Non-parametric foreground subtraction for 21cm epoch of reionization experiments

Details Non-parametric foreground subtraction for 21cm epoch of reionization experiments Non-parametric foreground subtraction for 21cm epoch of reionization experiments

2009-03-16

arxiv.org/abs/0903.2760v2

Mon. Not. Roy. Astron. Soc. 397 (2009) 1138-1152

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

15 pages, 12 figures; lengthened and two figures added, to match version accepted by MNRAS

Scientific paper

10.1111/j.1365-2966.2009.15081.x

An obstacle to the detection of redshifted 21cm emission from the epoch of reionization (EoR) is the presence of foregrounds which exceed the cosmological signal in intensity by orders of magnitude. We argue that in principle it would be better to fit the foregrounds non-parametrically - allowing the data to determine their shape - rather than selecting some functional form in advance and then fitting its parameters. Non-parametric fits often suffer from other problems, however. We discuss these before suggesting a non-parametric method, Wp smoothing, which seems to avoid some of them. After outlining the principles of Wp smoothing we describe an algorithm used to implement it. We then apply Wp smoothing to a synthetic data cube for the LOFAR EoR experiment. The performance of Wp smoothing, measured by the extent to which it is able to recover the variance of the cosmological signal and to which it avoids leakage of power from the foregrounds, is compared to that of a parametric fit, and to another non-parametric method (smoothing splines). We find that Wp smoothing is superior to smoothing splines for our application, and is competitive with parametric methods even though in the latter case we may choose the functional form of the fit with advance knowledge of the simulated foregrounds. Finally, we discuss how the quality of the fit is affected by the frequency resolution and range, by the characteristics of the cosmological signal and by edge effects.

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