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Parallel Session T3: Sim WG: sky modeling, component separation and lensing reconstruction(Chair: Blake Sherwin) [Jefferson 256]

Summary: File:S4Review.pdf [Session summary]

See draft schedule below. (post talks here)

  • [[File: ]]


Part 1 (20 mins) - Sky Modeling and Component Separation (moderated by Raphael, Blake may help with discussion)

- Update on recent work in sky modeling / component separation (~10 mins, Raphael)

- Discussion: open questions and (in particular) what are the next steps? (~10 mins, Raphael)


Part 2 (20 mins) - Lensing (moderated by Blake)

Short updates / talks about recent work with lots of discussion:

- auto / delensing foreground bias estimation (5 mins slides + 3 mins discussion, Alex) File:S4 lensing foregrounds alex aug2014.key.pdf

- small scale frequency cleaning / optimization (2+2 mins, Colin H.) [PDF]

- simulating delensing pipelines / delensing templates (brief update 2+2 mins, Kyle) [PDF]

- Discussion: what should we work on next? (4 mins on future work, Blake)


Some suggested discussion topics:

- All subjects: what are next steps for the group in i) sky modeling ii) component separation iii) lensing bias estimation iv) high-ell foreground cleaning and optimization v) delensing simulation

- FG modeling / cleaning: would realistic levels of decorrelation (or other effects) substantially change the optimization? What is enough complexity in foreground modeling? Can we get more data?

- Lensing, biases: what is the current take-home, how can we converge?

- Lensing cleaning, optimization: is current cleaning too conservative? How can we decide on frequency requirements?

- Lensing, delensing sims: issues and challenges, quadratic and max like?

- All topics (discussion at end): For next steps, what are our highest priorities?

Notes from session

Note-taker: Jason Henning

Notes for Foreground section:

  • Foregrounds dominate everywhere by orders of magnitude
  • Model YY=00
    • (not physical) model to validate Fisher forecasts in Science book with dust and sync with levels set by BICEP patch.
    • Multi-realization.
    • Gaussian realizations.
  • Model YY=01, 02, 03
    • contain instrument noise
    • limited resolution
    • assumed spectral dependence
    • (Different flavors of PySM)
      • differ by freq dependence of dust and sync, and AME
  • All models lead to small foreground residuals for S4 strawperson design
    • ~ order of magnitude under signal.
      • Partly due to high correlation between freq.
    • ILC prescription for foreground removal.
      • BICEP hasn’t achieved same residuals as Raphael.
      • Raphael’s model just requires maps to be properly calibrated to T_cmb.
  • Model YY=04
    • Ghosh, et al 1611.02418
    • based on GASS HI data
    • designed for larger decorrelation between freq.
  • Model YY=05
    • toy model with even more decorrelation
  • Models Y=04,05 lea to increase in sigma(r) of ~ 2 and bias for Raphael
      • Bias is ~ size of signal we’re trying to reconstruct.
    • Discussion about how much decorrelation we need to account for and how to model it.
      • Victor’s framework still wants large freq separation.
  • Model YY=06
    • Based on MHD
    • assumes constant dust-to-gas ratio
    • assume energy spectrum of electrons for synchrotron.
    • Reproduces E/B ratios.
    • Limited to small patches.
      • CP and RF disagree about how much this model would over-estimate correlation between dust and synchrotron at high Galactic latitude.
    • Resolution not good enough for use with lensing as well (sims don’t resolve turbulence).
    • Two sources of decorrelation
      • spatial variation of spectral index allowed by Planck.
      • line-of-sight effects
      • decorrelation between freq in dust is small.
    • What is the expected level of decorrelation???
    • We need sims to understand how we address decorrelation!!
    • We need models that address degree scales and needs of delensing survey simultaneously.
    • No non-forced sims have been able to show large decorrelation.


  • Add decorrelation into sims, more data, integrate high/low-ell sims.
  • Test closely-spaced freqs


  • NG-Foregrounds
    • Models for small-scale pol dust
      • Each gives different amount of BB power (while TT is tight).
    • Dust causes NG in lensing map.
      • So far no bias to lensing auto-spectra for the best 5% of sky for Vansyngel+ sims
      • 40% of sky there’s 1% bias in TT lensing, but still nothing in polarization. Comforting, but only as good as the sims.
    • Planck FFP8 sims (Challinor, Allison++2017)
      • Big bias (17% in TT, 62% in EBxEB)
      • Can downweight dusty areas and reduce bias in A_L from EBxEB (TT bias still there).
      • How does NG of dust impact delensing?
      • No average bias, but a lot of patch-patch scatter.
  • Takeways:
    • TT is biased from dust.
    • Polarization has bias that can be removed with downweighting (assuming uniform power over patches).
    • No evidence that we do not need dust and synch channels for high-ell survey.
    • Can we extend this to multi-freq?
    • How much do we trust freq decoherence in sims?

Colin Hill (Component Separation)

  • Simulated T maps
    • Missing small-scale Galac synch and AME.
    • All components properly correlated.
  • Simulated P maos
  • Constrained ICL
    • specifically project out whatever components you want.
    • Variance in final map larger since dof used.
  • If foregrounds are lower than noise floor ILC won’t pick them out but they can still bias lensing.
  • CP: Why aren’t we using these sims for low-ell forecasts as well?

Kyle Story (Lensed B-mode template)

  • “B-truth”: the best we could ever do
    • Leads to 99.9% delensing.
  • Quadratic estimator achieves ~ 90% delensing.
  • Next steps
    • Run these templates through r-estimation.
    • Add non-idealities to these maps?
    • Incorporate multi-freq, cleaned maps into pipeline.
    • LK: Investigating map-based delensing algorithms beyond QE is high-priority.
    • Move onto ML estimators.
    • Marius Millea
      • Starting to look at beyond QE delensing (1708.06753)

Action items/Next steps

Summarize action items here