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\begin{document}

\title{The Pan-STARRS Moving Object Processing System}

\author{
%people who have been paid to work on MOPS or devoted a good fraction of the
%past six years to MOPS roughly in order of *time* contribution
Robert Jedicke\altaffilmark{1} (jedicke@ifa.hawaii.edu),
Larry Denneau\altaffilmark{1},
Tommy Grav\altaffilmark{2},
Francesco Pierfederic\altaffilmark{4},
Daniel Chang\altaffilmark{1},
Andrea Milani\altaffilmark{5},
Mikael Granvik\altaffilmark{1},
Jeremy Kubica\altaffilmark{3},
Jonathan Myers\altaffilmark{6},
???,
%
%people who have made substantial contributions to MOPS development in
%alphabetical order
Tim Spahr\altaffilmark{7},
Gareth Williams\altaffilmark{7},
Steve Chesley\altaffilmark{8},
Richard Wainscoat\altaffilmark{1},
Jim Heasley\altaffilmark{1},
David Tholen\altaffilmark{1},
JJ Kavelaars\altaffilmark{9},
Charles Ying-Tong?\altaffilmark{9},
???
}

\slugcomment{N Pages, N Figures, N Table}

\altaffiltext{1}{Institute for Astronomy, U. of Hawaii, 2680 Woodlawn Dr, Honolulu, HI, 96822}
\altaffiltext{2}{Johns Hopkins U., Baltimore, MD}
\altaffiltext{3}{Google}
\altaffiltext{4}{???}
\altaffiltext{5}{U. of Pisa, Pisa, Italy}
\altaffiltext{6}{U. of Arizona, Tucson, AZ}
\altaffiltext{7}{Minor Planet Center, Boston, MA}
\altaffiltext{8}{Jet Propulsion Laboratory, Pasadena, CA}
\altaffiltext{9}{National Central U., Taipei, Taiwan}

\shorttitle{Pan-STARRS MOPS}
\shortauthors{Jedicke \etal}

\begin{abstract}

ABSTRACT

\end{abstract}
\keywords{Near-Earth Objects; Asteroids, Dynamics}
\maketitle



\section{Introduction}

Like most human activities, the history of asteroid and comet studies
is characterized by an exponential increase in their rate of discovery
since the identification of Ceres by Pizarro more than 200 years ago.
This work describes the next step in the evolution of asteroid surveys
--- a fully integrated end-to-end and open source moving object
processing system (MOPS) for one of the next generation wide field
astronomical surveys (Pan-STARRS, \cite{Hodapp2004}) that will employ
gigantic giga-pixel scale cameras to detect asteroids and comets
faster than ever before.

The first asteroids were discovered serendipitously and laboriously by
eye until the dedicated photographic surveys of the 1950s and 1960s
like the Yerkes-Macdonald \citep{Kuiper1958} and Palomar Leiden
Surveys \citep{vanHouten1970}.  The photographic surveys still
required a major effort due to the need for human `blinking' of the
images to identify the moving objects.  The realization in the early
1980s by \citet{Alvarez1980} that the extinction of the dinosaurs
$\sim$65 million years ago was precipitated by the impact of a large
asteroid or comet with the Earth provided the stimulus for wide field
photographic asteroid surveys for objects that might strike the Earth
\citep[\eg][]{Helin1979}.  While those survey identified many
asteroids and comets that may eventually strike the Earth
\citet{Shoemaker1995} discovered Comet Shoemaker-Levy 9, a train of
cometary framgents that slammed into the planet Jupiter in 1994
\citep[\eg][]{Weaver1995}.  

The photographic searches leveraged decades of experience in wide
field astronomical surveying but Spacewatch \citep{Gehrels1991}
spearheaded the first use of CCDs in asteroid surveys.  In the
beginning, the small CCDs of the time limited the success of this
search program but the asteroid discovery rate improved dramatically
when they obtained a high quantum efficiency 2K$\times$2K CCD with an
$\sim$30~arcmin field of view and adopted a `drift scanning' survey
technique that eliminated the need for long readout times.  At the
same time, \citet{Rabinowitz1991} developed the first automated moving
object detection program to identify asteroids and comets in
Spacewatch's drift scan images which launched the contemporary
generation of wide field surveys such as NEAT \citep{Helin1997},
LONEOS \citep{Bowell1995}, LINEAR \citep{Stokes2000} and the
contemporary leader in asteroid discovery, CSS \citep{Larson2007}.

