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Actionable Intelligence from Multisourced Events

Actionable Intelligence from Multisourced Events. Roy Williams Caltech with S.G.Djorgovski, C. Donalek, A. Drake, M. Graham, A. Mahabal, R. Seaman (NOAO). please pray for VAO. VOEvent VORapid Skyalert Infrastructure Decision = Human + Archive + Machine. What is VOEvent.

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Actionable Intelligence from Multisourced Events

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  1. Actionable Intelligence from Multisourced Events Roy Williams Caltech with S.G.Djorgovski, C. Donalek, A. Drake, M. Graham, A. Mahabal, R. Seaman (NOAO).

  2. please pray for VAO • VOEvent • VORapid • Skyalert Infrastructure • Decision = Human + Archive + Machine

  3. What is VOEvent • XML document according to VO-standard • Who, Wherewhen, Why, Params • Each event belongs to a Stream • key-value-semantic records • Author, Subscriber, Broker functions • Information not Imperative • Follow-ups are other VOEvents • Connected in citation graph to form Portfolio

  4. Microlensing event (OGLE)

  5. Global Event Authors and Followup AAVSO GCN @ NASA/GSFC SWIFT Fermi Integral AGILE LCOGT Liverpool Telescope La Palma Faulkes Hawaii/Australia PannSTARRS eStarExeter Caltech UKIRT Hawaii OGLE III Poland NOAOTucson Palomar P60 Caltech Stream Author Publisher Repository Relay Followup Subscriber Pairitel Berkeley CTIO/KPNO MOA Catalina RTS UAriz

  6. VORapidVirtual Observatory Rapid Transients Facility please pray for VAO • Transport • Tier 1 • Broker, Forward • Tier 2 • Applications • Content • VOEvent • Consume • Browse, Query, Subscribe, Decision • Author • Streams, Alerts, Automation • Portfolio • Annotation, Mining • Brokering

  7. Tier1 and Tier2 Event Nodes please pray for VAO Repository (Tier2) Author (Tier2) Jabber/XMPP or custom TCP? Broker (Tier1) • Tier1: • Immediate forwarding, standard & capable protocols • Tier2: • Subscription service, Repository, Query, Archive, Machine Learning, etc etc

  8. International GCN Broker skyalert.org Annotation from archives SkyAlert Astronomers Amateurs Students Microlensing Optical transients Radio transients X-ray transients Gamma transients Grav. waves Neutrinos Events and annotation disseminated to subscribers in real time with intelligence Followup Scheduler Telescope Telescope Telescope Event Authors Event Subscribers

  9. Stream as Event TemplateTaming Multisourced Data stream event skyalert.org made byrobot system at night made byperson in daytime

  10. skyalert.org Future .... ASKAP, IceCube LIGO/Virgo, MWA, SkyMapper, Veritas TeV secondary streams

  11. Portfolio of OneFirst Discovery VOEvent skyalert.org

  12. Rich portfolio: Supernova 2007sr in Antennae Groundbasedtelescope skyalert.org DSS1967 Hubble WFPC2 Astronomer’sTelegram Lightcurve Spectrum Discovery Image

  13. subscribers Event-Action-Event Cycle Streams (event template) Events (template instance) Triggers (for action) Annotation(linked data) skyalert.org Catalina Sky Survey mag1 mag2 mag3 asteroidness stellarity cutoutURL Original detection 19.34 19.32 19.39 1% 97% http://..... Archive follow-up SDSS archive uMag gMag uCutoutURL gCutoutURL 21.42 20.96 http://..... http://..... Berkeley lightcurves lightcurveURL probSN probCV Archive follow-up http://...... 87% 12% joint rule Palomar 60” follow-up gmag rmag ipmag zpmag datapackageURL Telescope follow-up 19.07 18.03 18.57 18.94 http://.....

  14. Google Sky has VOEvents skyalert.org Thanks to Ryan Scranton

  15. WWT Realtime Event Display skyalert.org Thanks to Jonathan Fay

  16. Bayesian Learning • Feature Vectors • Best Recommendation • Combine with Human judgment • Error bars, upper limits, and missing values • All are part of the prior • Summing opinions of multiple experts • Some not experts! • Relevance Vector Machine • Best of training set (most learning) • Tutorial • Escalation to real expert

  17. Building Feature Vector Mahabal et al arXiv:0810.4527 [astro-ph]

  18. Recommendation for Follow-Up Mahabal et al arXiv:0802.3199 [astro-ph] Sparse data  Ambiguous classification  best follow-up strategy to reduce confusion Example: optical light curve with a particular time cadence would discriminate between a Supernova and a quasar, Example: particular color measurement would discriminate between a cataclysmic variable eruption and a gravitational microlensing event,

  19. Human Volunteers • Science Layer • Describe what you see in image • Each person has level of expertise • How to use data most effectively • Game Layer • Makes people come back • Top 10 ranking etc • Anonymous partner a la gwap.com

  20. Human Computing • Ask humans to *describe* • not *interpret*

  21. Automated Decision through Tripod of Data decision human archive machinelearning • Archive • nearby radio source escalates p(blazar) • nearby galaxy escalates p(supernova) • Human • Crowded field? Artifact present? • Can make follow-up observation • Machine • Fuzzy center escalates p(host galaxy) • Moving source escalates p(asteroid) • Bobotic follow-up observation

  22. skyalert.org • Please try skyalert.org • register, then set an alert • Do you have a stream of astronomical events?(and can I have them) • Who knows how to make a scalable push network?(can we talk?)

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