LSST: Comprehensive NEO detection, characterization, and orbits Zeljko Ivezic 1 & R. Lynne Jones 1 1 University of Washington The Large Synoptic Survey Telescope (LSST) has Solar System mapping as one of its four key scientific design drivers, with emphasis on efficient Near-Earth Object (NEO) and Potentially Hazardous Asteroid (PHA) detection, orbit determination, and characterization. The baseline design satisfies strong constraints on the cadence of observations mandated by PHAs, such as closely spaced pairs of observations to link different detections and short exposures to avoid trailing losses. Due to frequent repeat visits LSST will effectively provide its own follow-up to derive orbits for detected moving objects. Detailed modeling of LSST operations, incorporating real historical weather and seeing data from Cerro Pachon in Chile, the LSST site, shows that LSST could find 90% of the PHAs with diameters larger than 250 m LSST using its baseline design cadence, and 75% of those greater than 140 m within ten years. However, simulations also show that LSST can reach the completeness of 90% of PHAs larger than 140m by optimizing observing cadence and extending the survey lifetime to 12 years. In addition to detecting and determining orbits for these PHAs, LSST will also provide valuable data on their physical characteristics through accurate color and variability measurements, which can be used to determine approximate taxonomical types, better size estimates by constraining albedos, rotation periods, and shape characteristics; thus constraining PHA properties relevant for risk mitigation strategies Detecting NEOs with LSST LSST will detect approximately 100,000 NEOs over its 10 year survey lifetime. LSST’s typical r band limiting magnitude of 24.5 enables detection of NEOs larger than 140m in diameter out to distances of about 2.5 AU. LSST could detect NEOs at solar elongations as small as 45 degrees; although these observations would be at high airmass, LSST would still be sensitive enough to detect NEOS as small as 140m to be detected on Venus-like orbits. NEO Impact Hazard In 2005 Congress directed NASA to implement a near-Earth object survey that would catalogue 90% of NEOs larger than 140m by Simulations of LSST detection capabilities indicate that LSST would discover 75% of the target population within ten years under the baseline cadence. These simulations were based on: Simulated pointing histories for LSST created by the Operations Simulator (OpSim). The OpSim uses a high-fidelity model of the telescope (slew speed, cable wraps, etc), real historical weather and seeing data from Cerro Tololo and Cerro Pachon (the LSST site), a model of the sky brightness similar to those available from other observatories, and a set of “proposals” based on the actual science goals that drive the observing cadence. PHA populations based on the known PHAs larger than 1km and (simulated separately) a synthetic population provided by Al Harris Simulated observations of each of those model PHAs over the lifetime of LSST; if the PHA was detected at a SNR>5, for at least 3 nights within a 30 night window with at least 2 observations within a 90 minute window on each night, it was considered “found”. While under the baseline survey cadence, LSST would discover 75% of the PHAs larger than 140m within ten years, reaching the Congressional goal of 90% would require a modified and extended NEO-Optimized survey, dedicating 15% of survey time to higher airmass searches near sun and along the northern ecliptic. This is possible by extending the survey lifetime to 12 years. Left: Number of potential impactors as a function of size/impact energy (Harris; modified from the 2007 NASA NEO report). Below: LSST NEA detection completeness as a function of size, after 10 years (solid line) and after 12 years of NEO- emphasized observing (upper dashed line). Early Warning Limits The available warning time is an important consideration for NEO impacts. While warning time for each individual PHA obviously depends on its size and orbit, typical warning time for 45m objects would be 1-3 months with LSST’s faint limiting magnitudes and observing cadence. The plots at right show an example of the available early warning time for a PHA. The top plot illustrates the PHA orbit in a rotating heliocentric system; the magenta line shows the area that LSST could detect a 140m object. The bottom plot shows the velocity vs distance diagram for the same PHA; the red dots show positions in 1-day intervals for which a 45m object would have V<24.5 (LSST’s faint limit) and the blue dots indicate intervals with V<20. The warning time is 39 days with LSST; however, note that LSST would also detect the PHA during three previous close approaches. Above: Histogram of apparent velocities for various populations of solar system objects. LSST Moving Object Pipeline System (MOPS) Discovering NEOs is the most challenging use-case for the moving object pipeline system (MOPS), the software that links individual detections of moving objects into identified objects with orbits. Each night, as images are obtained from the telescope, catalogs of sources detected in ‘difference images’ are created (‘DIASources’), then passed to