Sigma(r) vs r plots in prep for the DSR, polarization galactic mask.

  B. Racine, R. Flauger

Yet another posting about \(\sigma(r)\) vs. \(r\) plots.
WARNING, This is preliminary.
In this posting, we use galactic masks defined in polarization to throw away areas of the sky that are highly contaminated.
Updated on May 15th to add the case where Chile's observing efficiency is half the one at Pole, as well as a few missing clicks.


Introduction

In this recent posting, we proposed some \(\sigma(r)\) vs. \(r\) plots for the DSR.

In a subsequent posting, we updated our results with a more realistic handling of the joint observations, and studied the effect of the 20GHz channel on the constraints. We also tried to very naively boost the foreground level in the shallow part of the Chile map, since it is hitting a region with more foreground.


In the current posting, we instead apply a mask to the hitmap used for the Fisher forecast (thus reducing the number of modes used in the high foreground regions.)
As in the last posting (as mentioned in this posting):

1: \(\sigma(r)\) vs r for different hitcount maps.

The current DSR configuration is a 18 tubes configuration (slightly updated since the spreadsheet, now [2,2,6,6,6,6,4,4] tubes, each with [288, 288, 3524, 3524, 3524, 3524, 8438, 8438] detector per tube for [20, 30, 40, 85, 95, 145, 155, 220, 270] GHz, and 135 at 20GHz on a LAT).
We also show other configurations for comparison: 6 (1,2,2,1), 9 (1,3,3,2), 12 (1,4,4,3) in addition to 18 (2,6,6,4), where this notation shows the dichroic coupling.
Note that these configurations have been chosen so that they can sum to the default 18 tubes over 2 sites. This is studied in the next section.

In figure 1, we show the \(\sigma(r)\) vs r plots for different configurations, with or without decorrelation, after applying different cuts based on the polarized foreground levels.

Figure 1: \(\sigma(r)\) as a function of r, where the band shows the range of \(\sigma(r)\) depending on the inclusion of the 0.5% residual foreground bias (in quadrature with the \(\sigma(r)\), as explained for instance in this posting. Note that the bias is 3.22 higher in the shallow part of the Chile deep map.

In yellow, we show the Pole deep strategy, in red, we show the Pole wide strategy, in blue, the "hybrid" Chile deep strategy, in green the Chile deepest patch, delensed by Pole, and in grey the Chile shallow patch, delensed by the Chile LAT.
Figure 2: \(\sigma(r)\) as a function of r, where the band shows the range of \(\sigma(r)\) between the case with no decorrelation and the case with decorrelation (solid line) with foreground penalty as above.

In yellow, we show the Pole deep strategy, in red, we show the Pole wide strategy, in blue, the "hybrid" Chile deep strategy, in green the Chile deepest patch, delensed by Pole, and in grey the Chile shallow patch, delensed by the Chile LAT.

2: \(\sigma(r)\) Tables for combined observations.

In the post DSR spreadsheet, we were combining the constraints as a weighted average of independent results, i.e. summing the \(\sigma(r)\) in inverse quadrature. Here instead, we are combining at the map level, by simply summing the hitmaps for the overlapping deep patch. These new combined observations then go through Raphael's ILC to compute the residual signals and the corresponding "more optimal" scalings (see appendix A). For the Chile observations, we still add the shallow part in inverse quadrature (Since the patches don't overlap by definition, this is an ok approximation, even though they are measuring the same \(\ell\) mode.)

Figure 3: \(\sigma(r)\) for the combined Chile and Pole observations, for different assumptions.

3: \(\sigma(r)\) vs r for the combined observations.

In this plot, we show the \(\sigma(r)\) vs r for a total of 18 tubes, but split in different ways over the 2 sites.

Figure 4: \(\sigma(r)\) as a function of r for the combined Chile and Pole observations, where the band shows the range of \(\sigma(r)\) with foreground penalty as above, alone or with an additional observation efficiency penalty
We show the combination in the form of (Pole, Chile) number of tubes, dark red being all 18 tubes in Chile, dark blue all 18 tubes in Chile.

4: \(\sigma(r)\) vs r with bands for the foreground cut used.

In this section, we show \(\sigma(r)\) vs r plots, adding cuts based on the polarization intensity. Here we use the galactic masks introduced briefly in appendix B.

Figure 5: \(\sigma(r)\) as a function of r, where the band shows the range of \(\sigma(r)\) depending on the mask used in the analysis. The lower edge of the band uses the hitmaps after applying a cut keeping the 58% cleanest part of the full sky. The upper edge cuts to the 28% cleanest. For the upper edge, we still have an additional 0.5% residual foreground bias (in quadrature with the \(\sigma(r)\), as explained for instance in this posting. this is now subdominant to the masking effect.

In yellow, we show the Pole deep strategy, in red, we show the Pole wide strategy, in blue, the "hybrid" Chile deep strategy, in green the Chile deepest patch, delensed by Pole, and in grey the Chile shallow patch, delensed by the Chile LAT.
Figure 6: \(\sigma(r)\) as a function of r, where the band shows the range of \(\sigma(r)\) between the case with no decorrelation and the case with decorrelation (solid line) with the masking effect as above.

In yellow, we show the Pole deep strategy, in red, we show the Pole wide strategy, in blue, the "hybrid" Chile deep strategy, in green the Chile deepest patch, delensed by Pole, and in grey the Chile shallow patch, delensed by the Chile LAT.

Appendix A: Scaling factors.

This part need to be documented more, but meanwhile, here are some notes from Raphael defining the new scaling factors, and how to rescale the BPCM: here (pdf scan of Raphael's notes).

The 4 scaling factors introduced in the notes above are plotted here as a function of the value of r for the different masks. They can be downloaded in this tarball. They were plotted for the unmasked version in this posting

Appendix B: Galactic mask of polarized emission.

Here we show the different hitmaps as well as the new masks introduced. These have been produced by Raphael, based on Planck polarized intensity.

Comments on figure 7:

Figure 7: hitcount maps and the old noise variance scale factors for comparison, the new ones are plotted above. Note the different normalization in the plots here.
Figure 8: Diffuse galactic components after applying a mask based on he cleanest part of the sky.