# Difference between revisions of "Characterization of simulations for configurations 30-33"

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== Noise characterization == | == Noise characterization == | ||

− | To characterize the noise properties of the simulations, we provide a plot for each configuration comparing the noise spectra obtained from the simulations with the expectation based on the input noise model. In addition to the expected average, we can also predict the noise variance. The comparison of the predicted noise | + | To characterize the noise properties of the simulations, we provide a plot for each configuration comparing the noise spectra obtained from the simulations with the expectation based on the input noise model. |

+ | |||

+ | In addition to the expected average, we can also predict the noise variance. For inverse noise variance weighted maps, one finds | ||

+ | \sigma(N_\ell)=\sqrt{\frac{2}{(2\ell+1)\delta\ell f_{\rm sky}^{noise}}N_\ell. The comparison of the predicted standard deviation for the noise spectra with that obtained from the simulations is also shown for each configuration. | ||

+ | |||

+ | Finally, we show sample realizations of the noise maps for the Stokes Q parameter for the different frequency bands. | ||

+ | |||

+ | The theoretical expectation and simulations typically agree to within a few per cent. The lowest bin is an exception and (just like in the data challenge simulations) the noise estimated from the simulations exceeds the theory curve by as much as 20 per cent. This is caused by the prescription used (here and in the community more generally) to generate the apodized noise maps, which assumes that reweighting pixels by Nobs leaves the power spectrum unchanged. This assumption fails as on scales that approach scales on which the hits map varies. This can in principle be corrected but has not been done here. | ||

+ | |||

=== Configuration 30 === | === Configuration 30 === |

## Revision as of 23:47, 30 March 2019

## Contents

## Noise characterization

To characterize the noise properties of the simulations, we provide a plot for each configuration comparing the noise spectra obtained from the simulations with the expectation based on the input noise model.

In addition to the expected average, we can also predict the noise variance. For inverse noise variance weighted maps, one finds \sigma(N_\ell)=\sqrt{\frac{2}{(2\ell+1)\delta\ell f_{\rm sky}^{noise}}N_\ell. The comparison of the predicted standard deviation for the noise spectra with that obtained from the simulations is also shown for each configuration.

Finally, we show sample realizations of the noise maps for the Stokes Q parameter for the different frequency bands.

The theoretical expectation and simulations typically agree to within a few per cent. The lowest bin is an exception and (just like in the data challenge simulations) the noise estimated from the simulations exceeds the theory curve by as much as 20 per cent. This is caused by the prescription used (here and in the community more generally) to generate the apodized noise maps, which assumes that reweighting pixels by Nobs leaves the power spectrum unchanged. This assumption fails as on scales that approach scales on which the hits map varies. This can in principle be corrected but has not been done here.

### Configuration 30

#### Comparison of simulation average with noise model

#### Comparison of noise variance with expectation based on noise model and weight map

### Configuration 31

#### Comparison of simulation average with noise model

#### Comparison of noise variance with expectation based on noise model and weight map

### Configuration 32

#### Comparison of simulation average with noise model

#### Comparison of noise variance with expectation based on noise model and weight map

### Configuration 33

#### Comparison of simulation average with noise model

#### Comparison of noise variance with expectation based on noise model and weight map