1. EOR Detection Strategies Somnath Bharadwaj IIT Kharagpur

2. http://articles.moneycentral.msn.com/SavingandDebt/SaveMoney/10easyWays ToStashAwayThousands.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 WestonLiz Pulliam Weston 2 easy ways to detect the EOR 21-cm signal

3.  Will be seen in Emission T s > T   Varies with angle and frequency  Fluctuations caused by: Variations in the neutral fraction Fluctuations in gas density (Dark Matter Fluctuations) Peculiar Velocities The 21-cm EOR Signal yy xx

4. Radio Interferometric Arrays Frequency MHz1532353256101420 z8.35.03.41.30 GMRT 30 antennas 45m diameter

5. The 21-cm Signal yy xx z

6. Interferometry and Visibilities  / d A visibility V(U, ) records a single Fourier component of  I(  with angular wave number 2  U Angular multi-pole

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

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  m = cos 

10. Small Field of View FT DD

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 Multi-frequency angular power spectrum Jy 2 K2K2

14. Visibility correlations

15. The Estimator U1U1 U2U2 D/ V 2 (U,  )  U < D/ U >>  U Self Correlations avoided  0 ~  D

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

17. The HI Signal 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 10 -7 Jy 2

18. Decorrelation

19. 14 hrs GMRT Observations RA 01 36 46 DEC 41 24 23 153 MHz Observation 5 MHz Bandwidth 62.5 kHz resolution Primary Beam FWHM ~4 deg. Synthesized beam 28” x 23” Noise 1.6 mJy/Beam 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  ~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  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,  - 3D grid Fit and subtract a smooth polynomial along 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 V 2 (U,  ) is predicted to decorrelate less than 1% in the 5 MHz bandwidth of our observation The signal contribution decorrelates within 1 MHz Fit V 2 (U,  ) with a smooth polynomial in  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  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 70  Jy Bubble at center of field of view, phase factor and fall in amplitude if shifted

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, )=S(U, ) + N(U, ) + F(U, ) + H(U, )

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

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

33. The Filter Remove Foregrounds Optimize Signal to Noise Ratio

34. Predictions 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 x HI =1 Z=8.5 1000 hrs, > 22 Mpc

35. 2 Easy Ways to Detect the EOR 21-cm Signal

36. Thank You