https://cmb-s4.uchicago.edu/wiki/api.php?action=feedcontributions&user=Jacques&feedformat=atomCMB-S4 wiki - User contributions [en]2022-01-22T00:21:22ZUser contributionsMediaWiki 1.34.2https://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=1047UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T13:25:28Z<p>Jacques: /* Sky model for simulations and forecasting */</p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian, (cosmic strings?)<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
** discussion:<br />
* conservative regarding systematics<br />
** discussion:<br />
* conservative regarding foreground complexity<br />
** discussion:<br />
<br />
- What drives need for simplicity, transparency, flexibility?<br />
* Forecasting <--> Survey design feedback loop needs to be fast as we explore tradeoffs for different science drivers<br />
* Need flexibility to separately explore large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* Multiple parallel forecasting efforts should coordinate inputs/outputs to<br />
** understand where differences are due to input assumptions vs. methods.<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
- Role of Data Challenges<br />
* not most efficient tool for exploring tradeoffs<br />
* at specific points in survey definition space, can validate Fisher forecasts<br />
* common input sims, what is needed:<br />
** foreground maps<br />
** CMB maps<br />
** noise / systematics maps<br />
** encoding experiment filters: reobs matrices?<br />
* separate challengers / challengees?<br />
<br />
- How to parameterize S4 survey specifications<br />
** discussion:<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
** discussion:<br />
<br />
<br />
----<br />
<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* How can we get a "trustable" model of the level of foreground residuals (to treat them as a noise in forecasting)?<br />
<br />
<br />
----<br />
<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model (PSM). This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
A description of a early version of the Planck Sky model can be found in Delabrouille, Betoule, Melin, et al. (2013), "The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths", A&A; 553, 96 (http://adsabs.harvard.edu/abs/2013A%26A...553A..96D). See also the PSM web page: http://www.apc.univ-paris7.fr/~delabrou/PSM/psm.html<br />
<br />
The PSM has been used to generate simulations for Planck described in Planck 2015 results. XII. Full Focal Plane simulations (http://adsabs.harvard.edu/abs/2015arXiv150906348P).<br />
<br />
Here is a short overview of the components in the model, their main limitations, and plans to fix those limitations. Suggestions and prioritization would be useful.<br />
<br />
*Galactic emission: Low frequency model is still based on WMAP + Haslam 408 MHz maps. Dust based on Planck for the current version used to make Planck FFP8 maps (not public yet). Low frequency foregrounds should be updated on the basis of recent Planck analysis (http://adsabs.harvard.edu/abs/2015arXiv150606660P). All maps are limited by the limited angular resolution of the input observations.<br />
**Synchrotron: based on WMAP + Haslam 408 MHz maps, polarization from a large scale model of the galactic magnetic field and of depolarization. Complemented by random fluctuations on small scales. <br />
**Free-free: Should be de-noised at high galactic latitude. Assumed unpolarized for the moment.<br />
**CO: based on ground based observations (Dame et al.), only part-sky. Assumed unpolarized for the moment.<br />
** Spinning dust: Should be de-noised, frequency dependence of the emission should be pixel-dependent. Assumed unpolarized for the moment.<br />
** Dust: Temperature and Polarization based on Planck HFI (mostly 353 GHz). Several options for scaling with frequency exist.<br />
** Other lines than CO are missing<br />
*Clusters: based on number counts + random distribution on the sky, with relativistic corrections, scaling laws to connect mass to Y. Thermal SZ, kinetic SZ (with random directions), polarized SZ effects are implemented. Missing: contamination by radio and IR sources, correlation with CIB and with lensing.<br />
*CIB (unresolved background of (mostly high redshift) dusty galaxies<br />
*Radio sources<br />
*Local IR galaxies<br />
<br />
Some questions<br />
* How do we go beyond very simple models (e.g. one single power law synchrotron per pixel, one single dust greybody, ...). What is the evidence that we should go beyond such simple models?<br />
* Should we make simulations with (nasty) surprises, e.g. 0.1 to 1% polarization for spinning dust, CO, (free-free?)<br />
* Should we do blind analyses of simulations at this stage ?<br />
* What complementary data do we envisage (should we work on CMB-S4 only, or CMB-S4 + LiteBIRD, COrE+, PIXIE, X-rays, other?)<br />
* should we try to put in the simulated sky signatures of all the effects we will look for in real S4 data (prioritize, set requirements on accuracy of modeling)<br />
* alternatives to the PSM?<br />
<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
See also http://lanl.arxiv.org/pdf/1402.4108.pdf Wu, Errard, Dvorkin, Lee, McDonald, Slosar, Zahn.<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=1046UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T13:22:19Z<p>Jacques: /* Forecasting using simulations: complexity vs. feasibility? */</p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian, (cosmic strings?)<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
** discussion:<br />
* conservative regarding systematics<br />
** discussion:<br />
* conservative regarding foreground complexity<br />
** discussion:<br />
<br />
