Harvard-2017:T3

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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: ]]

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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)

File:Foregrounds.pdf

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)

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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.

Next-steps

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

Alex

• 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