REVIEW 1 cited by
Deep multi-redshift limits on Epoch of Reionisation 21cm Power Spectra from Four Seasons of Murchison Widefield Array Observations
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Deep multi-redshift limits on Epoch of Reionisation 21cm Power Spectra from Four Seasons of Murchison Widefield Array Observations
read the original abstract
We compute the spherically-averaged power spectrum from four seasons of data obtained for the Epoch of Reionisation (EoR) project observed with the Murchison Widefield Array (MWA). We measure the EoR power spectrum over $k= 0.07-3.0~h$Mpc$^{-1}$ at redshifts $z=6.5-8.7$. The largest aggregation of 110 hours on EoR0 high-band (3,340 observations), yields a lowest measurement of (43~mK)$^2$ = 1.8$\times$10$^3$ mK$^2$ at $k$=0.14~$h$Mpc$^{-1}$ and $z=6.5$ (2$\sigma$ thermal noise plus sample variance). Using the Real-Time System to calibrate and the CHIPS pipeline to estimate power spectra, we select the best observations from the central five pointings within the 2013--2016 observing seasons, observing three independent fields and in two frequency bands. This yields 13,591 2-minute snapshots (453 hours), based on a quality assurance metric that measures ionospheric activity. We perform another cut to remove poorly-calibrated data, based on power in the foreground-dominated and EoR-dominated regions of the two-dimensional power spectrum, reducing the set to 12,569 observations (419 hours). These data are processed in groups of 20 observations, to retain the capacity to identify poor data, and used to analyse the evolution and structure of the data over field, frequency, and data quality. We subsequently choose the cleanest 8,935 observations (298 hours of data) to form integrated power spectra over the different fields, pointings and redshift ranges.
Forward citations
Cited by 1 Pith paper
-
Mitigating residual foregrounds and systematic errors in SKA1-Low AA* EoR observations via Bayesian Gaussian Process Regression
Bayesian GPR recovers the 21cm signal within 2σ credible intervals for most k-modes (0.06 to 1.0 h/Mpc) in SKA1-Low simulations that include realistic residual foregrounds and systematics.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.