- What drives need for simplicity, transparency, flexibility?<br />
* Forecasting <--> Survey design feedback loop needs to be fast as we explore tradeoffs for different science drivers<br />
* Need flexibility to separately explore large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* Multiple parallel forecasting efforts should coordinate inputs/outputs to<br />
** understand where differences are due to input assumptions vs. methods.<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
- Role of Data Challenges<br />
* not most efficient tool for exploring tradeoffs<br />
* at specific points in survey definition space, can validate Fisher forecasts<br />
* common input sims, what is needed:<br />
** foreground maps<br />
** CMB maps<br />
** noise / systematics maps<br />
** encoding experiment filters: reobs matrices?<br />
* separate challengers / challengees?<br />
<br />
- How to parameterize S4 survey specifications<br />
** discussion:<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
** discussion:<br />
<br />
<br />
----<br />
<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* How can we get a "trustable" model of the level of foreground residuals (to treat them as a noise in forecasting)?<br />
<br />
<br />
----<br />
<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model (PSM). This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
A description of a early version of the Planck Sky model can be found in Delabrouille, Betoule, Melin, et al. (2013), "The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths", A&A; 553, 96 (http://adsabs.harvard.edu/abs/2013A%26A...553A..96D). See also the PSM web page: http://www.apc.univ-paris7.fr/~delabrou/PSM/psm.html<br />
<br />
The PSM has been used to generate simulations for Planck described in Planck 2015 results. XII. Full Focal Plane simulations (http://adsabs.harvard.edu/abs/2015arXiv150906348P).<br />
<br />
Here is a short overview of the components in the model, their main limitations, and plans to fix those limitations. Suggestions and prioritization would be useful.<br />
<br />
*Galactic emission: Low frequency model is still based on WMAP + Haslam 408 MHz maps. Dust based on Planck for the current version used to make Planck FFP8 maps (not public yet). Low frequency foregrounds should be updated on the basis of recent Planck analysis (http://adsabs.harvard.edu/abs/2015arXiv150606660P). All maps are limited by the limited angular resolution of the input observations.<br />
**Synchrotron: based on WMAP + Haslam 408 MHz maps, polarization from a large scale model of the galactic magnetic field and of depolarization. Complemented by random fluctuations on small scales. <br />
**Free-free: Should be de-noised at high galactic latitude. Assumed unpolarized for the moment.<br />
**CO: based on ground based observations (Dame et al.), only part-sky. Assumed unpolarized for the moment.<br />
** Spinning dust: Should be de-noised, frequency dependence of the emission should be pixel-dependent. Assumed unpolarized for the moment.<br />
** Dust: Temperature and Polarization based on Planck HFI (mostly 353 GHz). Several options for scaling with frequency exist.<br />
** Other lines than CO are missing<br />
*Clusters: based on number counts + random distribution on the sky, with relativistic corrections, scaling laws to connect mass to Y. Thermal SZ, kinetic SZ (with random directions), polarized SZ effects are implemented. Missing: contamination by radio and IR sources, correlation with CIB and with lensing.<br />
*CIB (unresolved background of (mostly high redshift) dusty galaxies<br />
*Radio sources<br />
*Local IR galaxies<br />
<br />
Some questions<br />
* How do we go beyond very simple models (e.g. one single power law synchrotron per pixel, one single dust greybody, ...). What is the evidence that we should go beyond such simple models?<br />
* Should we make simulations with (nasty) surprises, e.g. 0.1 to 1% polarization for spinning dust, CO, (free-free?)<br />
* Should we do blind analyses of simulations at this stage ?<br />
* What complementary data do we envisage (should we work on CMB-S4 only, or CMB-S4 + LiteBIRD, COrE+, X-rays, other?)<br />
* should we try to put in the simulated sky signatures of all the effects we will look for in real S4 data (prioritize, set requirements on accuracy of modeling)<br />
* alternatives to the PSM?<br />
<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
See also http://lanl.arxiv.org/pdf/1402.4108.pdf Wu, Errard, Dvorkin, Lee, McDonald, Slosar, Zahn.<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=1045UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T13:20:49Z<p>Jacques: /* Forecasting using simulations: complexity vs. feasibility? */</p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian, (cosmic strings?)<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
** discussion:<br />
* conservative regarding systematics<br />
** discussion:<br />
* conservative regarding foreground complexity<br />
** discussion:<br />
<br />
- What drives need for simplicity, transparency, flexibility?<br />
* Forecasting <--> Survey design feedback loop needs to be fast as we explore tradeoffs for different science drivers<br />
* Need flexibility to separately explore large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* Multiple parallel forecasting efforts should coordinate inputs/outputs to<br />
** understand where differences are due to input assumptions vs. methods.<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
- Role of Data Challenges<br />
* not most efficient tool for exploring tradeoffs<br />
* at specific points in survey definition space, can validate Fisher forecasts<br />