These modern wide field asteroid surveys were spurred and funded by
the NASA Spaceguard Program \citep{Harris2008} with the goal of
identifying 90\% of Near Earth Objects\footnote{Near Earth Objects are
  asteroids or comets with perihelion distances of $\le$1.3~AU.  Most
  of these objects are on unstable orbits with dynamical lifetimes of
  $<$10~Myrs \citep[\eg][]{Gladman2000} and will quickly be ejected
  from the Solar System or impact the Sun or Jupiter but a small
  fraction will eventually strike the Earth.} (NEO) larger than 1~km
diameter before the end of 2008.  All the surveys broadly employ
similar techniques for asteroid and comet identification.  Their wide
field cameras have large pixel scales (typically $\ga$1 arcsec) and
image a field 3-5 times within about an hour on a night.  Their moving
object detection software identifies `sources' in each image and then
spatially correlates the detections between the images to identify and
remove stationary objects.  All the `transient detections' are then
searched for consistency with being the same object moving linearly
across the sky at a constant rate of motion between the exposures.
The constant motion requirement allows the software to achieve per
detection S/N levels of 1.5-3$\sigma$.  The set of 3-5 linked
detections represent a possible real moving object and are reported to
the Minor Planet Center (MPC) though some groups review the detections
by eye before submission to reduce the false detection rate.  Overall,
these groups were wildly successful identifying $\sim$79\% of the
$\ge$1~km NEOs before 10 June 2008 \citep{Harris2008}.

The CCD surveys described above were designed to discover large NEOs,
not for the purpose of characterizing the size-frequency distribution
(SFD) of various solar system populations.  However, in the process of
searching for the NEOs they discovered several interesting objects
(\eg Comet Shoemaker-Levy 9, \citet{Shoemaker1995}; VFMOs,
\citet{Rabinowitz1993}; Pholus; 2008 TC$_3$, \citet{Jenniskens2009})
while {\it post facto} determinations of their surveying efficiency
allowed new or redeterminations of the SFD of many small body
populations (\eg IEOs, \citet{Zavodny2008}; NEOs,
\citet{Rabinowitz1993b}; Main Belt, \citet{Jedicke1998}; Centaurs,
\citet{Jedicke1997}; TNOs, \citet{Larsen2001}; Distant objects,
\citet{Larsen2007}).  Despite their success there is room for
improvement in their surveying techniques, moving object detection
software, reporting and followup including:
\begin{itemize}
\item Survey Technique and/or depth 

The existing surveys were not capable of meeting the Spaceguard Goal
\citep{Jedicke2003} due to their sky plane coverage and/or limiting
magnitude.  In particular, asteroids or comets on orbits that {\it
  will} impact the Earth are under represented towards opposition but
densely populate the sky at relatively small ecliptic latitudes and
solar elongations $\la 90\deg$ \citep[\eg][]{Chesley2004,Veres2009}.
Asteroids in this region of the sky are notoriously difficult to
observe because of the limited time that they are above the horizon,
the high air mass when they are available, their large phase angle
(and reduced illumination of the visible surface) and faintness.
Furthermore, the 3-5 repeat visits/night to the same field is wasteful
of survey time.  More sky could be covered or the same sky could be
imaged to greater depth if there were fewer visits to the same field
each night.  What is required is large aperture high-speed readout
camera systems capable of surveying at altitudes down to $\sim
20\deg$.

\item Moving object detection software

The software packages do not monitor their own detection efficiency
and accuracy.  Instead, analysis of the survey data must be performed
{\it post facto}.  It would be convenient if the packages incorporated
an intrinsic near real-time measure of both their efficiency and
accuracy for the purpose of system monitoring and subsequent data
reduction.  Furthermore, it would be useful to determine orbits rather
than merely reporting sets of detections to the MPC.  For instance,
some NEOs appear to move at the same rate as main belt objects in the
same field.  Obtaining their orbits would allow identification of
the NEOs that are `hidden in plain sight'.

\item Followup

To maximize survey coverage and their discovery rate most of the
surveys visit a field on only one night per lunation to obtain a set
of 3-5 linked detections.  The detections provide the sky plane
location and velocity vector for the object rather than an orbit.  To
identify NEOs the surveys flag candidate NEOs as those objects with
unusual rates of motion \citep[\eg][]{Rabinowitz1991,Jedicke1996} and
then reaquired the objects in special followup efforts that reduced
the time available for discovery of new objects.  Alternatively, they
reported the detections to the MPC which then performed a slightly
more sophisticated probabilistic analysis to determine the likelihood
that the object may be a NEO.  If the object met their likelihood
cutoff it would be posted on their NEO confirmation webpage and
followed up quickly by professional and amateur astronomers around the
world.  Given that telescope time is a valuable commodity and that the
next generation of professional asteroid survey telescopes will
discover more and even fainter objects it is clear that the survey
telescopes must provide their own target followup and it should be
incorporated directly into their survey pattern.

