1. The Pan-STARRS M oving O bject P rocessing S ystem (& Science) Robert Jedicke (for the Pan-STARRS collaboration) Institute for Astronomy University of Hawaii 2004 September 29

2. IMPACT IMPACT

4. The Pan-STARRS M oving O bject P rocessing S ystem (& Science) Robert Jedicke (for the Pan-STARRS collaboration) Institute for Astronomy University of Hawaii 2004 September 16

5. Robert Jedicke (for the Pan-STARRS collaboration) Institute for Astronomy University of Hawaii 2004 September 16 The Pan-STARRS M oving O bject P rocessing S ystem (& Science) (& Science)

6. Bigger Further Slower Dumber

8. DEFINITIONS COMETS ASTEROIDS icier dirtier

9. DEFINITIONS Near Earth Objects (NEO) NEO ZONE Perihelion < 1.3AU (about 130 million miles)

10. DEFINITIONS Potentially Hazardous Objects (PHO) PHO ZONE MOID < 0.05 AU (about 5 million miles)

11. PHO Orbit Earth Collision at perihelion

12. Non-Collision ‘PHO’ Orbit Not at Earth’s orbit at perihelion

13. 1995 CR

14. DEFINITIONS Death Plunge Objects (DPO)* * Not an official acronym

15. Solar System Animation #3

16. Main Belt Objects DEFINITIONS Trojans

17. DEFINITIONS Trans-Neptunian Objects (TNO) Comets Long Period Comets Halley Family Comets Short Period Comets Centaurs

18. DEFINITIONS Oort Cloud 3 light years

19. The Pan-STARRS M oving O bject P rocessing S ystem (MOPS)

20. Selected PanSTARRS’s Top Level Science Requirements MOPS shall create and maintain a data collection of detections and object parameters (e.g. orbit elements, absolute magnitudes) for >90\% of the PHOs that reach R=24 for  12 contiguous days during the course of Pan- STARRS operations. MOPS shall create and maintain a data collection (DC) of detections and object parameters (e.g. orbit elements, absolute magnitudes) for >90% of the members that reach R=24  12 contiguous days within each class of solar system object (Main Belt, Trojan, Centaur, TNO, Comet, etc, except NEO and PHO) during the course of Pan-STARRS operations.

21. Selected PanSTARRS’s Top Level Science Requirements MOPS shall create and maintain a data collection of detections and object parameters (e.g. orbit elements, absolute magnitudes) for >90\% of the PHOs that reach R=24 for  12 contiguous days during the course of Pan- STARRS operations. MOPS shall create and maintain a data collection (DC) of detections and object parameters (e.g. orbit elements, absolute magnitudes) for >90% of the members that reach R=24  12 contiguous days within each class of solar system object (Main Belt, Trojan, Centaur, TNO, Comet, etc, except NEO and PHO) during the course of Pan-STARRS operations.

22. Why?

23. REASON #1

24. REASON #2

25. SPACEGUARD GOAL

27. NASA NEO SDT

28. 99% completion of PHOs with D>1km  90% reduction in residual global impact risk 90% completion of PHOs with D>300m  50% reduction in sub-global impact risk Pan-STARRS & PHOs 99% completion of PHOs with D>1km  90% reduction in residual global impact risk 90% completion of PHOs with D>300m  50% reduction in sub-global impact risk

29. REASON #3

30. REASON #4

31. Existing Surveys

32. 3-5 images/night Linear motion Very low false- positive rate 3-5 images/night Linear motion Very low false- positive rate Existing Surveys – Step 1: Discovery & Identification Spacewatch Kitt Peak, AZ LINEAR White Sands, NM) LONEOS Flagstaff, AZ UHAS Mauna Kea, HI NEAT/JPL Haleakala, Maui NEAT/JPL Palomar, CA CSS - North Mt. Lemmon, AZ CSS -South Australia

33. Links detections to known objects Identifies new objects Fits orbits to all objects with new detections Much more… Existing Surveys – Step 2 Linkage & Orbit Determination Links detections to known objects Identifies new objects Fits orbits to all objects with new detections Much more… MPC