* common input sims, what is needed:<br />
** foreground maps<br />
** CMB maps<br />
** noise / systematics maps<br />
** encoding experiment filters: reobs matrices?<br />
* separate challengers / challengees?<br />
<br />
- How to parameterize S4 survey specifications<br />
** discussion:<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
** discussion:<br />
<br />
<br />
----<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* How can we get a "trustable" model of the level of foreground residuals (to treat them as a noise in forecasting)?<br />
<br />
----<br />
<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model (PSM). This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
A description of a early version of the Planck Sky model can be found in Delabrouille, Betoule, Melin, et al. (2013), "The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths", A&A; 553, 96 (http://adsabs.harvard.edu/abs/2013A%26A...553A..96D). See also the PSM web page: http://www.apc.univ-paris7.fr/~delabrou/PSM/psm.html<br />
<br />
The PSM has been used to generate simulations for Planck described in Planck 2015 results. XII. Full Focal Plane simulations (http://adsabs.harvard.edu/abs/2015arXiv150906348P).<br />
<br />
Here is a short overview of the components in the model, their main limitations, and plans to fix those limitations. Suggestions and prioritization would be useful.<br />
<br />
*Galactic emission: Low frequency model is still based on WMAP + Haslam 408 MHz maps. Dust based on Planck for the current version used to make Planck FFP8 maps (not public yet). Low frequency foregrounds should be updated on the basis of recent Planck analysis (http://adsabs.harvard.edu/abs/2015arXiv150606660P). All maps are limited by the limited angular resolution of the input observations.<br />
**Synchrotron: based on WMAP + Haslam 408 MHz maps, polarization from a large scale model of the galactic magnetic field and of depolarization. Complemented by random fluctuations on small scales. <br />
**Free-free: Should be de-noised at high galactic latitude. Assumed unpolarized for the moment.<br />
**CO: based on ground based observations (Dame et al.), only part-sky. Assumed unpolarized for the moment.<br />
** Spinning dust: Should be de-noised, frequency dependence of the emission should be pixel-dependent. Assumed unpolarized for the moment.<br />
** Dust: Temperature and Polarization based on Planck HFI (mostly 353 GHz). Several options for scaling with frequency exist.<br />
** Other lines than CO are missing<br />
*Clusters: based on number counts + random distribution on the sky, with relativistic corrections, scaling laws to connect mass to Y. Thermal SZ, kinetic SZ (with random directions), polarized SZ effects are implemented. Missing: contamination by radio and IR sources, correlation with CIB and with lensing.<br />
*CIB (unresolved background of (mostly high redshift) dusty galaxies<br />
*Radio sources<br />
*Local IR galaxies<br />
<br />
Some questions<br />
* How do we go beyond very simple models (e.g. one single power law synchrotron per pixel, one single dust greybody, ...). What is the evidence that we should go beyond such simple models?<br />
* Should we make simulations with (nasty) surprises, e.g. 0.1 to 1% polarization for spinning dust, CO, (free-free?)<br />
* Should we do blind analyses of simulations at this stage ?<br />
* What complementary data do we envisage (should we work on CMB-S4 only, or CMB-S4 + LiteBIRD, COrE+, X-rays, other?)<br />
* should we try to put in the simulated sky signatures of all the effects we will look for in real S4 data (prioritize, set requirements on accuracy of modeling)<br />
* alternatives to the PSM?<br />
<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
See also http://lanl.arxiv.org/pdf/1402.4108.pdf Wu, Errard, Dvorkin, Lee, McDonald, Slosar, Zahn.<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=1043UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T13:18:49Z<p>Jacques: </p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian, (cosmic strings?)<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
<br />
----<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* 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)?<br />
* for cluster science we probably should consider the issues of<br />
** relativistic corrections<br />
** point source contamination in clusters<br />
<br />
----<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model (PSM). This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
A description of a early version of the Planck Sky model can be found in Delabrouille, Betoule, Melin, et al. (2013), "The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths", A&A; 553, 96 (http://adsabs.harvard.edu/abs/2013A%26A...553A..96D). See also the PSM web page: http://www.apc.univ-paris7.fr/~delabrou/PSM/psm.html<br />
<br />
The PSM has been used to generate simulations for Planck described in Planck 2015 results. XII. Full Focal Plane simulations (http://adsabs.harvard.edu/abs/2015arXiv150906348P).<br />
<br />
Here is a short overview of the components in the model, their main limitations, and plans to fix those limitations. Suggestions and prioritization would be useful.<br />
<br />
*Galactic emission: Low frequency model is still based on WMAP + Haslam 408 MHz maps. Dust based on Planck for the current version used to make Planck FFP8 maps (not public yet). Low frequency foregrounds should be updated on the basis of recent Planck analysis (http://adsabs.harvard.edu/abs/2015arXiv150606660P). All maps are limited by the limited angular resolution of the input observations.<br />
