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The Pan-STARRSMoving Object Processing System
(& Science)
Robert Jedicke(for the Pan-STARRS collaboration)
Institute for AstronomyUniversity of Hawaii2004 September 29
IMPACT
IMPACT
IMPACT
The Pan-STARRSMoving Object Processing System
(& Science)
Robert Jedicke(for the Pan-STARRS collaboration)
Institute for AstronomyUniversity of Hawaii2004 September 16
Robert Jedicke(for the Pan-STARRS collaboration)
Institute for AstronomyUniversity of Hawaii2004 September 16
The Pan-STARRSMoving Object Processing System
(& Science) (& Science)
Bigger Further Slower Dumber
Bigger Further Slower Dumber
DEFINITIONS
COMETS
ASTEROIDS
icier
dirtier
DEFINITIONSNear Earth Objects (NEO)
NEO ZONEPerihelion < 1.3AU
(about 130 million miles)
DEFINITIONSPotentially Hazardous Objects (PHO)
PHO ZONEMOID < 0.05 AU
(about 5 million miles)
PHO Orbit
Earth Collisionat perihelion
Non-Collision ‘PHO’ Orbit
Not at Earth’sorbit at perihelion
1995 CR
DEFINITIONSDeath Plunge Objects (DPO)*
* Not an official acronym
Solar System Animation #3
Main Belt Objects
DEFINITIONS
TrojansTrojans
DEFINITIONSTrans-Neptunian Objects (TNO)
Comets
Long Period Comets
HalleyFamilyComets
Short PeriodComets
Centaurs
DEFINITIONS
Oort Cloud
3 light years
The Pan-STARRS Moving Object Processing System
(MOPS)
Selected PanSTARRS’s TopLevel 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.
Selected PanSTARRS’s TopLevel 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.
Why?
REASON #1
REASON #2
SPACEGUARD GOAL
SPACEGUARD GOAL
NASA NEO SDT
• 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
REASON #3
REASON #4
Existing Surveys
• 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
SpacewatchKitt Peak, AZ
LINEARWhite Sands, NM)
LONEOSFlagstaff, AZ
UHASMauna Kea, HI
NEAT/JPLHaleakala, Maui
NEAT/JPLPalomar, CA
CSS - NorthMt. Lemmon, AZ
CSS -SouthAustralia
• Links detections to known objects
• Identifies new objects
• Fits orbits to all objects with new detections
• Much more…
Existing Surveys – Step 2Linkage & Orbit Determination
• Links detections to known objects
• Identifies new objects
• Fits orbits to all objects with new detections
• Much more…
MPC
• Refine orbits• Calculate
impact probability
Existing Surveys – Step 3Impact Risk Assessment
• Refine orbits• Calculate
impact probability
• 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
Moving Object Processing System
Telescopes&
Survey
ImageProcessing
PipelineMOPS Impact
Probability
Pan-STARRS
Moving Object Processing System
Moving Object Processing System
• 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
Moving Object Processing System
• 107 asteroids within range of PanSTARRS
• ~200 / deg2 @ V=24 @ on ecliptic
• 107 detections / month (20X current rates)
PanSTARRS Asteroid Surveying
• 107 asteroids within range of PanSTARRS
• ~200 / deg2 @ V=24 @ on ecliptic
• 107 detections / month (20X current rates)
Cumulative Observations
0
20,000,000
40,000,000
60,000,000
80,000,000
100,000,000
120,000,000
140,000,000
160,000,000
1995
1996
1997
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
PS1 Starts
• Every survey mode obtains at least twoimages 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
twoimages 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
• 2 detections/nightwith multi-night linking
•synthetic data
increased sky coverage push deeper into noise more objects
real-time system monitoring efficiency determination correction for selection effects
Moving Object Processing System
• 2 detections/nightwith multi-night linking
•synthetic data
increased sky coverage push deeper into noise more objects
real-time system monitoring efficiency determination correction for selection effects
Transient Detection (IPP)
+
+
+
Combined
4 Telescopes
Moving
Stationary
Static
Transients
Transient Types
Fast Asteroidal Object
Normal Asteroidal Object
Slow Asteroidal Object
Death Plunge Object
Supernovae/GRB
Cometary ObjectDifference
Linking Detections
Day 11 Field-of-view1500 real detections +1500 false detections
Linking Detections
Day 51 Field-of-view1500 real detections +1500 false detections
Linking Detections
Day 91 Field-of-view1500 real detections +1500 false detections
•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
• 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)
Data Storage
• Large by most astronomical standards• Small in comparison to Pan-STARRS (~1%)
500 TerraBytes
• 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
• 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
MOPS: Known Object Attribution
MOPS: Synthetic Detection & Noise Generation
MOPS: Orbit Determination & Attribution Loop
MOPS: Linking New Detections
The Pan-STARRS
Solar System Survey & Science
Solar System Survey Locations
Evening Sweet Spot Morning Sweet SpotOpposition
19:00 HST 00:00 HST 05:00 HST
• 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?
• 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
• 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
Trojans of all giant planets L4 & L5 swarm statistics Genetic families SFD through rollover at H~11
Pan-STARRS & Trojan Asteroids
0
1
2
3
4
5
6
1 2 3 4
Series1
Series2Known
Pan-STARRS
Jupiter Saturn Uranus Neptune1
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
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 ! ! !
• 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
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
Pan-STARRS & Distant Planets
Jewitt 2003, ‘Project Pan-STARRS and the Outer Solar System,’ EMP
New Plutos320AU New Earths
620AU (50AU)
New Neptunes1230AU (130AU)
New Jupiters2140AU (340AU)
Pan-STARRS Minor Planet Summary
0
1
2
3
4
5
6
7
8
1 2 3 4 5 6 7 8 9 10
Series1
Series2
Series3
1
10,000,000
1,000,000
100,000
10,000
1,000
100
10
Known
PS 1 Year
PS 10 Years
NEO
/ PH
OM
ain B
eltJovian
Trojans
Oth
er Trojans
Cen
taurs
Com
etsTN
Os
Wid
e TNO
Bin
aries
Com
pan
ions
Interstellar V
isitors
PS1 - 2006PS4 - 2008Coming soon to an island near you.
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<R1 and G2>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