 470 nm to 760 nm. This choice was
intended to maximize discovery rates as the bandpass is a factor $\sim 2$
superior in flux sensitivity to all other filters in common usage with DECam.

  * •

We have planned for the use of a single additional filter, the $i$-band
filter, which would allow colors to be measured for TNOs discovered purely in
the $V\\!R$ filter. This appears to be the optimal filter configuration for
both discovery and color measurements given that typical TNO colors range from
neutral reflectance to some of the reddest objects in the solar system.

  * •

Our “fan”-shaped field pattern allows us to track objects’ sky-plane orbital
motion over the nominal 3 year course of the survey yielding significant
information about the TNO populations’ orbital characteristics. Although
initially designed for 3 years (a total of 10 fields), the “fan” shape was
extended in the last year of the survey to account for observations lost
during operational and national closures due to the COVID-19 pandemic.

  * •

The actual fields we observed each night were chosen to maximize observability
for the nights and half-nights we were allocated on the first year of the
project and to avoid bright field stars. This was adjusted for parallactic
motion of objects each year based on the date of the year of observation.

Our final survey will be the largest of its kind ever conducted, discovering
some $\sim 3,500$ TNOs and providing orbital information for at least one
thousand TNOs. In addition, it will exceed all future predictions for the LSST
in terms of depth and should allow a useful comparison to the LSST in terms of
object numbers and TNO properties.

This work is based in part on observations at Cerro Tololo Inter-American
Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2019A-0337; PI: D. Trilling),
which is managed by the Association of Universities for Research in Astronomy
(AURA) under a cooperative agreement with the National Science Foundation.
This work is supported by the National Aeronautics and Space Administration
under grant No. NNX17AF21G issued through the SSO Planetary Astronomy Program
and by the National Science Foundation under grants No. AST-2009096 and
AST-1409547. This research was supported in part through computational
resources and services provided by Advanced Research Computing at the
University of Michigan, Ann Arbor. This work used the Extreme Science and
Engineering Discovery Environment (XSEDE; Towns et al., 2014), which is
supported by National Science Foundation grant number ACI-1548562. This work
used the XSEDE Bridges GPU and Bridges-2 GPU-AI at the Pittsburgh
Supercomputing Center through allocation TG-AST200009. H. Smotherman
acknowledges support by NASA under grant No. 80NSSC21K1528 (FINESST). H.
Smotherman, M. Jurić and P. Bernardinelli acknowledge the support from the
University of Washington College of Arts and Sciences, Department of
Astronomy, and the DiRAC Institute. The DiRAC Institute is supported through
generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences
and the Washington Research Foundation. M. Jurić wishes to acknowledge the
support of the Washington Research Foundation Data Science Term Chair fund,
and the University of Washington Provost’s Initiative in Data-Intensive
Discovery. This project used data obtained with the Dark Energy Camera
(DECam), which was constructed by the Dark Energy Survey (DES) collaboration.
Funding for the DES Projects has been provided by the US Department of Energy,
the US National Science Foundation, the Ministry of Science and Education of
Spain, the Science and Technology Facilities Council of the United Kingdom,
the Higher Education Funding Council for England, the National Center for
Supercomputing Applications at the University of Illinois at Urbana-Champaign,
the Kavli Institute for Cosmological Physics at the University of Chicago,
Center for Cosmology and Astro-Particle Physics at the Ohio State University,
the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M
University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho
de Amparo á Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de
Desenvolvimento Científico e Tecnológico and the Ministério da Ciência,
Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the
Collaborating Institutions in the Dark Energy Survey. The Collaborating
Institutions are Argonne National Laboratory, the University of California at
Santa Cruz, the University of Cambridge, Centro de Investigaciones
Enérgeticas, Medioambientales y Tecnológicas–Madrid, the University of
Chicago, University College London, the DES-Brazil Consortium, the University
of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi
National Accelerator Laboratory, the University of Illinois at Urbana-
Champaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the Institut de
Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-
Maximilians Universität München and the associated Excellence Cluster
Universe, the University of Michigan, NSF’s NOIRLab, the University of
Nottingham, the Ohio State University, the OzDES Membership Consortium, the
University of Pennsylvania, the University of Portsmouth, SLAC National
Accelerator Laboratory, Stanford University, the University of Sussex, and
Texas A&M University.

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