**Synchrotron: based on WMAP + Haslam 408 MHz maps, polarization from a large scale model of the galactic magnetic field and of depolarization. Complemented by random fluctuations on small scales. <br />
**Free-free: Should be de-noised at high galactic latitude. Assumed unpolarized for the moment.<br />
**CO: based on ground based observations (Dame et al.), only part-sky. Assumed unpolarized for the moment.<br />
** Spinning dust: Should be de-noised, frequency dependence of the emission should be pixel-dependent. Assumed unpolarized for the moment.<br />
** Dust: Temperature and Polarization based on Planck HFI (mostly 353 GHz). Several options for scaling with frequency exist.<br />
** Other lines than CO are missing<br />
*Clusters: based on number counts + random distribution on the sky, with relativistic corrections, scaling laws to connect mass to Y. Thermal SZ, kinetic SZ (with random directions), polarized SZ effects are implemented. Missing: contamination by radio and IR sources, correlation with CIB and with lensing.<br />
*CIB (unresolved background of (mostly high redshift) dusty galaxies<br />
*Radio sources<br />
*Local IR galaxies<br />
<br />
Some questions<br />
* How do we go beyond very simple models (e.g. one single power law synchrotron per pixel, one single dust greybody, ...). What is the evidence that we should go beyond such simple models?<br />
* Should we make simulations with (nasty) surprises, e.g. 0.1 to 1% polarization for spinning dust, CO, (free-free?)<br />
* Should we do blind analyses of simulations at this stage ?<br />
* What complementary data do we envisage (should we work on CMB-S4 only, or CMB-S4 + LiteBIRD, COrE+, X-rays, other?)<br />
* should we try to put in the simulated sky signatures of all the effects we will look for in real S4 data (prioritize, set requirements on accuracy of modeling)<br />
* alternatives to the PSM?<br />
<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
** discussion:<br />
* conservative regarding systematics<br />
** discussion:<br />
* conservative regarding foreground complexity<br />
** discussion:<br />
<br />
- What drives need for simplicity, transparency, flexibility?<br />
* Forecasting <--> Survey design feedback loop needs to be fast as we explore tradeoffs for different science drivers<br />
* Need flexibility to separately explore large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* Multiple parallel forecasting efforts should coordinate inputs/outputs to<br />
** understand where differences are due to input assumptions vs. methods.<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
- Role of Data Challenges<br />
* not most efficient tool for exploring tradeoffs<br />
* at specific points in survey definition space, can validate Fisher forecasts<br />
* common input sims, what is needed:<br />
** foreground maps<br />
** CMB maps<br />
** noise / systematics maps<br />
** encoding experiment filters: reobs matrices?<br />
* separate challengers / challengees?<br />
<br />
- How to parameterize S4 survey specifications<br />
** discussion:<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
** discussion:<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
See also http://lanl.arxiv.org/pdf/1402.4108.pdf Wu, Errard, Dvorkin, Lee, McDonald, Slosar, Zahn.<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=1017UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T12:42:19Z<p>Jacques: </p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
<br />
----<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* 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)?<br />
* for cluster science we probably should consider the issues of<br />
** relativistic corrections<br />
** point source contamination in clusters<br />
<br />
----<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model (PSM). This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
A description of a early version of the Planck Sky model can be found in Delabrouille, Betoule, Melin, et al. (2013), "The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths", A&A; 553, 96 (http://adsabs.harvard.edu/abs/2013A%26A...553A..96D). See also the PSM web page: http://www.apc.univ-paris7.fr/~delabrou/PSM/psm.html<br />
<br />
The PSM has been used to generate simulations for Planck described in Planck 2015 results. XII. Full Focal Plane simulations (http://adsabs.harvard.edu/abs/2015arXiv150906348P).<br />
<br />
Here is a short overview of the components in the model, their main limitations, and plans to fix those limitations. Suggestions and prioritization would be useful.<br />
<br />
*Galactic emission: Low frequency model is still based on WMAP + Haslam 408 MHz maps. Dust based on Planck for the current version used to make Planck FFP8 maps (not public yet). Low frequency foregrounds should be updated on the basis of recent Planck analysis (http://adsabs.harvard.edu/abs/2015arXiv150606660P). All maps are limited by the limited angular resolution of the input observations.<br />
**Synchrotron: based on WMAP + Haslam 408 MHz maps, polarization from a large scale model of the galactic magnetic field and of depolarization. Complemented by random fluctuations on small scales. <br />
**Free-free: Should be de-noised at high galactic latitude. Assumed unpolarized for the moment.<br />
**CO: based on ground based observations (Dame et al.), only part-sky. Assumed unpolarized for the moment.<br />
** Spinning dust: Should be de-noised, frequency dependence of the emission should be pixel-dependent. Assumed unpolarized for the moment.<br />
** Dust: Temperature and Polarization based on Planck HFI (mostly 353 GHz). Several options for scaling with frequency exist.<br />
** Other lines than CO are missing<br />
*Clusters: based on number counts + random distribution on the sky, with relativistic corrections, scaling laws to connect mass to Y. Thermal SZ, kinetic SZ (with random directions), polarized SZ effects are implemented. Missing: contamination by radio and IR sources, correlation with CIB and with lensing.<br />
