Difference between revisions of "Map-making LFI"
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===Inputs=== | ===Inputs=== | ||
The noise MC assumes a three-parameter noise model: white noise level ($\sigma$), slope, and knee frequency ($f_\mathrm{knee}$), so that the noise power spectrum is assumed to have the form | The noise MC assumes a three-parameter noise model: white noise level ($\sigma$), slope, and knee frequency ($f_\mathrm{knee}$), so that the noise power spectrum is assumed to have the form | ||
| − | $ P(f) = \frac{2\sigma^2}{f_\mathrm{sample}}\left(\frac{f}{f_\mathrm{knee}}\right)^\mathrm{slope} $. | + | |
| + | :$ P(f) = \frac{2\sigma^2}{f_\mathrm{sample}}\left(\frac{f}{f_\mathrm{knee}}\right)^\mathrm{slope} $. | ||
| + | |||
| + | Here $f_\mathrm{sample}$ is the sampling frequency of the instrument. The noise parameters were determined separately for each radiometer | ||
Revision as of 10:11, 19 October 2012
Noise Monte Carlo Simulation[edit]
Overview[edit]
Calculating and handling full pixel-to-pixel noise covariances in Planck maps if feasible only at low resolution. To support the analysis of high-resolution maps, a Monte Carlo set of noise maps were produced. These noise Monte Carlos were produced at two levels of the analysis: 1) LFI Monte Carlo (MC) as part of the LFI data processing, and 2) Full Focal Plane (FFP) Monte Carlo as part of the joint HFI/LFI data processing. This page describes the LFI noise MC. For the FFP MC, see HL-sims.
Inputs[edit]
The noise MC assumes a three-parameter noise model: white noise level ($\sigma$), slope, and knee frequency ($f_\mathrm{knee}$), so that the noise power spectrum is assumed to have the form
- $ P(f) = \frac{2\sigma^2}{f_\mathrm{sample}}\left(\frac{f}{f_\mathrm{knee}}\right)^\mathrm{slope} $.
Here $f_\mathrm{sample}$ is the sampling frequency of the instrument. The noise parameters were determined separately for each radiometer
(Planck) Low Frequency Instrument
(Planck) High Frequency Instrument
