EOR Detection Strategies Somnath Bharadwaj IIT Kharagpur.

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EOR Detection Strategies Somnath Bharadwaj IIT Kharagpur

Transcript of EOR Detection Strategies Somnath Bharadwaj IIT Kharagpur.

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EOR Detection Strategies

Somnath Bharadwaj

IIT Kharagpur

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By Liz Pulliam Weston2 easy ways to detect the EOR 21-cm signal

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Will be seen in Emission Ts > 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

x

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Radio Interferometric Arrays

Frequency MHz 153 235 325 610 1420

z 8.3 5.0 3.4 1.3 0

GMRT30 antennas 45m diameter

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The 21-cm Signal

y

x

z

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Interferometry and Visibilities

/ d

A visibility V(U,) records a single Fourier component of I(with angular wave number 2U

Angular multi-pole

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Baseline Distribution

U

Visibility V(U,)

•21-cm Signal

•Noise (inherent to theobservation)

•Foregrounds from other astrophysical Sources

•Man-made RFI

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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

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The w term

l = cos m = cos

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Small Field of View

FT

D

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Relative Contributions

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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 DetectionDevelop a template based on prior knowledge of

the expected signal. Use this to search for the signal buried in noise and foregrounds. Matched Filter.

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Statistical Detection

Multi-frequency angular power spectrum

Jy2

K2

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Visibility correlations

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The Estimator

U1

U2

D/

V2 (U,)

U < D/

U >> U

Self Correlations avoided

0 ~ D

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Begum, A. et al. 2006 MNRAS, 372, L33

500 pc

80 pc

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The HI Signal

Bharadwaj, S.& Sethi, S.K. 2001, 22, 293Morales, M. F. & Hewitt, J. 2004, ApJ, 615,7; Bharadwaj, S. & Ali, Sk.S., 2005, MNRAS,356, 1519

10-7 Jy2

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Decorrelation

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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

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Visibility Correlations

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Foregrounds

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

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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, 1319Bowman, J. D. et al. 2009, ApJ, 695, 183

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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

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Foreground Removal

• The foreground contributions to V2 (U,) 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,) with a smooth polynomial in and subtract this out

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Measured Decorrelations

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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

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Ionized Bubble Detection

Datta, K.K. et al., 2007, MNRAS, 382, 809

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Signal from an HII bubble

70 Jy

Bubble at center of field of view, phase factor and fall in amplitude if shifted

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The Problem of Signal Detection

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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,)

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Matched Filter

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

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The Variance

Dominated by Foregrounds

Exceeds the Signal

Foreground Removal necessary

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The Filter

• Remove Foregrounds• Optimize Signal to Noise Ratio

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Predictions

Depends on antennasand baseline coverage

Filter effectively removes foreground

Noise reduces withobserving time

HI fluctuations outside the bubble impose a fundamental restriction on the smallest bubble that can be detected

xHI=1

Z=8.5

1000 hrs, > 22 Mpc

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2 Easy Ways to Detectthe EOR 21-cm Signal

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Thank You