5 research outputs found
Recommended from our members
Foreground modelling via Gaussian process regression: An application to HERA data
The key challenge in the observation of the redshifted 21-cm signal from
cosmic reionization is its separation from the much brighter foreground
emission. Such separation relies on the different spectral properties of the
two components, although, in real life, the foreground intrinsic spectrum is
often corrupted by the instrumental response, inducing systematic effects that
can further jeopardize the measurement of the 21-cm signal. In this paper, we
use Gaussian Process Regression to model both foreground emission and
instrumental systematics in hours of data from the Hydrogen Epoch of
Reionization Array. We find that a simple co-variance model with three
components matches the data well, giving a residual power spectrum with white
noise properties. These consist of an "intrinsic" and instrumentally corrupted
component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating
the line of sight power spectrum over scales h
cMpc) and a baseline dependent periodic signal with a period of
MHz (dominating over h cMpc) which should
be distinguishable from the 21-cm EoR signal whose typical coherence-scales is
MHz
Recommended from our members
Foreground modelling via Gaussian process regression: An application to HERA data
The key challenge in the observation of the redshifted 21-cm signal from
cosmic reionization is its separation from the much brighter foreground
emission. Such separation relies on the different spectral properties of the
two components, although, in real life, the foreground intrinsic spectrum is
often corrupted by the instrumental response, inducing systematic effects that
can further jeopardize the measurement of the 21-cm signal. In this paper, we
use Gaussian Process Regression to model both foreground emission and
instrumental systematics in hours of data from the Hydrogen Epoch of
Reionization Array. We find that a simple co-variance model with three
components matches the data well, giving a residual power spectrum with white
noise properties. These consist of an "intrinsic" and instrumentally corrupted
component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating
the line of sight power spectrum over scales h
cMpc) and a baseline dependent periodic signal with a period of
MHz (dominating over h cMpc) which should
be distinguishable from the 21-cm EoR signal whose typical coherence-scales is
MHz
The HERA-19 Commissioning Array: Direction-dependent Effects
Foreground power dominates the measurements of interferometers that seek a
statistical detection of highly-redshifted HI emission from the Epoch of
Reionization (EoR). The chromaticity of the instrument creates a boundary in
the Fourier transform of frequency (proportional to ) between
spectrally smooth emission, characteristic of the strong synchrotron foreground
(the "wedge"), and the spectrally structured emission from HI in the EoR (the
"EoR window"). Faraday rotation can inject spectral structure into otherwise
smooth polarized foreground emission, which through instrument effects or
miscalibration could possibly pollute the EoR window. Using data from the HERA
19-element commissioning array, we investigate the polarization response of
this new instrument in the power spectrum domain. We perform a simple
image-based calibration based on the unpolarized diffuse emission of the Global
Sky Model, and show that it achieves qualitative redundancy between the
nominally-redundant baselines of the array and reasonable amplitude accuracy.
We construct power spectra of all fully polarized coherencies in all
pseudo-Stokes parameters. We compare to simulations based on an unpolarized
diffuse sky model and detailed electromagnetic simulations of the dish and
feed, confirming that in Stokes I, the calibration does not add significant
spectral structure beyond the expected level. Further, this calibration is
stable over the 8 days of observations considered. Excess power is seen in the
power spectra of the linear polarization Stokes parameters which is not easily
attributable to leakage via the primary beam, and results from some combination
of residual calibration errors and actual polarized emission. Stokes V is found
to be highly discrepant from the expectation of zero power, strongly pointing
to the need for more accurate polarized calibration
Recommended from our members
Foreground modelling via Gaussian process regression: An application to HERA data
The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in ∼2 h of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an 'intrinsic' and instrumentally corrupted component with a coherence scale of 20 and 2.4 MHz, respectively (dominating the line-of-sight power spectrum over scales kâ ≤ 0.2 h cMpc-1) and a baseline-dependent periodic signal with a period of ∼1 MHz (dominating over kâ ∼0.4-0.8 h cMpc-1), which should be distinguishable from the 21-cm Epoch of Reionization signal whose typical coherence scale is ∼0.8 MHz