\item Reporting

The system of reporting sets of detections to the MPC has been very
effective and the followup response of the international community of
amateur and professional observers has been fantastic as amply
demonstrated by the case of 2008 TC$_3$ \citep{Jenniskens2009}.
Still, the process of submission by email to the MPC, posting on the
NEO confirmation page, downloading and acquisition by observers is
unwieldy.  Since there is no coordination between observers there is a
great deal of wasted time with multiple and unnecessary followup.  A
modernized, automated reporting system tuned specifically to the
capabilities and intersets of each followup observatory would be
useful to coordinate the followup effort.

\end{itemize}

Some of the problems itemized above have been addressed by other
surveys, in particular the well characterized main belt program of
\citet{Gladman2009} using software developed by \citet{Petit2004}.
They generate synthetic detections in the same images used to identify
their TNO candidates to measure the detection efficiency and accuracy.

The future of wide field surveying moving object processing systems is
embodied in the software jointly developed by the Panoramic Survey
Telescope and Rapid Response System (Pan-STARRS) project
\citep{Hodapp2004} and the LSSTC\footnote{Large Synoptic Survey
  Telescope Corporation} \citep{Tyson2001} as described in the
remainder of this paper.  Most of the issues described above are
resolved and the system is shown to have high efficiency and accuracy.
The next section is divided into subsections that loosely reflect the
data processing flow.  In each subsection we describe the performance
of that processing step and in the last sections we characterize the
overall system performance and results using real data from Pan-STARRS
and other operational surveys.




\section{Pan-STARRS}

The Pan-STARRS \citep{Hodapp2004} prototype telescope (PS1) on
Haleakala, Hawaii, began surveying for asteroids in the spring of
2009.  This 1.8~m diameter telescope has an $\sim$7~deg$^2$ field of
view and a $\sim$1.4 Gpixel orthogonal transfer CCD (OTCCD) camera
\citep{Tonry1997} with 0.XXX'' pixels.  PS1 is the prototype for a 4
telescope system (PS4) that the project hopes to build on the summit
of Mauna Kea, Hawaii, which should begin surveying late in 2XXX.
PS1's large aperture, field of view and $\la$7~s readout time will
allow imaging of the entire night sky visible from Hawaii to $r \sim
22.7$ in only 4 nights.

To reduce the detection rate of false transient objects PS1 will image
fields twice each night separated by a Transient Time Interval (TTI).
The Pan-STARRS Image Processing Pipeline (IPP, \citet{Magnier2006})
compares the two images to each other, and to a static sky map created
from earlier PS1 images of the same field, to discriminate between
false transients (\eg cosmic rays), stationary transients (\eg,
variable stars, gamma ray bursts, supernovae, and very distant
asteroids, planets or comets) and moving transients (\eg asteroids and
comets).  Thus, for each image the IPP produces a transient detection
source list composed of all transients that are not otherwise
identifiable as false transients like cosmic rays or other image
artifacts.  The rate of transient detections at 5(3)-$\sigma$ is $\sim
1,500$ ($1.5 \times 10^6$)/deg$^2$.

The PS1 telescope will be operated by the PS1 Science Consortium
(PS1SC) for 3.5 years of surveying.  The PS1SC survey plan
incorporates about a half dozen sub-surveys but only two are
specifically designed for moving object discovery (all the surveys are
suitable for moving object {\it detection} because they all employ at
least one TTI pair of images) --- the 3$\pi$ (opposition) survey and the
`sweet spot' survey.  The opposition survey will image the sky north
of $\sim -XXX\deg$ declination within $\pm30\deg$ (two hours) of
opposition in R.A. six times in each lunation.  The six images are
obtained on three nights, each night creating a TTI pair in $g$, $r$
or $i$ respectively.  The sweet spot survey images the sky within
about $\pm10\deg$ of the ecliptic and as close to the Sun as possible
subject to altitude, sky brightness constraints, etc.  The total sky
coverage in both these survey regions is about XXX deg$^2$/lunation.

