Difference between revisions of "UCSD-2019: Analysis/Pipeline Working Group: Low-ell BB"
From CMB-S4 wiki
Jump to navigationJump to search (→Notes) |
|||
Line 74: | Line 74: | ||
== Notes == | == Notes == | ||
+ | |||
+ | === Buza / Racine: CMB-S4 PGW Forecasting === | ||
+ | |||
+ | * Forecasting loop: achieved performance used to forecast CMB-S4 instrument -> generate map-based sims with sky model -> analyze to determine sigma(r) and compare to forecast | ||
+ | * 9 bands spanning 4 atmospheric windows. Using split-bands to guard against unknown foreground complications. (20 GHz channel is on LAT to avoid huge beams) | ||
+ | * Calculate detector NETs for South Pole and Chile. NETs are only used to rescale BICEP/Keck achieved performance -- no ab initio sensitivity calculation. | ||
+ | * Forecast assumes sky model with dust and synchrotron. Foregrounds are allowed to decorrelate -- Fisher calculation assumes 3% dust decorrelation between 217 and 353 GHz. | ||
+ | * Choose distribution of effort across nine frequencies plus delensing (10 channels) to minimize sigma(r). | ||
+ | * Data challenge maps use forecasted noise levels but consider many different foreground models. Validates forecast and also determines bias due to different foreground models. | ||
+ | * Analyze data challenge maps with two pipelines: ILC and parametric likelihood foreground cleaning. | ||
+ | * For DSR, converted optimized distribution into discrete instrument configurations in consultation with SAT and detectors groups. Settled on "configuration 5" -> reference design. | ||
+ | * Using detailed sky coverage maps for Chile (deep and shallow) and Pole (deep or wide). Still rescaling from BICEP/Keck noise, but accounting for change in depth and bandpower degrees of freedom. Also applied a foreground mask, which eliminates some of the Chile coverage. Optionally apply foreground penalty -- assume that we can clean down to 1% of foreground, residual acts as bias. | ||
+ | ** End up with variation in sigma(r) as a function of number of SATs in Chile vs Pole. Results are different for r=0 vs r=0.003. | ||
+ | * Lloyd: We don't currently have optimal amount of delensing throughput. Want to know what delensing throughput is needed to hit measurement requirements. See Marius' figure from DSR but need to know what A_L level is threshold. | ||
+ | |||
+ | === Sherwin: SO BB pipeline / delensing === | ||
+ | |||
+ | * SO is very different limit from S4 -- more noise and more sky, so delensing is less important. SO sigma(r) increases by factor of 2 without delensing. | ||
+ | * Using linearized delensing B-mode template constructed on curved sky. Using Wiener filter to downweight noisy regions, but this isn't much better than just masking. Lensing template treated as virtual frequency band. | ||
+ | * SO in intermediate regime where delensing is needed, but noise isn't quite good enough for internal delensing. Also using LSS (CIB, galaxies) as lensing tracer. Multi-tracer delensing gives significant improvement over CIB or CMB internal alone. Think that they will get to ~70% delensing once LSST data is available. | ||
+ | * Running this in a pipeline. Sims include lensing and LSS. | ||
+ | * Think that LSS calibration will be ok because can do it via cross-spectra with noisy CMB lensing map. Worried about bias due to Galactic dust in CIB maps, but ran some sims and it looks ok. | ||
+ | * Is multi-tracer delensing useful as cross-check for CMB-S4? Very complicated, but could be helpful for wide survey. Important to marginalize over A_lens because lensing residual might be uncertain. |
Revision as of 16:46, 18 October 2019
Contents
Charge
- Identify key decisions that must be made (and justified) prior to CD-1,
- Make progress on (or actually make) those decisions,
- Lay out a timeline and process for making each decision, consistent with the post-decision work and internal reviews that will be needed to complete preparations for CD-1,
- Ensure that those timelines and processes are understood and supported by the collaboration, and that we (together) believe we can follow them.
Key Issues
- How do we optimize detector allocation across frequency in the face of uncertainty about foreground properties, for both the SATs and the delensing LAT?
- What are the advantages of split bands? How does performance compare to non-split bands?
- Do we need a 20 GHz channel on delensing LAT (for SAT science)
- How well will de-lensing work in practice?
- What are the necessary analysis tools to answer these questions?
What is CD-1?
Blatantly copied from Analysis/Pipeline Working Group: Maps to C_ell:
Background/clarifying questions:
- What does “by CD-1” mean, and what are the implications for when tools need to be in place and working?
- According to APC white paper (https://arxiv.org/abs/1908.01062), CD-1 is in Q3 of FY2021 (so June 2021?).
- But according to project office, "Plan [must be] finalized by start of 2020 for delivering...CD-1"
- Working backward from there, any tool that could reasonably influence a CD-1 decision needs to be in place and working by ... ?
- Give an example timeline for an example decision?
- According to APC white paper (https://arxiv.org/abs/1908.01062), CD-1 is in Q3 of FY2021 (so June 2021?).
