Apex scanning strategy & map-making

Apex scanning strategy& map-making

A. Amblard & M. White

We simulated the observation from Atacama site :longitude : - 67˚45’ latitude : - 23˚1’

Observation time per day in equatorial coordinates foran elevation above 30 degrees

We choose WFS

Good time to observe : the beginning of the year because the sun is under the horizon when WFS is at least 30 degrees above

January 2004 : elevation of WFS (black), elevation of the sun (blue),& angular distance (-100 degrees) between WFS and the sun

3 scanning-strategy tried :

• Pure drift scanning

• Drift scanning + sweeping of secondary mirror

•Drift scanning + sweeping of the telescope

24 minutes of drift then repointingSpeed on the sky 0.25’/s

10’ peak to peak at 0.5 HzSpeed on the sky ~ 10’/s

2 degrees peak to peak at 50 mHzSpeed on the sky ~ 12’/s

Drift Scanning

The coverage is very homogenous, with a dispersion of about 1%

Fast sweeping

The coverage is very inhomogenous, with a dispersion of about 40%

Slow sweeping

The coverage is quite homogenous, with a dispersion of about 10%

SimulationsMain characteristics :

• 8 hours for 90 days simulated with a 10 Hz, about 25 millions

samples, only about 20 millions (enough to cover a 6x6 degrees field around WFS center) used for map-making

• 2 components introduced :

SZ signal obtained with M. White simulations (2048x2048pixels map of 0.2’ resolution

1/f gaussian noise with a spectrum modelwith σ= 280 μK/√Hz, fknee=10 mHz, α=-2the noise is generated in fourier space in 1 chunkof 20 millions elements

kneeff

NP 12

SZ map input

The original map has been smoothed to 0.8’ and centered on WFS

Position of the SZ signal in frequency space

Beam effectdue to low speed on the sky

SZ signal in the timelineFast sweeping

Slow sweeping

Pure drift

After a 0.1 Hz higpass filterthe SZ signal has almost disappeared

Map-making using MADmapPure drift :

The map-making converge to a striped solution, adding a CMB and dust componentor a second detector does not improve the convergence

Fast sweeping

The map-making converge to a reasonable solution,we recognize the principal clusters on the map

Slow sweeping

The map-making converge also to a reasonable solution (in fact faster by a factor 5),& we recognize the principal clusters on the map

Noise propertiesUsing MADCAP, we performed again the map-making and obtain the pixel-pixelnoise correlation matrix for the sweeping scan-strategies

Fast sweeping Slow sweeping

Histograms of the diagonal element of the noise correlation matrix

As seen on the observed time per pixel, the noise level is morehomogenous in the slow case (8% against 20% of dispersion)

Pixel-pixel noise correlationFast sweeping

Slow sweeping

3 rows of the noise correlation matrix (first,middle and last) showing how the pixelsare correlated with each other

The slow sweeping have the lowest level of correlation due to a better mixing of thetime correlation on the sky

Decrease of the correlation

auto-correlation

first neighbours

On average the correlation decrease faster for the slow sweeping than for the fast,there is a factor 5 between their decrease on the first neighbours.

Conclusions

• avoid the pure drift scanning strategy, its speed on the sky is slow (speed on the sky should bring the signal in good frequency range);

• slow sweeping is better than fast one : it seems better to increase the amplitude than the frequencybut imply to move the telescope ;

• with slow sweeping correlation could be small, butwhat if the noise is more correlated ?