Data Analysis Planning Bernard Schutz AEI

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Data Analysis PlanningBernard SchutzAEI
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Data analysis for LISA enabling technology not
ready

• LISA science requirements are to detect
  gravitational waves of certain kinds. Cannot be
  done without significant processing of data after
  it arrives on the ground. So DA is a project
  requirement.
• Experience with ground-based GW DA forms basis of
  expectations of sensitivity, but real situation
  quite different for LISA.
• LISA observations have high SNR (typically),
  which means that source confusion may be
  principal noise across most of LISA band.
  Requires simultaneous recognition and subtraction
  of multiple overlapping strong signals. We do not
  yet know how to do this.
• Quality with which this can be done affects all
  measurement accuracies, even the ability to
  detect the weaker signals.
• LISA DA system must be unified, iterative,
  recursive. Ground-based model (separate teams
  working on different sources) not suitable.
• Has implications for data rights, data product
  releases, etc.

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Problems yet to be solved

• Simultaneous removal of strong signals, effect on
  accuracy of various parameter determinations.
  Removal not trivial when signals come from
  different families.
• EMRI signals have complex structure. Relativity
  problem not yet solved, so we dont have full
  waveform predictions. Work also needed on doing
  searches over the parameter space.
• Black hole merger waveforms cannot be reliably
  predicted.
• Optimal use of TDI data streams (best antenna
  pattern, phase-delay pointing at higher
  frequencies, consistency at all frequencies).
  Must be studied for each kind of source.
• Real-world problems handling gaps, estimating
  instrumental noise, coping with sensitivity
  degradation, specifying sciencekeeping data need.
• Prepare data analysis methods for degraded
  missions (loss of an arm).
• Source refinements filters for interacting WD
  binaries, filters for merged BH ringdown,
  waveforms from intermediate-mass-ratio BH systems
  (say, 1001 or 5001), exotic sources like
  cosmic strings.
• Standard data architecture tasks quality
  assurance, testing, data synthesizers, estimate
  computing resources required, decide on data
  products, data pipeline, communication paths
  among data analysis/acquisition/operations teams.

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European structures

• Data analysis normally NOT part of ESA activity
  on missions left to PIs, supported by national
  agencies of member states.
• Have been exceptions, including (now) LISA.
• Oliver issued call for Letters of Intent (LoI),
  members of community volunteering to work on
  problem. 14 responses (7 from national groups),
  involving over 100 potential contributors.
• We hope that ESA will issue invitation to tender
  for coordination contract, to be settled this
  year.
• Reports to Oliver, performs management and
  coordination of design activity (solving problems
  on previous slide).
• Coordinates the respondents who sent in LoIs.
• Will work closely with the US effort and the LIST
  to ensure complete coverage of outstanding issues
  and unified goals and architecture.
• Make recommendations on Science Management Plan.

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Expected role of coordinating center

• Agree tasks/milestones/deliverables/timescales
  with LoI participants. Follow these up, maintain
  schedules.
• Define and supervise testing of algorithms,
  prototype software.
• Provide and maintain infrastructure databases,
  document control, file formats, APIs, some
  computer resources,
• Organize large (coordination) meetings and
  smaller workshops on specific tasks.
• Ensure each critical task has a backup in case
  principal provider runs into problems.
• Maintain liaison and coordination with JPL and
  LIST.
• Provide design of data analysis architecture,
  advise on Data Management Plan.

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Immediate problems affecting hardware design

• As part of preliminary ESA discussions, we drew
  up a planning document (tasks, interdependencies,
  deadlines). Here are the near-term urgent tasks
  we came up with that are relevant to hardware
  design
• Gaps!
• Protected observing periods.
• Tests during commissioning, including hardware
  signal injection.
• Specify instrumental noise stationarity
  requirements.
• Sciencekeeping data specification.
• How to estimate instrumental noise, which may be
  buried by signal over most of the band.
• Designing for degraded mission ensure available
  science can still be extracted from data.