Agenda
- Introduction by everyone in the room: who? where? what aspects of low-ell BB interest you? 5 minutes
- Recap of the plan for this session/CD-1 goals slides[Wu], 3 minutes
- Review of Fisher based S4 forecasting thus far leading to DSR appendix A [Ben/Victor/Raphael], 15 minutes
- Review of Map based S4 forecasting thus far and ideas for next steps Clem, 10 minutes
- Review of forecasting and simulations for Simons Observatory inflation science [Alonso / Errard / Sherwin], slides delensing slides 30 minutes
- Plans for working group [all], ~60 minutes
- Forecasting
- Quantitative comparison of SO and CMB-S4 DSR forecasts
- Optimization of delensing effort: update for DSR sensitivity, motivation for frequency coverage, feedback from real delensing efforts
- Data Challenge simulations
- More / better foreground models
- Instrumental systematics
- Other map non-idealities, i.e. filtering / mode loss
- Coordination with technical groups on instrument configuration, etc
- What else needs to be demonstrated for CD1?
- Bottlenecks for sim production
- Analysis of Data Challenges
- Who plans to participate? How do we get more participation?
- Delensing
- Specific Data Challenge plan / timeline
- Experiment config 06: DSR configuration, in progress
- Update / reoptimization of delensing survey?
- Inclusion of instrumental systematics -- which ones? how to include?
- Data Challenges coming out of Data Management group
- Forecasting
Remote attendance
Join Zoom Meeting https://zoom.us/j/340796462?pwd=S1o3Q3NQUlFOWkVVUXVrV1laK0JrQT09
- Meeting ID: 340 796 462
- Password: 654876
- One tap mobile
- +14086380968,,340796462# US (San Jose)
- +16465588656,,340796462# US (New York)
- Dial by your location
- +1 408 638 0968 US (San Jose)
- +1 646 558 8656 US (New York)
- Meeting ID: 340 796 462
- Find your local number: https://zoom.us/u/aeGN6VEA3T
Notes
Buza / Racine: CMB-S4 PGW Forecasting
- Forecasting loop: achieved performance used to forecast CMB-S4 instrument -> generate map-based sims with sky model -> analyze to determine sigma(r) and compare to forecast
- 9 bands spanning 4 atmospheric windows. Using split-bands to guard against unknown foreground complications. (20 GHz channel is on LAT to avoid huge beams)
- Calculate detector NETs for South Pole and Chile. NETs are only used to rescale BICEP/Keck achieved performance -- no ab initio sensitivity calculation.
- Forecast assumes sky model with dust and synchrotron. Foregrounds are allowed to decorrelate -- Fisher calculation assumes 3% dust decorrelation between 217 and 353 GHz.
- Choose distribution of effort across nine frequencies plus delensing (10 channels) to minimize sigma(r).
- Data challenge maps use forecasted noise levels but consider many different foreground models. Validates forecast and also determines bias due to different foreground models.
- Analyze data challenge maps with two pipelines: ILC and parametric likelihood foreground cleaning.
- For DSR, converted optimized distribution into discrete instrument configurations in consultation with SAT and detectors groups. Settled on "configuration 5" -> reference design.
- Using detailed sky coverage maps for Chile (deep and shallow) and Pole (deep or wide). Still rescaling from BICEP/Keck noise, but accounting for change in depth and bandpower degrees of freedom. Also applied a foreground mask, which eliminates some of the Chile coverage. Optionally apply foreground penalty -- assume that we can clean down to 1% of foreground, residual acts as bias.
- End up with variation in sigma(r) as a function of number of SATs in Chile vs Pole. Results are different for r=0 vs r=0.003.
- Lloyd: We don't currently have optimal amount of delensing throughput. Want to know what delensing throughput is needed to hit measurement requirements. See Marius' figure from DSR but need to know what A_L level is threshold.
Sherwin: SO BB pipeline / delensing
- SO is very different limit from S4 -- more noise and more sky, so delensing is less important. SO sigma(r) increases by factor of 2 without delensing.
- Using linearized delensing B-mode template constructed on curved sky. Using Wiener filter to downweight noisy regions, but this isn't much better than just masking. Lensing template treated as virtual frequency band.
- SO in intermediate regime where delensing is needed, but noise isn't quite good enough for internal delensing. Also using LSS (CIB, galaxies) as lensing tracer. Multi-tracer delensing gives significant improvement over CIB or CMB internal alone. Think that they will get to ~70% delensing once LSST data is available.
- Running this in a pipeline. Sims include lensing and LSS.
- Think that LSS calibration will be ok because can do it via cross-spectra with noisy CMB lensing map. Worried about bias due to Galactic dust in CIB maps, but ran some sims and it looks ok.
- Is multi-tracer delensing useful as cross-check for CMB-S4? Very complicated, but could be helpful for wide survey. Important to marginalize over A_lens because lensing residual might be uncertain.