Apex scanning strategy & map-making A. Amblard & M. White

We simulated the observation from Atacama site : longitude : - 67˚45’ latitude : - 23˚1’ We choose WFS Observation time per day in equatorial coordinates for an elevation above 30 degrees

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 repointing Speed on the sky 0.25’/s 10’ peak to peak at 0.5 Hz Speed on the sky ~ 10’/s 2 degrees peak to peak at 50 mHz Speed 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%

8 hours for 90 days simulated with a 10 Hz, about 25 millions Simulations Main 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 (2048x2048 pixels map of 0.2’ resolution 1/f gaussian noise with a spectrum model with σ= 280 μK/√Hz, fknee=10 mHz, α=-2 the noise is generated in fourier space in 1 chunk of 20 millions elements

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 effect due to low speed on the sky

SZ signal in the timeline Fast sweeping Pure drift Slow sweeping After a 0.1 Hz higpass filter the SZ signal has almost disappeared

Map-making using MADmap Pure drift : The map-making converge to a striped solution, adding a CMB and dust component or 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 properties Using MADCAP, we performed again the map-making and obtain the pixel-pixel noise 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 more homogenous in the slow case (8% against 20% of dispersion)

Pixel-pixel noise correlation Fast sweeping Slow sweeping 3 rows of the noise correlation matrix (first,middle and last) showing how the pixels are correlated with each other The slow sweeping have the lowest level of correlation due to a better mixing of the time 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 frequency but imply to move the telescope ; with slow sweeping correlation could be small, but what if the noise is more correlated ?