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B9: Astrophysics

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Multi-physics & multi-scale simulations, e.g. supernovae, stellar-evolution, ... (strong, weak, GR, E&M, chemistry,etc) and 10 orders of magnitude in space/time. ... – PowerPoint PPT presentation

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Title: B9: Astrophysics


1
B9 Astrophysics
  • What can be achieved in the next 5 - 10 years ?
  • Dark energy/dark matter data analysis (10
    experiments producing 10TB to 10PB datasets) -
    better understanding of the nature and evolution
    of the universe
  • Multi-physics multi-scale simulations, e.g.
    supernovae, stellar-evolution, galaxy-formation
    and interaction, etc. - often spanning all
    physical interactions/forces (strong, weak, GR,
    EM, chemistry,etc) and gt 10 orders of magnitude
    in space/time.

2
B9 Astrophysics
  • What are the challenges ?
  • Management of data deluge (detectors Moore's
    law)
  • Data access (technical sociological issues)
  • Connecting observation simulation data
  • Numerical precision
  • Auto- cross- validation verification
  • Algorithms implementations
  • Quantifying statistical and systematic
    (including computational) uncertainties
  • Interdisciplinary, interagency, international
    field
  • Education training - this generation next

3
B9 Astrophysics
  • What is the state of the art ?
  • (Mostly) piecemeal by sub-field
  • Code proliferation, much of it in the public
    domain
  • Disparate data formats, models, types,
    distribution, provenance, accessibility
  • How can we accelerate development ?
  • "Decadal survey" for computational astrophysics
  • Interagency coordination (including OMB),
    international outreach

4
B9 Astrophysics
  • What are the expected outcomes and impacts of
    acceleration ?
  • Simulation data standards cf. Virtual
    Observatory
  • Concentrated effort on the most important
    computational astrophysical topics
  • What scale of investment is needed ?
  • Comparable to the experiments DoE is invested in
    already
  • .SNAP/JDEM, LSST, DES, SDSS, etc.

5
B9 Astrophysics
  • What are the risks ?
  • Missed opportunities for optimizing experiments
  • Building something no-one wants. This has to be
    science driven - no one machine/paradigm fits all
  • Who/what are we missing ?
  • Input from many from the field - many are at the
    APS meeting
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