neo_capabilities.tex

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\begin{document}
\title{The Large Synoptic Survey Telescope as a Near-Earth Object Discovery Machine}

\author{R. Lynne Jones\altaffilmark{1},
Colin T. Slater\altaffilmark{1},
Joachim Moeyens\altaffilmark{1},
Lori Allen\altaffilmark{2},
Tim Axelrod\altaffilmark{3},
Kem Cook\altaffilmark{4},
\v{Z}eljko Ivezi\'{c}\altaffilmark{1},
Mario Juri\'{c}\altaffilmark{1},
Jonathan Myers\altaffilmark{5},
Catherine E. Petry\altaffilmark{6}
}
\altaffiltext{1}{Department of Astronomy, University of Washington, Box 351580,
  Seattle, WA 98195, USA}
\altaffiltext{2}{National Optical Astronomy Observatory, 950 North Cherry
Avenue, Tucson, AZ 85719, USA}
\altaffiltext{3}{University of Arizona, Steward Observatory, 933 North Cherry
Avenue, Tucson, AZ 85721, USA}
\altaffiltext{4}{Cook Astronomical Consulting, 220 Duxbury CT, San Ramon, CA 94583, USA}
\altaffiltext{5}{Work performed at the Large Synoptic Survey Telescope, 950 North Cherry Avenue, Tucson, AZ 85719, USA; 
Jonathan is now affiliated with Johns Hopkins University, Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD 20723, USA}
\altaffiltext{6}{Large Synoptic Survey Telescope, 950 North Cherry Avenue, Tucson, AZ 85719, USA}

\begin{abstract}
Using the most recent prototypes, design, and as-built system information, we test and quantify the capability of the Large Synoptic Survey Telescope (LSST) to discover Potentially Hazardous Asteroids (PHAs) and Near-Earth Objects (NEOs).
We empirically estimate an expected upper limit to the false detection rate in LSST image differencing, using measurements on DECam data and prototype LSST software and find it to be about $450$~deg$^{-2}$. We show that this rate is already tractable with current prototype of the LSST Moving Object Processing System (MOPS) by processing a 30-day simulation consistent with measured false detection rates.
We proceed to evaluate the performance of the LSST baseline survey strategy for  PHAs and NEOs using a high-fidelity simulated survey pointing history. 
We find that LSST alone, using its baseline survey strategy, will detect 66\% of the PHA and 61\% of the NEO population objects brighter than $H=22$, with the uncertainty in the estimate of $\pm5$ percentage points. By generating and examining variations on the baseline survey strategy, we show it is possible to further improve the discovery yields.
In particular, we find that extending the LSST survey by two additional years and doubling the MOPS search window increases the completeness for PHAs to 86\% (including those discovered by contemporaneous surveys) without jeopardizing other LSST science goals (77\% for NEOs). This equates to reducing the undiscovered population of PHAs by additional 26\% (15\% for NEOs), relative to the baseline survey.
\end{abstract}

\keywords{Near-Earth objects --- Image processing -- Asteroids}

\include{intro}

\include{strategy}

\include{image_differencing}

\include{mops}

\include{opsim}

\include{discussion}

\acknowledgements
This material is based upon work supported in part by the National Science
Foundation through Cooperative Agreement 1258333 managed by the Association of
Universities for Research in Astronomy (AURA), and the Department of Energy
under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator
Laboratory. Additional LSST funding comes from private donations, grants to
universities, and in-kind support from LSSTC Institutional Members. MJ and CTS wish 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.

\appendix
\include{appendixMOPS}

\bibliography{neo_capabilities}
\end{document}