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EOR Detection Strategies

EOR Detection Strategies. Somnath Bharadwaj IIT Kharagpur. http://articles.moneycentral.msn.com/SavingandDebt/SaveMoney/10easyWaysToStashAwayThousands.aspx 10 easy ways to stash away thousands Readers share their secret ploys to save cash throughout the year. These clever ideas make

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EOR Detection Strategies

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  1. EOR Detection Strategies Somnath Bharadwaj IIT Kharagpur

  2. http://articles.moneycentral.msn.com/SavingandDebt/SaveMoney/10easyWaysToStashAwayThousands.aspxhttp://articles.moneycentral.msn.com/SavingandDebt/SaveMoney/10easyWaysToStashAwayThousands.aspx 10 easy ways to stash away thousands Readers share their secret ploys to save cash throughout the year. These clever ideas make saving money easy and painless. By Liz Pulliam Weston 2 easy ways to detect the EOR 21-cm signal

  3. The 21-cm EOR Signal • Will be seen in Emission Ts > Tg • Varies with angle and frequency • Fluctuations caused by: • Variations in the neutral fraction • Fluctuations in gas density (Dark Matter Fluctuations) • Peculiar Velocities qy n qx

  4. Radio Interferometric Arrays GMRT 30 antennas 45m diameter

  5. The 21-cm Signal qy z qx

  6. Interferometry and Visibilities l / d A visibility V(U,n) records a single Fourier component of dI(q,n) with angular wave number 2p /U Angular multi-pole

  7. Baseline Distribution • Visibility V(U,n) • 21-cm Signal • Noise (inherent to the • observation) • Foregrounds from other • astrophysical Sources • Man-made RFI U

  8. Why bother about Visibilities? FT Why not try to detect the signal in the Image? Noise in different visibilities is uncorrelated Noise in different pixels of the image is correlated Imaging Artifacts Incomplete u-v coverage

  9. The w term l = cos a m = cos b

  10. Small Field of View FT D/l

  11. Relative Contributions

  12. 2 Easy Ways Statistical Detection Statistical properties of the 21-cm signal are significantly different from those of foregrounds and noise. Use this to separate out the signal. Ionized Bubble Detection Develop a template based on prior knowledge of the expected signal. Use this to search for the signal buried in noise and foregrounds. Matched Filter.

  13. Statistical Detection Jy2 K2 Multi-frequency angular power spectrum

  14. Visibility correlations

  15. The Estimator D/l DU < D/l U >> DU U1 U2 V2 (U,Dn) q0 ~ l / D Self Correlations avoided

  16. 80 pc 500 pc Begum, A. et al. 2006 MNRAS, 372, L33

  17. The HI Signal 10-7 Jy2 Bharadwaj, S.& Sethi, S.K. 2001, 22, 293 Morales, M. F. & Hewitt, J. 2004, ApJ, 615,7; Bharadwaj, S. & Ali, Sk.S., 2005, MNRAS,356, 1519

  18. Decorrelation

  19. 14 hrs GMRT Observations 153 MHz Observation 5 MHz Bandwidth 62.5 kHz resolution Primary Beam FWHM ~4 deg. Synthesized beam 28” x 23” Noise 1.6 mJy/Beam RA 01 36 46 DEC 41 24 23 Ali, Sk. S. et al. 2008, MNRAS, 385, 2166

  20. Visibility Correlations

  21. Foregrounds Santos, M.J. et al. 2005, ApJ,625,575; Di Matteo, T. et al., 2002,ApJ, 564,576 Zaldarriaga, M. et al., 2004, ApJ, 608, 622

  22. Foreground Removal • Foregrounds are all continuum sources • Emissions at Dn ~1 MHz are expected to be highly correlated • The HI signal decorrelates within 1 MHz • In the image cube – fit and subtract a smooth polynomial in n along each pixel Jelic, V. et al. 2008, MNRAS, 389, 1319 Bowman, J. D. et al. 2009, ApJ, 695, 183

  23. Foreground Removal • Advantages in working with visibilities • Grid the visibilities V(U,n) - 3D grid • Fit and subtract a smooth polynomial along n at each U grid point Liu, A. 2009, arxiv-0903.4890 McQuinn, M. et al. 2006, ApJ, 653,815; Morales, M.F. et al. 2006, ApJ,648, 767

  24. Foreground Removal • The foreground contributions to V2 (U,Dn) is predicted to decorrelate less than 1% in the 5 MHz bandwidth of our observation • The signal contribution decorrelates within 1 MHz • Fit V2 (U,Dn) with a smooth polynomial in Dn and subtract this out

  25. Measured Decorrelations

  26. Three Visibility Correlation • Probes the Bispectrum • Significant as the HI distribution at reionization is expected to be quite non-Gaussian • Non-zero only if three baselines form a closed triangle • Decorrelates within Dn ~ 1MHz Bharadwaj, S. & Pandey, S.K. 2005, MNRAS, 358, 968

  27. Ionized Bubble Detection Datta, K.K. et al., 2007, MNRAS, 382, 809

  28. Signal from an HII bubble Bubble at center of field of view, phase factor and fall in amplitude if shifted 70 mJy

  29. The Problem of Signal Detection

  30. The Contaminants • We treat the Noise, Foregrounds and the HI Fluctuations outside the bubble as independent random signals • V(U,n)=S(U,n) + N(U,n) + F(U,n) + H(U,n)

  31. Matched Filter V(U,n)=S(U,n) + N(U,n) + F(U,n) + H(U,n)

  32. The Variance Dominated by Foregrounds Exceeds the Signal Foreground Removal necessary

  33. The Filter • Remove Foregrounds • Optimize Signal to Noise Ratio

  34. Predictions Z=8.5 Depends on antennas and baseline coverage Filter effectively removes foreground Noise reduces with observing time HI fluctuations outside the bubble impose a fundamental restriction on the smallest bubble that can be detected xHI=1 1000 hrs, > 22 Mpc

  35. 2 Easy Ways to Detectthe EOR 21-cm Signal

  36. Thank You

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