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Foreground subtraction or foreground avoidance?

Foreground subtraction or foreground avoidance?. Adrian Liu, UC Berkeley. Vision. The redshifted 21cm line is possibly our only direct probe of reionization and the dark ages. 21cmFAST, Mesinger et al. Current power spectrum limits from experiments like PAPER….

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Foreground subtraction or foreground avoidance?

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  1. Foreground subtraction or foreground avoidance? Adrian Liu, UC Berkeley

  2. Vision

  3. The redshifted 21cm line is possibly our only direct probe of reionization and the dark ages 21cmFAST, Mesinger et al.

  4. Current power spectrum limits from experiments like PAPER… Parsons, AL et al. 2013, 1304.4991

  5. …are sensitivity/integration time limited at high k… Parsons, AL et al. 2013, 1304.4991

  6. …are likely limited by foreground contamination at low k. Parsons, AL et al. 2013, 1304.4991

  7. Foreground contamination is serious Foregrounds ~ O(100 K); Signal ~ O(1-10 mK)

  8. Cosmic Microwave Background 21cm Tomography (See AL, Pritchard, Tegmark, Loeb 2013 PRD 87, 043002 for more details)

  9. Foreground subtraction • Work at low k. • Instrumental noise low. • Foreground modeling requirements extreme. Parsons, AL et al. 2013, 1304.4991

  10. Foreground avoidance • Work at high k. • Instrumental noise high. • Foreground modeling requirements easier. Parsons, AL et al. 2013, 1304.4991

  11. Foreground subtraction or foreground avoidance?

  12. A robust framework for the quantification of errors is essential for a detection of the power spectrum. • “Optimal” methods may be overly aggressive and susceptible to mis-modeling of foregrounds. • Assuming that foregrounds are Gaussian-distributed may lead to an underestimation of errors. • Foreground avoidance may be a more robust way forward. Take-home messages

  13. Necessary ingredients for successful foreground mitigation

  14. A power spectrum estimation framework that fully propagates error covariances. Ingredients for foreground mitigation Foreground model Fourier, binning Data Model uncertainty Bias removal

  15. 100 101 10-50 100 10-1 10-100 10-2 10-1 AL 2013, in prep.

  16. 100 101 10-50 100 10-1 10-100 10-2 10-1 AL 2013, in prep.

  17. 100 101 10-50 100 10-1 10-100 10-2 10-1 AL 2013, in prep.

  18. A power spectrum estimation framework that fully propagates error covariances. • Window functions. • Covariant errors. Ingredients for foreground mitigation

  19. Along constant k-tracks, error properties differ k~3hMpc-1 k~0.4hMpc-1 k~0.1hMpc-1

  20. 80% Ignoring error correlations can yield larger error bars or mistaken detections 60% 40% Relative error bar increase 20% 0% -20% 10-2 10-1 100 101 k [Mpc-1] Dillon, AL, Williams et al. 2013, 1304.4229

  21. A power spectrum estimation framework that fully propagates error covariances. • Window functions. • Covariant errors. Ingredients for foreground mitigation

  22. A power spectrum estimation framework that fully propagates error covariances. • Window functions. • Covariant errors. • A good foreground model including error covariances(see, e.g., Trott et al. 2012, ApJ 757, 101). Ingredients for foreground mitigation Foreground model Model uncertainty

  23. A power spectrum estimation framework that fully propagates error covariances. • Window functions. • Covariant errors. • A good foreground model including error covariances(see, e.g., Trott et al. 2012, ApJ 757, 101). • A method for propagating foreground properties through instrumental effects (e.g. chromatic beams). Ingredients for foreground mitigation

  24. 100 101 10-50 100 10-1 10-100 10-2 10-1 AL 2013, in prep.

  25. A power spectrum estimation framework that fully propagates error covariances. • Window functions. • Covariant errors. • A good foreground model including error covariances(see, e.g., Trott et al. 2012, ApJ 757, 101). • A method for propagating foreground properties through instrumental effects (e.g. chromatic beams). Ingredients for foreground mitigation

  26. Foreground subtraction or foreground avoidance?

  27. Subtraction Avoidance Projection matrix, e.g. delay transform

  28. 102.5 Error(avoid) Error(sub) 100 101 10-1 100 10-2 10-1 AL 2013, in prep.

  29. 102.5 Error(avoid) Error(sub) 100 101 10-1 100 10-2 10-1 AL 2013, in prep.

  30. Subtraction Avoidance AL 2013, in prep.

  31. Leakage from mismodeled foregrounds more extended for subtraction than for avoidance 101 100 Avoidance 100 10-50 10-100 10-1 10-1 10-2 AL 2013, in prep.

  32. Leakage from mismodeled foregrounds more extended for subtraction than for avoidance 101 100 Subtraction 100 10-50 10-100 10-1 10-1 10-2 AL 2013, in prep.

  33. Non-Gaussianity?

  34. Foregrounds are highly non-Gaussian Histogram de Oliveira-Costa 2008, MNRAS 388, 247 Log[p(T)] T

  35. 10-2 10-4 p(T) Log-norm 10-6 Gaussian 10-8 0 2000 1000 T [K] AL 2013, in prep.

  36. Assuming Gaussianity doesn’t bias the estimator Pick b to ensure cancellation

  37. Assuming Gaussianity causes the error to be underestimated

  38. Assuming Gaussianity causes the error to be underestimated

  39. A robust framework for the quantification of errors is essential for a detection of the power spectrum. • “Optimal” methods may be overly aggressive and susceptible to mis-modeling of foregrounds. • Assuming that foregrounds are Gaussian-distributed may lead to an underestimation of errors. • Foreground avoidance may be a more robust way forward. Take-home messages

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