The Pan-STARRS Moving Object Processing System (MOPS) described below
was designed for use with the PS4 system where the combined images
from four telescopes will provide complete coverage over the entire
field but its use with the PS1 system suffers from a $\sim$XXX\%
camera fill factor ($f$).  The loss of image plane coverage results
from gaps between the OTCCDs, dead cells and gaps on the CCDs
themselves, problematic pixels (\eg anomalous dark current or
non-linearity), the allocation of sub-sections on the OTCCDs (known as
cells) to high speed acquisition of bright guide stars, and a very
small percentage is excised by the U.S. Air Force. Since MOPS requires
six detections/lunation for new object discovery the maximum
achievable system efficiency ($\epsilon$) for moving objects must be
somewhere in the range of $f^1 \le \epsilon_{max} \le f^6$ depending
on the object's rate of motion, the distribution of `dead' image plane
pixels/area and the telescope pointing repeatability.  This implies
that the maximum MOPS detection efficiency for moving objects in the
field of view must lie in the range XXX-XXX\% even for objects that
would be imaged at high S/N.

In part to correct for the loss of field due to the camera fill factor
the Pan-STARRS IPP provides a Live Pixel Server (LPS) that determines
whether a pixel at or near a specific (RA,dec) in an image falls on
`live' pixels on the camera.  As described below, this service is
necessary for the MOPS in two ways: 1) to generate synthetic moving
object data and 2) when attempting to recover known asteroids that are
in the field of view.  In a related fashion, the IPP characterizes its
detection efficiency for point sources as a function of S/N that is
also used by the MOPS for the same purposes.

Thus, PS1 provides to the MOPS, a PS1 `Science Client', all the
required information for identifying moving objects: 1) transient
detections (RA, dec, flux, etc.) 2) the Live Pixel Server and 3) point
source detection efficiency.



\section(The Pan-STARRS Moving Object Processing System (MOPS))

\subsection{Overview}

\begin{figure}[btp]
\centering
%\includegraphics[scale=1.0]{./}
\caption{CAPTION.}
\label{fig.MOPSFlowchart}
XXXLD High level flowchart for the PS1 Moving Object Processing System.
\end{figure}

Figure \ref{fig.MOPSFlowchart} provides a high level view of data flow
to, through and from MOPS.  Each of the steps in MOPS data processing
is described in the following subsections.  This MOPS is the first
integrated system that processes data from the transient detection
source list through orbit determination, precovery and attribution,
etc.

Note that there is a clear separation between the IPP and MOPS.  The
MOPS is not responsible for image processing and has no contact
whatsoever with the image data unlike the object detection algorithm of
\citet[\eg][]{Petit2004}.  This was an intentional design
decision based on the scale of the Pan-STARRS project.  The IPP is
responsible for monitoring its detection efficiency and accuracy as is
the MOPS.  In this way each Pan-STARRS subsystem could be developed
almost independently following its own set of standard engineering
design principles from Softare Requirement Specifications (SRS),
through Software Requirement Review (SRR), Preliminary Design Review
(PDR), Critical Design Review (CDR) and finally Operational Readiness
Review (ORR).  Interactions between the subsystems were specified and
maintained through Interface Requirement Descriptions (IRD) and
Interface Design Specifications (IDS).  Each major MOPS design
milestone was reviewed by a panel consisting of Pan-STARRS senior
management and two independent external reviewers.  The full set of
engineering documents is freely available to interested parties.

One major MOPS innovation is the incorporation of a synthetic solar
system model (described in \S\ref{ss.SolarSystemModel} and
\citet{Grav2009}) that allowed us to monitor progress in MOPS
development and measures its performance during real operations.
During $\sim$4 years of MOPS development we continuously benchmarked
its performance using 3 metrics for each MOPS subcomponent (where
applicable):
\begin{itemize}

\item Efficiency

The fraction of available `objects' (specific to a processing step)
that were correctly identified.

\item Accuracy

The fraction of identified `objects' (specific to a processing step)
that are correct.

\item Goodness

The fraction of correctly identified `objects' (in this case derived
orbits) that provide an ephemeris error (the distance between the
predicted and actual positions of the object after a specific time in
the future) less than a predefined value.