*CIB (unresolved background of (mostly high redshift) dusty galaxies<br />
*Radio sources<br />
*Local IR galaxies<br />
<br />
Some questions<br />
* How do we go beyond very simple models (e.g. one single power law synchrotron per pixel, one single dust greybody, ...). What is the evidence that we should go beyond such simple models?<br />
* Should we make simulations with (nasty) surprises, e.g. 0.1 to 1% polarization for spinning dust, CO, (free-free?)<br />
* Should we do blind analyses of simulations at this stage ?<br />
* What complementary data do we envisage (should we work on CMB-S4 only, or CMB-S4 + LiteBIRD, COrE+, X-rays, other?)<br />
* should we try to put in the simulated sky signatures of all the effects we will look for in real S4 data (prioritize, set requirements on accuracy of modeling)<br />
* alternatives to the PSM?<br />
<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
** discussion:<br />
* conservative regarding systematics<br />
** discussion:<br />
* conservative regarding foreground complexity<br />
** discussion:<br />
<br />
- What drives need for simplicity, transparency, flexibility?<br />
* Forecasting <--> Survey design feedback loop needs to be fast as we explore tradeoffs for different science drivers<br />
* Need flexibility to separately explore large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* Multiple parallel forecasting efforts should coordinate inputs/outputs to<br />
** understand where differences are due to input assumptions vs. methods.<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
- Role of Data Challenges<br />
* not most efficient tool for exploring tradeoffs<br />
* at specific points in survey definition space, can validate Fisher forecasts<br />
* common input sims, what is needed:<br />
** foreground maps<br />
** CMB maps<br />
** noise / systematics maps<br />
** encoding experiment filters: reobs matrices?<br />
* separate challengers / challengees?<br />
<br />
- How to parameterize S4 survey specifications<br />
** discussion:<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
** discussion:<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
See also http://lanl.arxiv.org/pdf/1402.4108.pdf Wu, Errard, Dvorkin, Lee, McDonald, Slosar, Zahn.<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=1016UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T12:41:19Z<p>Jacques: </p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
<br />
----<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* 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)?<br />
* for cluster science we probably should consider the issues of<br />
** relativistic corrections<br />
** point source contamination in clusters<br />
<br />
----<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model (PSM). This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
A description of a early version of the Planck Sky model can be found in Delabrouille, Betoule, Melin, et al. (2013), "The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths", A&A; 553, 96 (http://adsabs.harvard.edu/abs/2013A%26A...553A..96D). See also the PSM web page: http://www.apc.univ-paris7.fr/~delabrou/PSM/psm.html<br />
<br />
The PSM has been used to generate simulations for Planck described in Planck 2015 results. XII. Full Focal Plane simulations (http://adsabs.harvard.edu/abs/2015arXiv150906348P).<br />
<br />
Here is a short overview of the components in the model, their main limitations, and plans to fix those limitations. Suggestions and prioritization would be useful.<br />
<br />
*Galactic emission: Low frequency model is still based on WMAP + Haslam 408 MHz maps. Dust based on Planck for the current version used to make Planck FFP8 maps (not public yet). Low frequency foregrounds should be updated on the basis of recent Planck analysis (http://adsabs.harvard.edu/abs/2015arXiv150606660P). All maps are limited by the limited angular resolution of the input observations.<br />
**Synchrotron: based on WMAP + Haslam 408 MHz maps, polarization from a large scale model of the galactic magnetic field and of depolarization. Complemented by random fluctuations on small scales. <br />
**Free-free: Should be de-noised at high galactic latitude. Assumed unpolarized for the moment.<br />
**CO: based on ground based observations (Dame et al.), only part-sky. Assumed unpolarized for the moment.<br />
** Spinning dust: Should be de-noised, frequency dependence of the emission should be pixel-dependent. Assumed unpolarized for the moment.<br />
** Dust: Temperature and Polarization based on Planck HFI (mostly 353 GHz). Several options for scaling with frequency exist.<br />
** Other lines than CO are missing<br />
*Clusters: based on number counts + random distribution on the sky, with relativistic corrections, scaling laws to connect mass to Y. Thermal SZ, kinetic SZ (with random directions), polarized SZ effects are implemented. Missing: contamination by radio and IR sources, correlation with CIB and with lensing.<br />
*CIB (unresolved background of (mostly high redshift) dusty galaxies<br />
*Radio sources<br />
*Local IR galaxies<br />
<br />
Some questions<br />
* How do we go beyond very simple models (e.g. one single power law synchrotron per pixel, one single dust greybody, ...). What is the evidence that we should go beyond such simple models?<br />
