CMB Data Analysis: techniques and results

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Sheila Lawson
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1 CMB Data Analysis: techniques and results Paolo Natoli Dipartimento di Fisica Università Tor Vergata On behal of COFIS WP 2310

2 Outline Background and context Result highlights Robustness to systematic effects Component separation Non Gaussianity Accurate model of the CMB likelihood Conclusions and remarks

3 Why is CMB data analysis so challenging? Large Problem Many Gb/channel/day 10 5 to 10 7 pixels Targeted to signals with unfavourable S/N ratio. Detectors inject noise. Noise is often correlated (long memory, or 1/f ) and cross-correlated across different detectors Systematics (non stochastic noise) play an increasingly important role in high accuracy experiments.

4 Size of dataset is an issue Need to observe large fractions of the sky to decrease sampling variance observe up to few arcmin resolution, to gather most cosmological information Maps with many resolution elements (pixel) typically in the million range

resolution, to gather most cosmological information Maps with

5 Increasing size/complexity COBE, early 1990, ~ 10 K pixels WMAP, early 2000, ~ 1M pixels Planck, launch 2009, > 10 M pixels

6 But noise is the true enemy! Good CMB detectors have noise sensitivities in the range < 100 µk s Experiments must spin (or chop) quickly to modulate the signal (exp. low modes, dipole often used for calibration) in the frequency range were amplifiers are stable enough. Systematic effects effectively behave as spurious signals: need proper statistical treatment cosmological signal is largely subdominant in the timestream, have to resort to non trivial statistical techniques to dig it out

the signal (exp. low modes, dipole often used for calibration) in the frequency range were amplifiers are stable enough.

7 Key tiers in the pipeline Calibration / Map Making / Noise estimation Separation of the CMB field (intensity and polarization) from astrophysical components Angular power spectrum estimation / modeling of the CMB likelihood Monte Carlo based analysis is customary

polarization) from astrophysical components Angular power

8 Escalation in computer power Tflops 100 Planck 1 WMAP 0.1 Balloons (e.g Boomerang) COBE Time

9 Work package on CMB data analysis Wide area (~ 20 topics) Very transverse: from raw data processing to science extraction Very well connected: if you get something wrong, you will see it at some point I will review a few highlights

extraction Very well connected: if you get something

10 Robustness to systematics: rotation of polarization plane How well will future missions need to calibrate their focal plane? Very important effect: photon propagation is a probe of fundamental physics

11 Rotation: effect for Planck 1 to 4 deg L Pagano et al

12 Rotation effect for BPOL 0.1 to 0.7 deg! L Pagano et al

13 Robustness to systematics: accounting for realistic beam profiles Sandri et al

14 TT EE TE BB G. Polenta/ T. Poutanen

15 Auto-power spectrum: Noise bias basic definition: d lm = a lm + n lm ~ C l = 1 2l + 1 d lm d lm = C l + N l Signal + Noise power spectrum CMB power spectrum 0.1% noise biase can be detected G. Polenta et al.

16 Mixing matrix estimation with CCA Correlated Component Analysis (CCA, Bedini et al. 2006, Bonaldi et al ) estimates the mixing matrix, the key ingredient for component separation, from a set of multi-frequency microwave maps. Drawbacks of traditional CCA: The input maps should have common resolution The mixing matrix was provided with no estimation error attached We developed a version of CCA working in the harmonic domain (HCCA) capable to work with multi-resolution data and to provide an estimation of the errors Results on Planck simulated polarization data: actual error in ricovering dust and synchrotron spectral indices vs estimated error. Error estimates are meanly correct Bonaldi et al.

Drawbacks of traditional CCA: The input maps should have common resolution The mixing matrix was provided with no estimation error attached We developed a version of CCA

17 Gaussianity of the CMB Minkowski Functionals Highly accurate reference Monte Carlo pipeline P. Natoli et al.

18 CMB Power Spectrum and Likelihood at low resolution with BolPol WMAP 5 year data re-analysis with optimal code A. Gruppuso et al.

19 Conclusions and remarks High precision cosmology is only possible through high accuracy data analysis. Techniques are likely to rely increasingly heavier on simulated data and Monte Carlo techniques: computational requirements will go up. CMB data analysis for present and future missions requires close interaction between experimentalists, analysts and theorist to ensure efficient data mining and distill scientific results. Although CMB techniques are being exported to other fields (e.g. infrared processing), know how is heavily specialized and tightly anchored to requirements and needs imposed within the community: outsourcing of resources does not work well.

CMB data analysis for present and future missions requires close interaction between experimentalists, analysts and theorist to ensure efficient data mining and

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