Cited by 32Astro and Planetary ScienceSpace Satellite Systems and ControlAstronomy and Astrophysical Research

MODELING THE PERFORMANCE OF THE LSST IN SURVEYING THE NEAR-EARTH OBJECT POPULATION

Open access
Grav, T., Mainzer, A. K., Spahr, T.·2016·The Astronomical Journal

Abstract

We have performed a detailed survey simulation of the LSST performance with regards to near-Earth objects (NEOs) using the project’s current baseline cadence. The survey shows that if the project is able to reliably generate linked sets of positions and times (a so-called “tracklet”) using two detections of a given object per night and can link these tracklets into a track with a minimum of three tracklets covering more than a ∼12 day length-of-arc, then they would be able to discover 62% of the potentially hazardous asteroids (PHAs) larger than 140 m in its projected 10 year survey lifetime. This completeness would be reduced to 58% if the project is unable to implement a pipeline using the two detection cadence and has to adopt the four detection cadence more commonly used by existing NEO surveys. When including the estimated performance from the current operating surveys, assuming these would continue running until the start of LSST and perhaps beyond, the completeness fraction for PHAs larger than 140 m would be 73% for the baseline cadence and 71% for the four detection cadence. This result is a lower completeness than the estimate of Ivezić et al.; however, the result is quite close to that of Jones et al., who show ∼70% completeness using the identical survey cadence as used here. We show that the traditional method of using absolute magnitude H < 22 mag as a proxy for the population with diameters larger than 140 m results in completeness values that are too high by ∼5%. Our simulation makes use of the most recent models of the physical and orbital properties of the NEO and PHA populations, as well as simulated cadences and telescope performance estimates provided by the LSST project. The consistency of the results presented here when compared to those of Jones et al. demonstrates the robustness of these survey modeling approaches. We also show that while neither LSST nor a space-based IR platform like NEOCam individually can complete the survey for 140 m diameter NEOs, the combination of these systems can achieve that goal after a decade of observation.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3847/0004-6256/151/6/172

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • After accounting for differences in input assumptions and models, each of the independent works (Jones et al. 2018; Vereš & Chesley 2017a, 2017b; Grav et al. 2016) calculates a completeness value that is consistent within a few percent.

    “After accounting for differences in their input assumptions and models, each of these independent works calculates a completeness value that is consistent within a few percent.”

    From LSST: From Science Drivers to Reference Design and Anticipated Data Products (2019) · cites this paper as Grav et al. 2016

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 33 · Cited by 32

Research works printed but not linked: 6

  1. Binzel, R. P., Lupishko, D., di Martino, M., Whiteley, R. J., & Hahn, G. J. 2002, in Asteroids III, ed. W. F. Bottke Jr., A. Cellino, P. Paolicchi, & R. P. Binzel (Tucson, AZ: Univ. Arizona Press), 255
  2. Bowell, E., Hapke, B., Domingue, D., et al. 1989, Asteroids II, ed. R. P. Binzel, T. Gehrels, & M. S. Matthews (Tucson, AZ: Univ. Arizona Press), 524
  3. G. B. Valsecchi, D. Vokrouhlický, & A. Milani (Cambridge: Cambridge Univ. Press), 353
  4. S. R. Chesley et al. (Cambridge: Cambridge Univ. Press), 282
  5. Jones, R. L., Yoachim, P., Chandrasekharan, S., et al. 2014, Proc. SPIE, 9149, 0
  6. Larson, S. 2007, in IAU Symp. 236, Near Earth Objects, our Celestial Neighbors: Opportunity and Risk, ed. G. B. Valsecchi, D. Vokrouhlický, & A. Milani (Cambridge: Cambridge Univ. Press), 323

Authors
Grav, T., Mainzer, A. K., & Spahr, T.
Affiliations
  • Planetary Science Institute (US)
  • California Institute of Technology (US)
Corresponding author
Grav, T.
Venue
The Astronomical Journal
Published
2016-06-01
Volume
151
Issue
6
Pages
172
ISSN
  • 0004-6256 (ISSN-L)
  • 1538-3881
OpenAlex
W2342371723
Cited by
32
References
33
Citations by year
2026: 2 · 2025: 6 · 2024: 3 · 2023: 4 · 2019: 4 · 2018: 3 · 2017: 4 · 2016: 6
FWCI
2.71
Type
journal article
Language
en
Open access
Locations
  • The Astronomical Journal (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astro and Planetary Science
  • Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
  • Physical Sciences › Physics and Astronomy › Instrumentation › Astronomy and Astrophysical Research
Keywords
LSST, near-Earth objects, potentially hazardous asteroids, survey simulation, absolute magnitude

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review