12 research outputs found
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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
Upper limits on the 21 cm epoch of reionization power spectrum from one night with LOFAR
We present the first limits on the Epoch of Reionization 21 cm H i power spectra, in the redshift range z = 7.9–10.6, using the Low-Frequency Array (LOFAR) High-Band Antenna (HBA). In total, 13.0 hr of data were used from observations centered on the North Celestial Pole. After subtraction of the sky model and the noise bias, we detect a non-zero (1-σ) excess variance and a best 2-σ upper limit of at k = 0.053 h cMpc−1 in the range z = 9.6–10.6. The excess variance decreases when optimizing the smoothness of the direction- and frequency-dependent gain calibration, and with increasing the completeness of the sky model. It is likely caused by (i) residual side-lobe noise on calibration baselines, (ii) leverage due to nonlinear effects, (iii) noise and ionosphere-induced gain errors, or a combination thereof. Further analyses of the excess variance will be discussed in forthcoming publications
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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
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Hydrogen Epoch of Reionization Array (HERA) Phase II Deployment and Commissioning
Funder: New Frontiers in Research Fund Exploration grant programFunder: Canadian Institute for Advanced Research (CIFAR) Azrieli Global Scholars programFunder: Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant and a Discovery Launch SupplementFunder: Sloan Research FellowshipFunder: William Dawson ScholarshipAbstract
This paper presents the design and deployment of the Hydrogen Epoch of Reionization Array (HERA) phase II system. HERA is designed as a staged experiment targeting 21 cm emission measurements of the Epoch of Reionization. First results from the phase I array are published as of early 2022, and deployment of the phase II system is nearing completion. We describe the design of the phase II system and discuss progress on commissioning and future upgrades. As HERA is a designated Square Kilometre Array pathfinder instrument, we also show a number of “case studies” that investigate systematics seen while commissioning the phase II system, which may be of use in the design and operation of future arrays. Common pathologies are likely to manifest in similar ways across instruments, and many of these sources of contamination can be mitigated once the source is identified.</jats:p