* Should we make simulations with (nasty) surprises, e.g. 0.1 to 1% polarization for spinning dust, CO, (free-free?)<br />
* Should we do blind analyses of simulations at this stage ?<br />
* What complementary data do we envisage (should we work on CMB-S4 only, or CMB-S4 + LiteBIRD, COrE+, X-rays, other?)<br />
* should we try to put in the simulated sky signatures of all the effects we will look for in real S4 data (prioritize, set requirements on accuracy of modeling)<br />
<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
** discussion:<br />
* conservative regarding systematics<br />
** discussion:<br />
* conservative regarding foreground complexity<br />
** discussion:<br />
<br />
- What drives need for simplicity, transparency, flexibility?<br />
* Forecasting <--> Survey design feedback loop needs to be fast as we explore tradeoffs for different science drivers<br />
* Need flexibility to separately explore large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* Multiple parallel forecasting efforts should coordinate inputs/outputs to<br />
** understand where differences are due to input assumptions vs. methods.<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
- Role of Data Challenges<br />
* not most efficient tool for exploring tradeoffs<br />
* at specific points in survey definition space, can validate Fisher forecasts<br />
* common input sims, what is needed:<br />
** foreground maps<br />
** CMB maps<br />
** noise / systematics maps<br />
** encoding experiment filters: reobs matrices?<br />
* separate challengers / challengees?<br />
<br />
- How to parameterize S4 survey specifications<br />
** discussion:<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
** discussion:<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
See also http://lanl.arxiv.org/pdf/1402.4108.pdf Wu, Errard, Dvorkin, Lee, McDonald, Slosar, Zahn.<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=992UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T11:41:04Z<p>Jacques: </p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
<br />
----<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* 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)?<br />
* for cluster science we probably should consider the issues of<br />
** relativistic corrections<br />
** point source contamination in clusters<br />
<br />
----<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model. This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
A description of a early version of the Planck Sky model can be found in Delabrouille, Betoule, Melin, et al. (2013), "The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths", A&A; 553, 96 (http://adsabs.harvard.edu/abs/2013A%26A...553A..96D). See also the PSM web page: http://www.apc.univ-paris7.fr/~delabrou/PSM/psm.html<br />
<br />
The PSM has been used to generate simulations for Planck described in Planck 2015 results. XII. Full Focal Plane simulations (http://adsabs.harvard.edu/abs/2015arXiv150906348P).<br />
<br />
Here is a short overview of the components in the model, their main limitations, and plans to fix those limitations. Suggestions and prioritization would be useful.<br />
<br />
*Galactic emission: Low frequency model is still based on WMAP + Haslam 408 MHz maps. Dust based on Planck for the current version used to make Planck FFP8 maps (not public yet). Low frequency foregrounds should be updated on the basis of recent Planck analysis (http://adsabs.harvard.edu/abs/2015arXiv150606660P). All maps are limited by the limited angular resolution of the input observations.<br />
**Synchrotron: based on WMAP + Haslam 408 MHz maps, polarization from a large scale model of the galactic magnetic field and of depolarization. Complemented by random fluctuations on small scales. <br />
**Free-free: Should be de-noised at high galactic latitude. Assumed unpolarized for the moment.<br />
**CO: based on ground based observations (Dame et al.), only part-sky. Assumed unpolarized for the moment.<br />
** Spinning dust: Should be de-noised, frequency dependence of the emission should be pixel-dependent. Assumed unpolarized for the moment.<br />
** Dust: Temperature and Polarization based on Planck HFI (mostly 353 GHz). Several options for scaling with frequency exist.<br />
** Other lines than CO are missing<br />
*Clusters: based on number counts + random distribution on the sky, with relativistic corrections, scaling laws to connect mass to Y. Thermal SZ, kinetic SZ (with random directions), polarized SZ effects are implemented. Missing: contamination by radio and IR sources, correlation with CIB and with lensing.<br />
*CIB (unresolved background of (mostly high redshift) dusty galaxies<br />
*Radio sources<br />
*Local IR galaxies<br />
<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
* conservative regarding systematics<br />
* conservative regarding foreground complexity<br />
<br />
<br />
<br />
- How important are Simplicity, Transparency, Flexibility?<br />
* Forecasting <--> Survey design tradeoffs for different science drivers<br />
* Would like to see this separately for large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
<br />
<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=987UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T11:15:36Z<p>Jacques: </p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
<br />
----<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* 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)?<br />
* for cluster science we probably should consider the issues of<br />
** relativistic corrections<br />
** point source contamination in clusters<br />
<br />
----<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model. This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
