The Pan-STARRS Moving Object Pipeline

Transcription

1 Astronomical Data Analysis Software and Systems XVI O4-1 ASP Conference Series, Vol. XXX, 2006 R.Shaw,F.HillandD.Bell,eds. The Pan-STARRS Moving Object Pipeline Larry Denneau, Jr. Pan-STARRS, Institute for Astronomy, University of Hawaii, Honolulu, HI, Jeremy Kubica Google, Inc., Pittsburg, PA, Robert Jedicke Institute for Astronomy, University of Hawaii, Honolulu, HI, Abstract. The Moving Object Processing System (MOPS) team of the University of Hawaii s Pan-STARRS telescope is developing software to automatically discover and identify >90% of near-earth objects (NEOs) 300m in diameter and >80% of other classes of asteroids and comets. MOPS relies on new, efficient multiple-hypothesis KD-tree and variabletree search algorithms developed by Kubica and the Carnegie Mellon AUTON Laboratory to search the detection pairs obtained per night. Candidate intra- and inter-night associations of detections are evaluated for consistency with a real solar system object and orbits are computed. We describe the basic operation of the MOPS pipeline, identify pipeline processing steps that are candidates for multiple-hypothesis spatial searches, describe our implementation of those algorithms and provide preliminary results for MOPS. 1. Introduction The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) under development by Institute for Astronomy at the University of Hawaii will provide state-of-the-art capability in several areas: four wide-field (7 deg 2 ) identical telescopes operating in parallel, a 1.4-gigapixel orthogonal transfer array (OTA) detector for each telescope, 0.01-arcsecond astrometric precision, and a limiting magnitude of R = 24. In a single night Pan-STARRS will observe approximately 6000 deg 2 of sky. All combined, this capability will allow Pan- STARRS to perform automated asteroid searching on an unprecedented scale. The Pan-STARRS single prototype telescope, called PS1, is expected to see first light in 2007, followed by the complete four-telescope system, PS4, in The Moving Object Processing System (MOPS) client of the Pan-STARRS Image Processing Pipeline (IPP) is developing software to automatically discover and identify >90% of near-earth objects (NEOs) 300m in diameter and >80% 1

2 2 Denneau, Kubica & Jedicke of other classes of asteroids and comets. MOPS is a traditional software pipeline that runs unattended and continuously to perform its asteroid discovery. 2. MOPS Synthetic Solar System The MOPS Synthetic Solar System (SSS) is an artifically-generated population of 11 million objects that represent various populations of objects that will be discovered by MOPS. The SSS exists so that the MOPS pipeline can be developed and verified using realistic data and so that operating efficiencies can be monitored. During the operation of PS4, the MOPS will require a way to measure how efficiently it is processing its input data stream. The MOPS will estimate its operating efficiency on real sky data by injecting its full synthetic model into the real data stream and then extract efficiency parameters from the synthetic stream after processing. As the MOPS discovers and verifies new objects, in particular near-earth objects (NEOs) and potentially hazardous objects (PHOs), orbital parameters and observations will be reported to external sources for further evaluation of impact hazard. 3. Intra-Night Linking Each night, the MOPS will search its detections database in order to identify intra-night linkages called tracklets. The simplest tracklet will be a pair of detections obtained within a 30-minute transient time interval (TTI). At 5σ there will be roughly 200 false detections deg 2, which is comparable to the expected sky-plane density of asteroids on the ecliptic (and therefore also comparable to the density of synthetic detections on the sky). At 1 deg/day an object will move about 75 arcsec ( 375 pixels) in a 30-minute TTI. To identify objects moving this fast requires linking all possible pairs of detections in an image that lie within 375 pixels of one another. Fully 50% of pairings attempted in this manner will be incorrectly linked tracklets. The vast majority of solar system objects move slower than about 1 deg/day, and for fast-moving objects there is other information available to reduce the false tracklet rate (detection flux and shape). For instance, at 1 deg/day an object will move about 4.2 pixels in a 30s exposure (assuming 0.3 pixel scale). It is expected that typical Pan-STARRS images will have PSFs on the order of 0.6 and at this rate of motion these objects will suffer trailing losses and measurably distend the appearance of the detection. Tracklets are identified using KD-tree spatial sorting and indexing. KD-tree algorithms can rapidly identify all tracklets in a single night s fields in tens of minutes on a typical science-grade PC. Given the constraints available to the algorithm that will identify tracklets it is expected that the false tracklet rate will be less than a percent.

