UMICH-2015: Simulations & Data Analysis Break-Out Session 1

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What needs to be in the S&DA section of the CMB-S4 Science Book?

For each mission phase (design, construction, deployment, observation):

  • What are the S&DA goals?
  • What (human, compute) resources are required? available?

Given this:

  • Where does the balance lie between veracity and tractability?
  • How do we ensure that our progress through phases is evolutionary not revolutionary?

Please add to the italic-headed lists of resources and their points of contact below



A hierarchy of methods of decreasing veracity/increasing tractability.

Simulations used for:

  • Forecasting/Design
    • sample mission model space for given sky model(s)
    • requires explicit metric(s), eg. Science/$
  • Validation & verification
    • representative & self-consistent mission realization(s)
    • bootstrapping V&V of both data analysis and simulation codes
  • Debiasing & uncertainty quantification
    • accuracy & statistics
    • dominant computational cost

For each simulation element (sky simulation, data simulation):

  • What is the current status?
  • What is required in each mission phase?
  • What are the challenges to meeting these requirements?

Sky Simulation Tools/POC

  • PSM (Delabrouille): IDL code generating 10-component foreground model
  • S4SM?
  • Hydrodynamical sims

Data Simulation Tools/POC

  • PSM (Delabrouille): IDL code applying detector band-passes to sky model; pyPSM python re-implementation in progress as precursor to web interface
  • TOAST (Kisner): C++ MPI/OpenMP tools for massively parallel TOD manipulation, including OTFS of colored correlated noise timelines; pyTOAST python re-implementation in progress including OTFS of full-beam convolved sky timelines & diskless interface with pre-processing
  • CONVIQT/libCONVIQT (Prezeau/Keskitalo): full-beam convolution in the time domain
  • FEBeCoP (Rocha): effective-beam convolution in the pixel domain
  • Fisher matrix methods

Data Analysis


How do we combine data from multiple telescopes of multiple classes at multiple sites?

What do we do about data covariance (functional forms, approximate matrices, Monte Carlos, other)?

For each data analysis element (mission characterization, pre-processing, map-making, component separation, power spectrum estimation, parameter estimation, ... ):

  • What is the current status?
  • What is required in each mission phase?
  • What are the challenges to meeting these requirements?

Map Making Tools/POC

  • MADAM/TOAST (Keskitalo): Fortran MPI/OpenMP destriper with TOAST OTFS & Monte Carlo capabilities.
  • Springtide (Ashdown): Fortran MPI/OpenMP destriper with TOAST OTFS & Monte Carlo capabilities.

Component Separation Tools/POC

Power Spectrum Estimators/POC

Parameter Estimators/POC

Data Challenge & Computational Resources

Cmb hpc scaling.jpg

Suborbital data growth exactly tracks Moore's Law; satellite data grow at half the rate.

Beware of committing to compression (lossy, outdated)

Computational Resources/POC

  • Currently available
    • NERSC (Borrill): DOE general purpose HPC center with new top-10 system every 3 years; 1% of cycles annually for 20 years, accounts for anyone, public data repository,
  • Future potential
    • ALCF: DOE leadership HPC center with a new top-5 system every 3 years; limited number of users & 'heroic' computations, next-generation architecture in common with NERSC
    • NSC: Chinese HPC center with world #1 system; limited number of Chinese-led users & 'heroic' computations


  • Dedicated sky modeling activity!
    • Clean
    • Self-consistent
    • Usable
  • Pipelines & interfaces
    • Tightly-coupled pipelining where I/O is prohibitive (time-domain) - interface in memory.


    • Loosely-coupled pipelining where I/O is reasonable (pixel-, multipole-, parameter-domains) - interface on disk.


    • Support for both rapid prototyping and efficient production
  • Standard data objects & formats
    • Define file and memory formats for interfacing, informed by computational efficiency.
    • Generalized mission model
  • Data distribution
    • Take-out vs Eat-in
  • Synergies with S3, LiteBIRD, COrE+, etc
    • Common sky models
    • Parallel pipelines (V&V, prototyping, general vs specific)

Secondary anisotropies - lensing, tSZ, kSZ, CIB, cross-correlations, point sources, etc.

Fast mocks

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