2011
DOI: 10.1111/j.1365-2966.2011.18208.x
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The GMRT Epoch of Reionization experiment: a new upper limit on the neutral hydrogen power spectrum at z≈ 8.6

Abstract: We present a new upper limit to the 21‐cm power spectrum during the Epoch of Reionization (EoR) which constrains reionization models with an unheated IGM. The GMRT‐EoR experiment is an ongoing effort to make a statistical detection of the power spectrum of 21‐cm neutral hydrogen emission at redshift z∼ 9. Data from this redshift constrain models of the EoR, the end of the Dark Ages arising from the formation of the first bright UV sources, probably stars or mini‐quasars. We present results from approximately 5… Show more

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Cited by 187 publications
(184 citation statements)
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“…The predicted 2σ upper limit in the absence of the a celestial signal is shown in dashed cyan, assuming T K 500 sys = . The triangles indicate 2 σ upper limits from GMRT (Paciga et al 2011) (yellow) at z = 8.6, MWA ) at z = 9.5 (magenta), and the previous PAPER upper limit (P14) at z = 7.7 (green). The magenta curve shows a predicted model 21 cm power spectrum at 50% ionization (Lidz et al 2008). the error bars on the spherically averaged power spectrum P(k), where positive and negative k  measurements are kept separate for diagnostic purposes.…”
Section: Power Spectrum Constraintsmentioning
confidence: 99%
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“…The predicted 2σ upper limit in the absence of the a celestial signal is shown in dashed cyan, assuming T K 500 sys = . The triangles indicate 2 σ upper limits from GMRT (Paciga et al 2011) (yellow) at z = 8.6, MWA ) at z = 9.5 (magenta), and the previous PAPER upper limit (P14) at z = 7.7 (green). The magenta curve shows a predicted model 21 cm power spectrum at 50% ionization (Lidz et al 2008). the error bars on the spherically averaged power spectrum P(k), where positive and negative k  measurements are kept separate for diagnostic purposes.…”
Section: Power Spectrum Constraintsmentioning
confidence: 99%
“…We take the conservative approach and do not correct for this effect, noting that the leakage of Q in to I will result in positive power, increasing our limits. Foreground removal techniques discussed in the literature include spectral polynomial fitting (Wang et al 2006;Bowman et al 2009a;Liu et al 2009a), principal component analysis (Liu & Tegmark 2011;Paciga et al 2011;Masui et al 2013;Paciga et al 2013), non-parametric subtractions (Harker et al 2009;Chapman et al 2013), and inverse covariance weighting (Liu & Tegmark 2011;Dillon et al 2013Dillon et al , 2014Liu et al 2014aLiu et al , 2014b, Fourier-mode filtering Petrovic & Oh (2011), and per-baseline delay filtering described in P12b. This delay-spectrum filtering technique is well-suited to the maximum redundancy PAPER configuration, which is not optimized for the other approaches where high fidelity imaging is a prerequisite.…”
Section: Wideband Delay Filteringmentioning
confidence: 99%
“…Pen et al (2009) have carried out 150 MHz GMRT observations at a high Galactic latitude to place an upper limit of 2 C /2π < 3K on the polarized foregrounds at < 1000. Paciga et al (2011) placed an upper limit on the 21-cm power spectrum during the EoR of (70 mK) 2 at wavenumbers of k = 0.65 h/Mpc which after a more careful handling of the foreground subtraction had to be increased to (248 mK) 2 for k = 0.50 h/Mpc (Paciga et al 2013). This shows how proper foreground removal remains a challenge.…”
Section: Selected Results From Ska Precursors and Pathfindersmentioning
confidence: 99%
“…We then subtract the wide-band deconvolved foreground model from the measured visibilities to suppress the scattering of foreground emission off of unsmooth sampling weights relating to RFI flagging. In essence, delay-space CLEAN is a direct analog of the image-domain modeling proposed for foreground removal (Paciga et al 2011;Datta et al 2009). The difference here is that it is acting per-baseline, and that, in addition to selecting CLEAN components in delay bins that map to imagedomain coordinates, delay-space CLEAN is also restricted to models that produce smooth spectral responses.…”
Section: Reducing Sidelobes Of Foregrounds Arising From Frequency-dommentioning
confidence: 99%