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APPIC meeting APC , 9 May 2014

Data access challenges for the eLISA gravitational space mission. Pierre Binétruy ,. APPIC meeting APC , 9 May 2014. Th e frequency spectrum of gravitational waves. 1 Mkm. M: 0.5 B€, L: 1.5 B€. ESA « Cosmic Vision » 2015-2035. L2. L3. The hot and energetic Universe.

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APPIC meeting APC , 9 May 2014

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  1. Data access challenges for the eLISAgravitationalspace mission Pierre Binétruy, APPIC meeting APC,9 May2014

  2. The frequencyspectrum of gravitationalwaves 1 Mkm

  3. M: 0.5 B€, L: 1.5 B€ ESA «Cosmic Vision » 2015-2035 L2 L3 The hot and energeticUniverse The gravitationalwaveUniverse M7 M6 C:2022,L:2035 C:2020,L:2032 C:2014,L:2028 C<2020,L:2034 M5 M4 S1,… M d’Op C:2014,L:2026 C:2018,L:2030 M2 M3 EUCLID PLATO L:2020 L:2024 JUICE BEPI COLOMBO L1 M1 Solar Orbiter JWST L:2022

  4. White paper :supported by more than 1200 scientists

  5. Someprinciples to redefine the mission LISA NGO: • Keep the sameprinciple of measurement and the samepayload concept • Innovate the least possible with respect to LISAPathfinder • Optimize the orbitand the launcher:removemasse • Simplify the payload Solutions • Remove one of the triangle arms: • mother-daughter configuration • Reduce the arm lengthfrom5 Mkm to 1 Mkm • New orbitcloser to Earth (drift away) • Inertialsensoridentical to LISAPathfinder • Nominal mission length: 2years(ext. to 5years)

  6. Roadmap for eLISA • eLISA Science Theme selected as L3 in 2013 • Technology Roadmap work 2013 – 2015 • Possibly continued Mission Concept Study 2014 – 2015 • Successful LISA Pathfinder flight in 2015 • Assessment of technology status • Possibly additional work, e.g. breadboardingof Payload + (1 to 4) years • Selection of Mission Concept in 2015 + (1 to 4) • Possibly Start EQM of complete Payload 2015 + (2 to 5) • Start of Industrial Definition Study 2015 + (2 to 5) • Start of Industrial Implementation 2015 + (6 to 9) • Launch in 2015 + (15 to 18)

  7. The European consortium for eLISA ? Data Centre SE lead

  8. Thescienceof eLISA

  9. A. Petiteau

  10. Astronomy ofsupermassive black holes in the 2020s Distance (in redshift) ET (proposed) eLISA Future Obs.EM LSST, JWST, EELT, X rays Redshift Z  SNR SKA, Pulsar Timing aLIGO, aVIRGO, KAGRA Mass [log M/M☉]

  11. Testofgravityin strongregime Plunge Merger Ringdown RG: approximation postNewtonienne Théorie de perturbation RG: relativité numerique LGW = 1023 L

  12. EMRI (Extreme Mass Ratio Inspiral) Gravitationalwavesproduced by massive objects (stars or black holes of mass10 to 100 M) falling into the horizon of a supermassive black hole. Allows to identify in a unique way the geometry of space-time close to a black hole (the object cycles some 105 times beforeplunginginto the horizon)

  13. Data analysis Challenge: signalsfrom the wholeUniverse all with a latge S/N ratio. How to separatethem? (≠groundinterferometers)

  14. important progress of the analysismethodsthese last years thanks to theMock LISA Data Challenge • 4supermassive black holes • 5 EMRI • 26.1 milliongalacticbinaries • instrumental noise

  15. Data processing Data policy: all data publiclyreleased tous membres consortium consortium France

  16. Centre François Arago (APC): external data center for the LISAPathfinder mission (2015-2016) foreseen data processing center for eLISA François Arago Centre (FACe) LISAPathfinderexerciseatFACe

  17. eLISA Phase 0

  18. Final Adoption Launch Development Science Operations Post Op Misson lifecycle Assumption: 5-year science operations (max) Definition Cruise Commissioning Calibration Consortium activities before DPC starting Algorithms Development & simulations Consortium collaboration design & tools Ramp-up Early DPC setup 12 years before launch Consortium meetings follow-up DPC starting DPC Design DPC Development Algorithms & Pipeline Development DPC lifecycle Pipeline Testing NGO Products Generation NGO Simulations NGO Products Distribution DPC support Phases E1, E2 Phases 0, A Phase B Phases C, D

  19. Physical Infrastructures criteria & scenarios 3 criteria, 4 scenarios §9.1

  20. Physical Infrastructures scenarios: Key characteristics Frontier may depends on scenario & technology. Example: hadoop as-a-service could be in « OS » layer

  21. Simulated use case of infrastructure needs Unanticipated peaks (Arbitrary here) Weekly recurring analysis Scenario 4 Purely on-demand infrastructures Scenarios 1, 2 and 3 Dedicated or Reserved infrastructures • These scenarios are characterized by an initial investment equals to maximum needs to be sure to be able to cover resource needs • This scenario maximizes resource allocation by providing on-demand hosting according to on-the-fly needs. It allows managing resource needs, without facing any initial investment: resource allocation depends upon the instantaneous needs of the resources

  22. 2.1 Early DPC

  23. Whystartsoearly? • allow as soon as possible the community to develop code in a coordinatedway: this • isvery important if one has to release the data publicly. • coordinatewith the groundinterferometers • the data willaddress a large community (astrophysicists) whichis not used to thiskind of • data: providesimulated data and associated software to getacquaintedwithsuch data. • becausethisis a discovery mission, the development of code will not stop with the • launch: conceive the centre and itsdevelopmentplatform in waythatallowsflexibility • and adapt to new discoveries or new theories; betterstartearly to benefitevolution of • thinking in comingyears.

  24. WebsiteeLISA https://www.elisascience.org/

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