A description of a early version of the Planck Sky model can be found in Delabrouille, Betoule, Melin, et al. (2013), "The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths", A&A; 553, 96. See also the PSM web page: http://www.apc.univ-paris7.fr/~delabrou/PSM/psm.html<br />
<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
* conservative regarding systematics<br />
* conservative regarding foreground complexity<br />
<br />
<br />
<br />
- How important are Simplicity, Transparency, Flexibility?<br />
* Forecasting <--> Survey design tradeoffs for different science drivers<br />
* Would like to see this separately for large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
<br />
<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=984UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-22T11:11:50Z<p>Jacques: </p>
<hr />
<div>=='''Forecasting for the S4 Science Book'''==<br />
<br />
==='''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''===<br />
<br />
Julian<br />
<br />
What are the right inputs to the simulations?<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Mission model<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
What are the required output(s) of the simulations?<br />
* Maps<br />
* Covariances<br />
<br />
What are the required analysis outputs/metrics/figures of merit?<br />
* Power spectra<br />
* Parameters<br />
* Uncertainties<br />
<br />
What is the right balance between the veracity of the models used in the simulations and the tractability of their production and analysis?<br />
* Spectral domain<br />
* Map domain<br />
* Time domain<br />
<br />
What resources are available over the coming year?<br />
* People<br />
* Codes<br />
* Cycles & storage<br />
<br />
<br />
----<br />
==='''Forecasting using simulations: complexity vs. feasibility?'''===<br />
<br />
JLarge scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* 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)?<br />
* for cluster science we probably should consider the issues of<br />
** relativistic corrections<br />
** point source contamination in clusters<br />
<br />
----<br />
===Sky model for simulations and forecasting===<br />
<br />
A significant unknown in any forecasting with S4 is the complexity of foreground emission (the level is approximately known from current models).<br />
To go a step beyond first order estimates, we should make simulations of the sky emission with representative foreground complexity, including (plausible / possible) surprises.<br />
<br />
An effort is ongoing in the Planck collaboration to produce a final Planck Sky Model. This tool builds on 10 years of development of modelling sky emission in a parametric way, based on a large collection of data sets (maps observed at various frequencies, catalogues of objects, number counts, etc.).<br />
<br />
<br />
----<br />
===Some additional "Forecasting for S4 Science Book" Framing Questions===<br />
<br />
(John)<br />
<br />
- How can we make these forecasts truly realistic?<br />
* grounded in experience / achieved performance<br />
* conservative regarding systematics<br />
* conservative regarding foreground complexity<br />
<br />
<br />
<br />
- How important are Simplicity, Transparency, Flexibility?<br />
* Forecasting <--> Survey design tradeoffs for different science drivers<br />
* Would like to see this separately for large scale / degree scale / arcmin scale survey parameters<br />
* Input assumptions easily changeable to determine dependencies<br />
* What are the roles in this forecasting stage for<br />
** timestream level sims (if any)?<br />
** map level sims?<br />
** bandpower level likelihoods / fisher analyses?<br />
<br />
<br />
- What assumptions for external datasets? (e.g. BAO from...)<br />
<br />
<br />
<br />
<br />
-----<br />
<br />
===Previous Forecasting plots from S4 Snowmass===<br />
<br />
S4 Snowmass Inflation white paper (1309.5381) forecast plot:<br />
<br />
[[File:S4_snowmass_inflation_1309.5381_p12.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
- Foreground separation approach (e.g. multifreq/multicomponent tools that can be used, while maintaining simplicity/transparency)<br />
<br />
- Delensing treatment (e.g. allow different survey depth assumptions for arcmin vs deg scales)<br />
<br />
- Systematics treatment (e.g. include assumption of systematic uncertainty scaling with noise?)<br />
<br />
- alternative B-mode FOM's? (min detectable r, n_t, etc.)<br />
<br />
- <br />
<br />
<br />
S4 Snowmass Neutrinos white paper (1309.5383) forecast plot:<br />
<br />
[[File:S4_snowmass_neutrinos_1309.5383_p11.png|600px]]<br />
<br />
How do we want the S4 science book forecast to do better?<br />
<br />
-<br />
<br />
-<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Dark_Energy_/_Gravity_/_Dark_Matter_break-out_session_2&diff=386UMICH-2015: Dark Energy / Gravity / Dark Matter break-out session 22015-09-17T22:49:25Z<p>Jacques: </p>
<hr />
<div>==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Dark Energy / Gravity / Dark Matter|Return to Dark Energy / Gravity / Dark Matter sessions page]]<br />
<br />
-------<br />
<br />
====Key Science Questions:====<br />
* What are the cosmological constraints on dark energy, modified gravity, and neutrinos from a CMB-S4 SZ cluster survey? <br />
* What synergies will there be with other multi-wavelength surveys? <br />
<br />
====Key Instrumental and Systematics Questions:====<br />
* How many clusters (vs mass and redshift) will CMB-S4 detect? How will this vary with beam size, frequency coverage, and depth? Are foregrounds an issue (e.g. sources in clusters)?<br />