\end{itemize}

During MOPS development we were constantly working towards ensuring
that it would operate at PS1 transient detection rates.  At a limiting
magnitude of $r \sim 22.7$ corresponding to $S/N \sim 5$ we expected
$\sim$1500 asteroids or comets per deg$^2$ on the ecliptic - about
equal to the false transient detection rate at the same $S/N$.  Since
the detection rate of real solar system objects decreases with
ecliptic latitude the {\it best} ratio of false:real detections was
1:1 with the ratio increasing dramatically off the ecliptic.  At about
1,000 images/night and $\sim$7~deg$^2$/field MOPS must be capable of
processing about $3 \times 10^6$~detections/night.  

We also wanted to maintain a low significance detections (LSD) data
base for the purpose of precovery and attribution of known objects and
discovery of slower moving objects which are less challenging to
identify than faster moving objects in the inner solar.  The design
goal for the LSD was to maintain a smaller set of information for each
of the transient detections with $S/N \ga 3$ corresponding to a sky
plane density of $\sim1.5 \times 10^6$/deg$^2$ --- about
$10^7$~detections/field or $10^10$~detections/night!




\subsection{Database}

XXXLD NEED FEW PARAGRAPH? DESCRIPTION FROM LD
mySQL
LSD



\subsection{Solar System Model}
\label{ss.SolarSystemModel}

XXXTG SHOULD TOMMY PROVIDE FEW PARAGRAPHS OF TEXT?  IS THERE A GOOD FIGURE?
rationale for SSM
GRID model
reference to Grav paper



\subsection{Simulations}

XXXMG I THINK THE DEFINITIVE SIMULATION WE USE HERE SHOULD BE MG'S TWO
YEAR FULL DENSITY SIMULATION.  MG PROVIDE SOME TEXT?

XXXMG MAY NEED TO USE OTHER SIMULATIONS AT SOME TIMES BELOW.  BUT
ALWAYS FULL DENSITY SIMS WITH REALISTIC ASTROMETRIC ERROR *AND*
NOMINAL ERROR.

overview of how simulations were performed the studies below
nominal vs. realistic astrometric uncertainty



\subsection{Tracklets}

XXX RJ CAN WRITE THIS SECTION BUT NEEDS RESULTS OF SIMULATIONS
XXX HOW ABOUT A TABLE/FIGURE WITH COLUMNS THAT ARE 
* OBJECT TYPE (IMPACTOR, NEO, MBO, TNO, PLANETX, SPC, LPC, GRID )
* FINDTRACKLET EFFICIENCY
* FINDTRACKLET ACCURACY
* LINKTRACKLET EFFICIENCY
* LINKTRACKLET ACCURACY

XXXRJ WILL CONTACT JON MYERS RE collapseTracklets MAYBE WE SHOULD HAVE
TWO 2D FIGS - ONE SHOWING EFF AND THE OTHER ACCURACY AS A FUNCTION OF
BOTH NUMBER OF DETECTIONS AND TIME BETWEEN FIRST AND LAST?


\subsection{Tracks}

XXX THE DATA FOR THIS SECTION IS INCLUDED IN THE LAST SECTION
linking tracklets into tracks
eff \& acc vs. object type, false detections
what about fast moving trailed objects?



\subsection{Initial Orbit Determination}

XXXAM DOES ANDREA WANT TO PROVIDE A SHORT FEW PARAGRAPHS HERE?
OTHERWISE I WOULD PROBABLY JUST REFER THE READER TO HIS PAPERS AND
PROVIDE SOME OF MY OWN BACKGROUND.

XXXAM IS IT EVEN POSSIBLE TO SEPARATE OUT THE IOD FROM THE OD WHEN
USING ORBIT-SERVER?  MAYBE WE SHOULD USE MPCIOD FOR THIS SECTION?

XXXMG PROVIDE TABLE/FIGURE SHOWING IOD EFFICIENCY AND ACCURACY FOR ALL OBJECT
TYPES GIVEN ABOVE.  SHOULD BE SPLIT INTO SEPARATE OPPN AND SWEET SPOTS TOO.

overview of techniques
complications in oppn and ss regions
eff \& acc vs. object type, false detections
why did we not use statistical ranging? - too much time, difficult to store
in DB, unnecessary?



\subsection{Orbit Determination}

XXXMG PROVIDE TABLE/FIGURE SHOWING IOD EFFICIENCY AND ACCURACY FOR ALL OBJECT
TYPES GIVEN ABOVE.  SHOULD BE SPLIT INTO SEPARATE OPPN AND SWEET SPOTS TOO.