34. Refine orbits Calculate impact probability Existing Surveys – Step 3 Impact Risk Assessment Refine orbits Calculate impact probability

35. Fully integrated Detection, attribution, linking, orbit identification Orbit fitting Parallel synthetic data analysis  Real-time efficiency/bias Fully integrated Detection, attribution, linking, orbit identification Orbit fitting Parallel synthetic data analysis  Real-time efficiency/bias M oving O bject P rocessing S ystem Telescopes & Survey Image Processing Pipeline MOPS Impact Probability Pan-STARRS

36. M oving O bject P rocessing S ystem

38. MPC requires that reported detections be real  forces Pan-STARRS to obtain  3 images/night  reducing total sky coverage  reducing total discoveries Difficult to control/monitor system efficiency  introduce synthetic objects into data stream  determine efficiency in real time  monitor system performance in real time MPC requires that reported detections be real  forces Pan-STARRS to obtain  3 images/night  reducing total sky coverage  reducing total discoveries Difficult to control/monitor system efficiency  introduce synthetic objects into data stream  determine efficiency in real time  monitor system performance in real time M oving O bject P rocessing S ystem

39. 10 7 asteroids within range of PanSTARRS ~200 / deg 2 @ V=24 @ on ecliptic 10 7 detections / month (20X current rates) PanSTARRS Asteroid Surveying 10 7 asteroids within range of PanSTARRS ~200 / deg 2 @ V=24 @ on ecliptic 10 7 detections / month (20X current rates)

40. Cumulative Observations PS1 Starts

41. Every survey mode obtains at least two images at each location separated by a Transient Time Interval (15-30 minutes)  serendipitous positions & colours Solar system survey re-visits each location after 3-6 days  obtain 3-4 nights/month  ~12 day arc Observing Cadence Every survey mode obtains at least two images at each location separated by a Transient Time Interval (15-30 minutes)  serendipitous positions & colours Solar system survey re-visits each location after 3-6 days  obtain 3-4 nights/month  ~12 day arc

42. 2 detections/night with multi-night linking synthetic data  increased sky coverage  push deeper into noise  more objects  real-time system monitoring  efficiency determination  correction for selection effects M oving O bject P rocessing S ystem 2 detections/night with multi-night linking synthetic data  increased sky coverage  push deeper into noise  more objects  real-time system monitoring  efficiency determination  correction for selection effects

43. Transient Detection (IPP) + + + Combined 4 Telescopes Moving Stationary Static Transients

44. Transient Types Fast Asteroidal Object Normal Asteroidal Object Slow Asteroidal Object Death Plunge Object Supernovae/GRB Cometary Object Difference

45. Linking Detections Day 1 1 Field-of-view 1500 real detections + 1500 false detections

46. Linking Detections Day 5 1 Field-of-view 1500 real detections + 1500 false detections

47. Linking Detections Day 9 1 Field-of-view 1500 real detections + 1500 false detections

48. Brute force (MPC) approach  100X Pan-STARRS computing power kd-tree (CMU) approach  ~1/3 Pan-STARRS computer power Linking Detections Brute force (MPC) approach  100X Pan-STARRS computing power kd-tree (CMU) approach  ~1/3 Pan-STARRS computer power

49. Must include –All major solar system perturbing bodies –Full error analysis Two available solutions –AstDys (Italy) –JPL (USA) Orbit Determination Must include –All major solar system perturbing bodies –Full error analysis Two available solutions –AstDys (Italy) –JPL (USA)

50. Data Storage Large by most astronomical standards Small in comparison to Pan-STARRS (~1%) 500 TerraBytes

51. Inject synthetic objects into MOPS parallel to real data analysis  monitor system efficiency for correcting observational selection effects  monitor system performance to flag unusual behavior Synthetic Data Inject synthetic objects into MOPS parallel to real data analysis  monitor system efficiency for correcting observational selection effects  monitor system performance to flag unusual behavior

52. Synthetic model matches real distributions  all asteroid and comet types  realistic orbit and size distribution  realistic shape, rotation periods, pole orientations  + ‘unusual’ orbits e.g. hyperbolic interstellar, retrograde main belt, distant Earths Synthetic Data Synthetic model matches real distributions  all asteroid and comet types  realistic orbit and size distribution  realistic shape, rotation periods, pole orientations  + ‘unusual’ orbits e.g. hyperbolic interstellar, retrograde main belt, distant Earths

