UMICH-2015: Simulations & Data Analysis Break-Out Session 2

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Forecasting for the S4 Science Book

What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?


What are the right inputs to the simulations?

  • Sky model
    • CMB:
      • scalar, tensor, non-Gaussian
    • Foregrounds:
      • components
    • Self-consistency
  • Mission model
    • Instrument:
      • beams, band-passes, noise PSDs
      • detector electronics, data acquisition
    • Observation:
      • scanning strategy, flags
      • atmosphere, ground pickup

What are the required output(s) of the simulations?

  • Maps
  • Covariances

What are the required analysis outputs/metrics/figures of merit?

  • Power spectra
  • Parameters
  • Uncertainties

What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?

  • Spectral domain
  • Map domain
  • Time domain

What resources are available over the coming year?

  • People
  • Codes
  • Cycles & storage

Forecasting using simulations with varying balance between complexity and feasibility?


Right balance issue: Large scale simulations with full complexity require long investment and significant resources. How can we go beyond very basic forecasting that neglects foregrounds and systematics and defines an instrument with only a beam size, a sky coverage, and a sensitivity, without implementing the full sims? How many levels of complexity should we consider, e.g.

  • basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction
  • moderate: inhomogeneous noise with non-stationarity and low-frequency excess
  • advanced: model of atmospheric noise based on map differences from existing data on patches or on model; scanning and filtering + map-making for detector sets, scaling-up as a function of number of detectors; simple foreground subtraction with, e.g. decorrelation, NILC, or simple model fitting per pixel
  • full: as realistic as possible

A few additional ideas:

  • 1/ell noise: usually experiments do not perform as well on large scales as extrapolated from noise rms. Can we work-out an empirical law, possibly based on existing experiments?
  • foregrounds: separate polarization issues (for primordial and lensing B-modes mostly) from temperature issues (on small scales, for SZ and extragalactic astrophysics). Should we worry about EE and TE foregrounds for parameter estimations?
  • to what extend can we consider that we have a trustable model of the level of foreground residuals (to treat them as a noise in forecasting)?
  • for cluster science we probably should consider the issues of
    • relativistic corrections
    • point source contamination in clusters

Previous Forecasting plots from S4 Snowmass

S4 Snowmass Inflation white paper (1309.5381) forecast plot:

S4 snowmass inflation 1309.5381 p12.png

How do we want the S4 science book forecast to do better?

- Foreground treatment

- Delensing treatment

- Systematics treatment

- alternative B-mode FOM's? (min detectable r, n_t, etc.)


S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:

S4 snowmass neutrinos 1309.5383 p11.png

How do we want the S4 science book forecast to do better?



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