XXX THIS IS CURRENTLY COMPLICATED BY THE PROCESS WHERE WE USE
ORBIT-SERVER AND THEN THE JPL CODE BY ORBIT-SERVER IS ALREADY
PROVIDING A DIFFERENTIALLY CORRECTED ORBIT.  MAYBE WE SHOULD USE THE
MPCIOD RESULTS AGAIN HERE?

XXX I DON'T THINK IT MAKES MUCH SENSE TO DESCRIBE THE TECHNIQUE(S)
HERE.  JUST DESCRIBE THE RESULTS THAT WE FIND IN THE TABLE.

overview of techniques
complications in oppn and ss regions
eff \& acc vs. object type, false detections



\subsection{Precovery \& Attribution (PandA)}

XXXLD I THINK LARRY IS THE BEST PERSON TO DESCRIBE THIS TECHNIQUE.
SOMETHING SHORT AND SWEET.

XXXMG PROVIDE TABLES/FIGURES SHOWING PANDA EFF AND ACC FOR ALL OBJECT TYPES

overview of techniques
complications in oppn and ss regions
eff \& acc vs. object type, false detections



\subsection{Orbit Identification}

XXXLD WE ARE STILL USING KUBICA'S SIMPLISTIC CODE RIGHT?

XXXMG PROVIDE TABLE/FIGURE SHOW ORBIT IDENTIFICATION EFF AND ACC
VS. POPULATION TYPE

overview of techniques
complications in oppn and ss regions
eff \& acc vs. object type, false detections




\subsection{link2\_server}

XXXAM ANDREA TO PROVIDE SIMPLE EXPLANATION AND TABLE OR FIGURE
(PREFERABLY) SHOWING EFFICIENCY AND ACCURACY?



\subsection{known\_server}

XXXAM ANDREA TO PROVIDE SIMPLE EXPLANATION AND TABLE OR FIGURE
(PREFERABLY) SHOWING EFFICIENCY AND ACCURACY?




\subsection{GRID Simulations}

XXXMG HAVE WE EVER RUN A GRID ONLY SIMULATION?  COULD YOU DO SO SOON TO GENERATE OBJECT RECOVERY EFFICIENCY/ACCURACY IN E VS A FIGS?

Performance on the GRID 
detection efficeincy to Barnards Star?




\subsection{Data Export}

XXXDC DANIEL PROVIDE A FEW PARAGRAPHS DESCRIPTION OF HOW THIS WORKS

automated reporting to MPC
current reporting philosophy




\subsection{Data Exchange Standard}

XXXAM ANDREA PROVIDE A FEW PARAGRAPHS DESCRIPTION OF THE UTILITY OF THE DES

rationale




\subsection{Alert System}

XXXFP I WILL ASK FRANCESCO IF HE WOULD LIKE TO WRITE UP A FEW
PARAGRAPHS.  IF NOT, I WILL PROBABLY ASK LD



\subsection{System Performance}

XXXMG PLEASE PROVIDE THE MORE INTERESTING TIMING FIGURES 

XXX ALL THE SUBSECTIONS ABOVE DESCRIBE EFF \& ACC OF MOPS
SUBCOMPONENTS.  WOULD BE GOOD TO PROVIDE A MEASURE OF EFF AND ACC
AFTER THE 2 YEAR SIMULATION.  I.E. AFTER ALL IS SAID AND DONE HOW GOOD
WERE WE AT FINDING ORBITS OF OBJECTS THAT APPEARED IN THE IMAGES?

overall eff \& acc results
processing times
data rates




\section{MOPS \& Real Data}

XXXRJ ROBERT CAN PROBABLY WRITE MOST OF THIS XXXLD ONCE WE GET PS1
DATA LARRY COULD PRODUCE SOME INTERESTING FIGURES SHOWING ACTUAL AND
SYNTHETIC PERFORMANCE

Spacewatch, TALCS, CFHT-LS, PS1





\section{Availability \& Ongoing Development}

XXXRJ ROBERT WILL WRITE THIS SECTION WITH HELP FROM LD

FTE development time?

freely available with caveats




\section{Summary}

XXXRJ ROBERT WILL WRITE THIS SECTION




\acknowledgements

Jeff Kantor
Tim Axelrod
Wen-Ping Chen
Shinsuke Abe
Rex Chang
Bob McMillan
Don Yeomans \& Ted Bowell for acting as external reviewers on all
MOPS engineering panels.
???

Funding agency for PS and MOPS.  The PS1SC.

LSSTC.






\bibliography{mops.references}
\bibliographystyle{icarus} 


\end{document}