3 3 Figure 1. Conceptual diagram of intra-night linking. Note that some fast-moving detections have been associated over large distances using elongation information present in the detection. 4. Inter-Night Linking Following the completion of intra-night linking, the MOPS uses the new tracklets linkages and attempts to link them with old tracklets from previous nights. These associations are called tracks. The simplest manner to envisage the process is to extrapolate each tracklet s motion vector to a pre-specified time on the other nights. If the extrapolated position and motion vector coincides with another tracklet on another night then assume that both tracklets represent the same object and use the internight motion to predict the object s location and motion vector on other nights. Once multiple nights of tracklets are linked into a track for a proposed object, an initial orbit determination (IOD) will be computed for the detections in the track. If a sufficiently good IOD is obtained (with low residuals), the MOPS will attempt a differentially-corrected orbit determination. If a sufficiently low residual is again achieved, the track is considered provisionally to be a real object and is inserted into the MOPS database. Otherwise the tracklets are released back into the pool for further processing. The combinatoric complexity of linking millions of tracklets together over many nights is intractable without special spatial indexing and searching. For this problem, MOPS uses a variable-tree algorithm developed by Jeremy Kubica and Carnegie Mellon s AUTON Laboratory. The variable-tree approach generates model tracks using endpoint tracklets at some starting and ending time, then looks for support tracks that satisfy the models. 5. Results The Pan-STARRS MOPS team has performed simulations using full sky-plane density of asteroids and expected density of false detections. With these simulations, MOPS is able to achieve > 99.9% efficiency in its intra-night linkages. The only reason the MOPS is not at 100% is that the intra-night linker is aggresive:

4 4 Denneau, Kubica & Jedicke y T 1 x T 2 T 3 T 4 t Figure 2. Conceptual diagram of inter-night linking. Model tracks are generated using tracklets at times T1 and T4. Support tracks are obtained from times T2 and T3. When a sufficient number of support tracks are obtained, the linkage is returned to MOPS for orbit determination. a small fraction of correct linkages are incorrectly linked into three-detection tracklets with an incorrect detection. Currently we do not believe that this loss will hurt the MOPS s overall efficiency. In Inter-night linking, the MOPS is able to achieve > 97% efficiency with NEOs and > 98% of main belt objects. Losses in intra-night linking are primarily due to non-quadratic sky-plane motion of asteroids; these objects are lost in the variable-tree approach since the variable-tree model requires quadratic sky-plane motion. It is possible to open up thresholds in the variable tree processing to accept larger deviations from quadratic motion at the cost of many more false tracks (and thus greater post-processing). Using current tresholds, variable-tree linking generates nearly 300-to-1 false linkages to correct linkages. Tables 1 and 2 summarize MOPS intra- and inter-night linking performance. The MOPS team is encouraged with these results and believes that KDtree and variable-tree spatial searching will allow MOPS and Pan-STARRS to achieve its NEO discovery goals. Table 1. MOPS Intra-night linking performance. Tracklets Available Found Percent 636, , % Acknowledgments. We are extremely grateful to Carnegie Mellon University and LSST Corporation for their contribution to development of MOPS.

5 5 Table 2. MOPS Inter-night linking performance. Type Available Found Percent NEO % MB 151, , % TNO % False 45M N/A 286X References Kubica, J., Moore, A., Connolly, R., & Jedicke, R A Multiple Tree Algorithm for the Efficient Association of Asteroid Observations. The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2005), ACM Press, Eds. Robert L. Grossman and Roberto Bayardo and Kristin Bennett and Jaideep Vaidya, p Kubica, J., Denneau, L., Grav, T., Heasley, J., Jedicke, R., Masiero, J., & Tholen, D. Efficient Intra- and Inter-night Linking of Asteroid Detections using KD-Trees In preparation. Bottke, W. F., Jedicke, R., Morbidelli, A., Petit, J. & Gladman, B. Understanding the Distribution of Near-Earth Asteroids. Science, 288: , Jedicke, R. Pan-STARRS Moving Object Pipeline Requirements, Institute for Astronomy, University of Hawaii, Kaiser, N., Pan-STARRS Project Team. Asteroid Collision Hazard Reduction Requirements, Institute for Astronomy, University of Hawaii, Chesley, S., Heasley, J., Jedicke, R. & Spahr, T. MOPS: NEO Preliminary Orbit Calculation Studies, Institute for Astronomy, University of Hawaii, Jedicke, R. ACM 2005 Plenary Oral Presentation, Petit, J.-M., Holman, M., Scholl, H., Kavelaars, J. & Gladman, B. An automated moving object detection package. Mon. Not. R. Astron. Soc. 347, , 2004.