* How well can we calibrate cluster masses? What are the most promising techniques / measurements for reducing uncertainty, and improving cosmological constraints?<br />
<br />
------<br />
<br />
'''Multi-wave Cluster Information:'''<br />
* Overlap with X-ray (e.g., eRosita) and optical (e.g., LSST, Euclid, WFIRST) optical surveys<br />
* Projections for cluster catalog and cosmology for a joint SZ + Optical survey? + X-ray? <br />
* Need for other follow-up? e.g., How will we confirm and measure redshifts for z = 2 clusters? <br />
* New constraints enabled by multi-wave overlap (e.g., kSZ); What are they? How well will they constrain cosmology?<br />
<br />
'''Cluster Mass Calibration:'''<br />
* CMB cluster lensing; forecasts as a function of beam-size and depth<br />
* Optical weak lensing from LSST, Euclid, WFIRST; how accurate will we measure masses for a S4 cluster survey?<br />
* Clustering of clusters<br />
<br />
'''Simulations:'''<br />
* Projections for cluster counts for different S4 configurations: What is needed for this? <br />
* Projections for cosmological constraints: What is needed? What assumptions will we make?</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=385UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-17T22:36:31Z<p>Jacques: </p>
<hr />
<div>'''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''<br />
<br />
Julian:<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Instrument/observation model(s)<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
* Analyses<br />
** What are the output(s) of the simulations?<br />
** What are the analysis metrics/figures of merit?<br />
<br />
What is the right balance between the veracity of the models and the tractability of their production and analysis?<br />
<br />
What resources (people, codes, cycles) are available over the coming year?<br />
<br />
----<br />
'''Forecasting using simulations with varying balance between complexity and feasibility?'''<br />
<br />
Jacques:<br />
<br />
Right balance issue: Large scale simulations with full complexity require long investment and significant resources.<br />
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?<br />
How many levels of complexity should we consider, e.g.<br />
* basic: sensitivity with Gaussian white stationary noise; gaussian beams; sky fraction<br />
* moderate: inhomogeneous noise with non-stationarity and low-frequency excess<br />
* 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<br />
* full: as realistic as possible<br />
<br />
A few additional ideas:<br />
* 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?<br />
* 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?<br />
* 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)?<br />
* for cluster science we probably should consider the issues of<br />
** relativistic corrections<br />
** point source contamination in clusters<br />
<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=374UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-17T22:07:13Z<p>Jacques: </p>
<hr />
<div>'''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''<br />
<br />
Julian:<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Instrument/observation model(s)<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
* Analyses<br />
** What are the output(s) of the simulations?<br />
** What are the analysis metrics/figures of merit?<br />
<br />
What is the right balance between the veracity of the models and the tractability of their production and analysis?<br />
<br />
What resources (people, codes, cycles) are available over the coming year?<br />
<br />
----<br />
'''Forecasting using simulations with varying balance between complexity and feasibility?'''<br />
<br />
Jacques:<br />
<br />
Right balance issue: Large scale simulations with full complexity require long investment and very significant resources.<br />
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?<br />
How many levels of complexity should we consider?<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacqueshttps://cmb-s4.uchicago.edu/wiki/index.php?title=UMICH-2015:_Simulations_%26_Data_Analysis_Break-Out_Session_2&diff=367UMICH-2015: Simulations & Data Analysis Break-Out Session 22015-09-17T22:00:59Z<p>Jacques: </p>
<hr />
<div>'''What S&DA do we need to do to support the science sections of the CMB-S4 Science Book?'''<br />
<br />
Julian:<br />
<br />
* Sky model<br />
** CMB:<br />
*** scalar, tensor, non-Gaussian<br />
** Foregrounds:<br />
*** components<br />
** Self-consistency<br />
<br />
* Instrument/observation model(s)<br />
** Instrument: <br />
*** beams, band-passes, noise PSDs<br />
*** detector electronics, data acquisition<br />
** Observation:<br />
*** scanning strategy, flags<br />
*** atmosphere, ground pickup<br />
<br />
* Analyses<br />
** What are the output(s) of the simulations?<br />
** What are the analysis metrics/figures of merit?<br />
<br />
What is the right balance between the veracity of the models and the tractability of their production and analysis?<br />
<br />
What resources (people, codes, cycles) are available over the coming year?<br />
<br />
----<br />
'''Forecasting using simulations with varying balance between complexity and feasibility?'''<br />
<br />
Jacques:<br />
<br />
Large scale simulations with full complexity require long investment and very significant resources.<br />
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?<br />
Should we try something like that?<br />
<br />
----<br />
<br />
==Wiki navigation==<br />
[[Cosmology with CMB-S4|Return to main workshop page]]<br />
<br />
[[UMICH-2015: Simulations & Data Analysis| Return to Simulations & Data Analysis page]]</div>Jacques