NASA REPORT • JULY 21, 2016 Name of Meeting • Location • Date - Change in Slide Master 1

LSST Discovery Potential for Solar System Science and Planetary Defense

Zeljko Ivezic Mario Juric Lynne Jones Colin Slater Joachim Moeyens (University of Washington) and the LSST Project.

SBAG June 14, 2017

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Outline

1. A comment about the JPL study of LSST performance 2. Estimates of false positive rates for LSST 3. A general comment about NEO completeness estimates 4. What will LSST do for NEOs?

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1. A comment about the JPL study of LSST performance

− analogous simulations were also performed by the LSST Project; detailed report is public: http://ls.st/LDM-156

− reported IOD (Initial Orbit Determination) runtimes for existing codes are from several times to up to an order of magnitude shorter than planned LSST allocation for MOPS

− therefore, there is quite a robust compute reserve (N.B. the MOPS compute needs are at least an order of magnitude more modest than resources needed for other LSST data processing)

− these simulations and improved understanding of the algorithms imply that false positive rates several times higher than anticipated could be readily handled (relatively easily up to about 10 times higher rates)

− most importantly, our results are fully statistically consistent with the results reported by the JPL group

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2. Rates of false positives in image differencing expected for LSST

We have empirically estimated the false positive rate using DECam images and LSST software. The full report is publicly available at http://ls.st/j88 Summary: - DECam data from DECam NEO survey (PI: Lori Allen, NOAO) - need to be careful about covariant per-pixel noise when computing SNR

- for SNR>5 threshold: 500-1000 sources per sq. deg.

- after exclusion of static sources (1% loss of fill factor), there are on average 350 sources per sq. deg. (includes false positives, asteroids, and true astrophysical transients) - based on visual inspection, an upper limit for false positives in DECam imaging data is 263 sources per sq. deg., and implies a rate of 460 per sq. deg. for LSST

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2. Rates of false positives in image differencing expected for LSST

- the rate of false positives is a strong function of SNR:

Need to be careful about covariant noise when computing SNR:

False positive rates due to background

fluctuations for LSST:

SNR FP (per sq.deg) 4.0 4,200 4.5 570 5.0 60 5.5 5 6.0 0.3

It is likely that at least some reported high

false positive rates are due to this effect.

http://ls.st/j88

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3. A general comment about NEO completeness estimates

One has to be extremely careful when comparing different simulations because there are a number of important systematics effects, e.g.: - NEO vs. PHA: 3-4% (larger for PHAs) - the contribution of known objects (~10-20%) - the window width: 30 days vs. 15 days is worth 2% - effective detection depth and NEO brightness prediction: 0.2

mag is worth 2% - of course, cadence details matter, too One can easily change numerical estimates from 55% to 85% by changing underlying assumptions!

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The current baseline cadence is optimized for science returns.

6% of observing time is optimized for asteroids

(300 visits in griz)

For detailed analysis, see http://ls.st/0si

4. What will LSST do for NEOs? From the science characterization point of view, LSST is not strongly driven by the NEO completeness. For example, reducing the bias in and understanding the selection function is a much stronger science driver.

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The completeness for the baseline cadence (left) is still rising after 10 years of surveying - an additional 3-4% can be gained with, e.g. two, additional years of surveying: this motivates astro_lsst_01_1016

10 12

1) astro_lsst_01_1016 - increased Ecliptic coverage (24%, with 69% for

the main survey; for baseline 6% and 85%) - in 12 years, the main survey gets 98% of the

observing time allocated to it in the nominal 10-year survey: for other science programs, this cadence has very similar performance as the baseline cadence

NEO-enhancements to baseline cadence

baseline: minion_1016

astro_lsst_01_1016 years

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Main conclusions: 1) By improving analysis software and running an NEO-optimized survey

(astro_lsst_01_1016) for 12 years, the completeness for PHAs with H<22 can be boosted from 66% to 77%

2) With the contribution of known objects, the total completeness could approach 90% 3) The NEO-optimized cadence obtains as many visits for the main survey in 12 years as the baseline survey in 10 years: therefore, the impact on other science programs is about neutral (e.g. negative impact: it takes 5 years for what is done in 4 years with baseline; positive impact: proper motion errors larger by 20% using baseline cadence) 4) The NEO-optimized cadence astro_lsst_01_1016 could serve as a model for NASA’s participation in LSST operations.

Baseline vs. NEO-enhanced cadences:

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Summary 1. Our simulations agree with the JPL study. 2. Empirically estimated upper limit for the LSST false positive rate in image

differencing is 460 per sq. deg. 3. MOPS can handle this rate of false positives with about two orders of

magnitude less computing capacity than planned for other LSST data processing needs.

4. LSST cadence strategy can be optimized to boost

the NEO completeness. Assuming a 12-year survey, and modest improvements in software and data management resources, the completeness for PHAs with H<22 can be (optimistically) increased to close to ~90% (assuming a contribution from known objects)

5. Simulation astro_lsst_01_1016 spends 24% of time on NEO-optimized

observations and runs 2 years longer: it could serve as a model for “NASA’s participation in LSST operations”.

http://ls.st/j88

http://ls.st/LDM-156

http://ls.st/0si