MOPS along with significant metadata about the images, previous associated detections, and flags related to image processing. At the end of each night, MOPS links the individual DIASources from the night into ‘tracklets’ consisting of two DIASources. To allow for cases where many images of the same object are obtained on the same night, tracklets are then merged. The tracklets typically span 15 to 90 minutes of time and assume simple linear motion. MOPS then links the tracklets into ‘tracks’ using the position and velocity information from the tracks. Three nights of tracklets are required to form a track; the goal is to allow a 30 night window for track generation. The tracks are fit with a quadratic (or higher order) polynomial plus a topocentric correction; tracks with high residuals are rejected immediately. Orbits are then fit to the remaining tracks; orbits with high residuals are rejected. Orbital arcs are extended backward in time by searching for additional previous tracklets consistent with the orbit; as new observations are acquired, additional detections are added to the orbit. LSST will provide the orbits (including uncertainties) and associated DIASources, including their positions, fluxes, and shape measurements (and uncertainties) of all objects in a database updated nightly. More details about MOPS are available in the “Moving Object Pipeline System Design” document at Above: Data flow through the LSST nightly processing pipeline, including MOPS. Above: MOPS associates individual detections (DIASources) into tracklets (2 DIASources), merges tracklets within a night where possible, then links these tracklets into tracks, and finally orbits. Above: MOPS efficiently links DIASources into tracklets, and tracklets into tracks, using kd-trees. Above: MOPS identifies potential tracks by linking tracklets following roughly quadratic motion; then attempts to fit a higher-order polynomial plus a topocentric correction to the DIASources. The top panel of the plot above illustrates the sky motion of an NEO over 20 nights (solid black line); the red dots indicate hypothetical observations at 11pm and midnight each night. The panels to the lower left show the separate RA and Dec motions, including a cubic fit, as a function of time. The panels to the lower right show the residuals between the fit and the actual motion; with a quadratic fit only, these residuals can be 40” and larger. The topocentric correction has not been included here, but is visible in these residuals. This second fitting step reduces the number of initial quadratic-only tracks by approximately a factor of 50. Orbit fitting reduces the number of tracks by approximately another order of magnitude. Above: Size/Absolute Magnitude limits for NEOs discovered at LSST’s typical limiting magnitude in r band, as a function of both geocentric (left column) and heliocentric (right column) distance, and at 60 degrees solar elongation (top row) and opposition (bottom row). Above: The ecliptic plane and the positions of NEAs outside the orbit of Venus. This is an edited version of Figure 3.5 from the 2010 NRC study “Defending Planet Earth: Near-Earth Object Surveys and Hazard Mitigation Strategies”, with additions of the LSST search region for NEOs (the red lines). LSST can point to within 20 degrees of the horizon; this allows observations to 45 degrees solar elongation. Spacecraft Targets By discovering 100,000’s of NEOs, LSST will greatly improve the chances of discovering a suitable, low-velocity, nearby NEO to serve as a target for future spacecraft missions. NEO Characterization LSST will acquire multiple NEO observations in ugrizy (most relevant being griz) filters, allowing generation of rough taxonomies for most objects. In addition, these color classifications will improve the statistical determination of the size distribution. Previous surveys have shown that the albedo distribution of asteroids is bimodal, with one peak having a mean albedo of 0.06 while the other peak has a mean of 0.20 in g or about 0.25 in r or i, and that these peaks are correlated with color. Low albedo MBAs are C-, D-, and P-types asteroids, while those MBAs with higher albedos are S-, R-, V-, E-, and M-type asteroids. While this does not permit firm determination of the albedo for a single object, it does reduce the error on the overall size distribution by 30-50%. MOPS Simulations Simulations of MOPS performance have been run using a test Solar System Model (the S3M; Grav et al., PASP 2011) observed with the baseline LSST observing cadence (realistically simulated using the LSST Operations Simulator), and adding variable amounts of additional ‘noise’ detections. The results show that requiring 2 detections per night, with 3 nights of detections, is sufficient for linking objects moving at main belt speeds and slower, given anticipated LSST noise levels. Further development is planned to improve MOPS performance for NEOs. Artist’s depiction of the DAWN spacecraft orbiting Ceres (Credit: McREL) Asteroid colors can be used to determine a general spectral type (left; Parker et al. Icarus 2008); these colors also map to albedo, as demonstrated by NEOWISE (right; Mainzer et al. AJ 2012 – albedo in visible to left, in IR to right)