53. MOPS : Known Object Attribution

54. MOPS : Synthetic Detection & Noise Generation

55. MOPS : Orbit Determination & Attribution Loop

56. MOPS : Linking New Detections

57. The Pan-STARRS Solar System Survey & Science

58. Solar System Survey Locations Evening Sweet Spot Morning Sweet SpotOpposition 19:00 HST00:00 HST05:00 HST

59. Tens of thousands of NEOs  Size-frequency distribution  Orbit distribution  Source fitting  Genetic families? Pan-STARRS & NEOs/PHOs Tens of thousands of NEOs  Size-frequency distribution  Orbit distribution  Source fitting  Genetic families?

60. Pan-STARRS will find as many objects in one lunation as have been identified since the discovery of Ceres in 1801 Pan-STARRS & the Main Belt

61. 10,000,000 MB objects in ten years  Size-frequency distribution  Orbit distribution  New small asteroid families  Asteroid/comet transition objects  Asteroid collisions  Pole Orientations  Rotation Rates  Shapes 10,000,000 MB objects in ten years  Size-frequency distribution  Orbit distribution  New small asteroid families  Asteroid/comet transition objects  Asteroid collisions  Pole Orientations  Rotation Rates  Shapes Pan-STARRS & the Main Belt

62.  Trojans of all giant planets  L4 & L5 swarm statistics  Genetic families  SFD through rollover at H~11 Pan-STARRS & Trojan Asteroids Known Pan-STARRS JupiterSaturnUranusNeptune 1 10 100 1,000 10,000 100,000 1,000,000 Jewitt 2003, ‘Project Pan-STARRS and the Outer Solar System,’ EMP  Trojans of all giant planets  L4 & L5 swarm statistics  Genetic families  SFD through rollover at H~11

63. Pan-STARRS & Comets Pan-STARRS will find ~10X as many comets per year as all existing surveys 1,000’s of comets in ten years operation  Dormant detections at large distance  Size-frequency distribution  Orbit distribution INTERSTELLAR ! ! ! Pan-STARRS will find ~10X as many comets per year as all existing surveys 1,000’s of comets in ten years operation  Dormant detections at large distance  Size-frequency distribution  Orbit distribution INTERSTELLAR ! ! !

64. Comet designation problem New Proposal  Comet Jedicke-XXX  X=(0-9,a-z,A-Z) (base 62)  allows for ~240,000 comets P/Jedicke 1996A1 Pan-STARRS & Comets

65. Pan-STARRS & TNOs ~20,000 TNOs  Inclination distribution  Size-frequency distribution  Orbit distribution / dynamical structure  More Plutos?  ~100 wide binaries ~20,000 TNOs  Inclination distribution  Size-frequency distribution  Orbit distribution / dynamical structure  More Plutos?  ~100 wide binaries

66. Pan-STARRS & Distant Planets Jewitt 2003, ‘Project Pan-STARRS and the Outer Solar System,’ EMP New Plutos 320AU New Earths 620AU (50AU) New Neptunes 1230AU (130AU) New Jupiters 2140AU (340AU)

67. Pan-STARRS Minor Planet Summary 1 10,000,000 1,000,000 100,000 10,000 1,000 100 10 Known PS 1 Year PS 10 Years NEO / PHO Main Belt Jovian Trojans Other Trojans Centaurs Comets TNOs Wide TNO Binaries Companions Interstellar Visitors

68. PS1 - 2006 PS4 - 2008 Coming soon to an island near you.

69. Pan-STARRS Problem: Pan-STARRS plans on using a very wide ‘Solar System’ G filter but is required to reach R=24. Assuming that the R-filter transmission is 100% in the range [R1,R2] and 0% outside that range and that the G-filter has similar performance in the range [G1,G2] where G1 R2, what is the ratio of the required exposure times in the two filters to reach R=24 in the AB magnitude system? Assuming that Vega is a black-body, what is the answer in the Johnson system? Make other reasonable assumptions as necessary