21 research outputs found
Revival of the magnetar PSR J1622-4950: observations with MeerKAT, Parkes, XMM-Newton, Swift, Chandra, and NuSTAR
New radio (MeerKAT and Parkes) and X-ray (XMM-Newton, Swift, Chandra, and
NuSTAR) observations of PSR J1622-4950 indicate that the magnetar, in a
quiescent state since at least early 2015, reactivated between 2017 March 19
and April 5. The radio flux density, while variable, is approximately 100x
larger than during its dormant state. The X-ray flux one month after
reactivation was at least 800x larger than during quiescence, and has been
decaying exponentially on a 111+/-19 day timescale. This high-flux state,
together with a radio-derived rotational ephemeris, enabled for the first time
the detection of X-ray pulsations for this magnetar. At 5%, the 0.3-6 keV
pulsed fraction is comparable to the smallest observed for magnetars. The
overall pulsar geometry inferred from polarized radio emission appears to be
broadly consistent with that determined 6-8 years earlier. However, rotating
vector model fits suggest that we are now seeing radio emission from a
different location in the magnetosphere than previously. This indicates a novel
way in which radio emission from magnetars can differ from that of ordinary
pulsars. The torque on the neutron star is varying rapidly and unsteadily, as
is common for magnetars following outburst, having changed by a factor of 7
within six months of reactivation.Comment: Published in ApJ (2018 April 5); 13 pages, 4 figure
Automated Detection of Antenna Malfunctions in Large-N Interferometers: A case study With the Hydrogen Epoch of Reionization Array
We present a framework for identifying and flagging malfunctioning antennas in large radio
interferometers. We outline two distinct categories of metrics designed to detect outliers along known failure
modes of large arrays: cross-correlation metrics, based on all antenna pairs, and auto-correlation metrics, based
solely on individual antennas. We define and motivate the statistical framework for all metrics used, and present
tailored visualizations that aid us in clearly identifying new and existing systematics. We implement these
techniques using data from 105 antennas in the Hydrogen Epoch of Reionization Array (HERA) as a case study.
Finally, we provide a detailed algorithm for implementing these metrics as flagging tools on real data sets
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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
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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
Characterization of inpaint residuals in interferometric measurements of the epoch of reionization
To mitigate the effects of Radio Frequency Interference (RFI) on the data analysis pipelines of 21 cm interferometric instruments, numerous inpaint techniques have been developed. In this paper, we examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that is capable of inpainting RFI corrupted data. We train our network on simulated data and show that our network is capable of inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modelling are best suited for inpainting over narrowband RFI. We show that with our fiducial parameters discrete prolate spheroidal sequences (DPSS) and CLEAN provide the best performance for intermittent RFI while Gaussian progress regression (GPR) and least squares spectral analysis (LSSA) provide the best performance for larger RFI gaps. However, we caution that these qualitative conclusions are sensitive to the chosen hyperparameters of each inpainting technique. We show that all inpainting techniques reliably reproduce foreground dominated modes in the power spectrum. Since the inpainting techniques should not be capable of reproducing noise realizations, we find that the largest errors occur in the noise dominated delay modes. We show that as the noise level of the data comes down, CLEAN and DPSS are most capable of reproducing the fine frequency structure in the visibilities
Automated Detection of Antenna Malfunctions in Large-N Interferometers: A Case Study With the Hydrogen Epoch of Reionization Array
We present a framework for identifying and flagging malfunctioning antennas in large radio interferometers. We outline two distinct categories of metrics designed to detect outliers along known failure modes of large arrays: cross-correlation metrics, based on all antenna pairs, and auto-correlation metrics, based solely on individual antennas. We define and motivate the statistical framework for all metrics used, and present tailored visualizations that aid us in clearly identifying new and existing systematics. We implement these techniques using data from 105 antennas in the Hydrogen Epoch of Reionization Array (HERA) as a case study. Finally, we provide a detailed algorithm for implementing these metrics as flagging tools on real data sets
Impact of instrument and data characteristics in the interferometric reconstruction of the 21 cm power spectrum
Combining the visibilities measured by an interferometer to form a cosmological power spectrum is a complicated process. In a delay-based analysis, the mapping between instrumental and cosmological space is not a one-to-one relation. Instead, neighbouring modes contribute to the power measured at one point, with their respective contributions encoded in the window functions. To better understand the power measured by an interferometer, we assess the impact of instrument characteristics and analysis choices on these window functions. Focusing on the Hydrogen Epoch of Reionization Array (HERA) as a case study, we find that long-baseline observations correspond to enhanced low-k tails of the window functions, which facilitate foreground leakage, whilst an informed choice of bandwidth and frequency taper can reduce said tails. With simple test cases and realistic simulations, we show that, apart from tracing mode mixing, the window functions help accurately reconstruct the power spectrum estimator of simulated visibilities. The window functions depend strongly on the beam chromaticity and less on its spatial structure – a Gaussian approximation, ignoring side lobes, is sufficient. Finally, we investigate the potential of asymmetric window functions, down-weighting the contribution of low-k power to avoid foreground leakage. The window functions presented here correspond to the latest HERA upper limits for the full Phase I data. They allow an accurate reconstruction of the power spectrum measured by the instrument and will be used in future analyses to confront theoretical models and data directly in cylindrical space
Search for the Epoch of Reionisation with HERA: Upper Limits on the Closure Phase Delay Power Spectrum
Radio interferometers aiming to measure the power spectrum of the redshifted
21 cm line during the Epoch of Reionisation (EoR) need to achieve an
unprecedented dynamic range to separate the weak signal from overwhelming
foreground emissions. Calibration inaccuracies can compromise the sensitivity
of these measurements to the effect that a detection of the EoR is precluded.
An alternative to standard analysis techniques makes use of the closure phase,
which allows one to bypass antenna-based direction-independent calibration.
Similarly to standard approaches, we use a delay spectrum technique to search
for the EoR signal. Using 94 nights of data observed with Phase I of the
Hydrogen Epoch of Reionization Array (HERA), we place approximate constraints
on the 21 cm power spectrum at . We find at 95% confidence that the 21
cm EoR brightness temperature is (372) "pseudo" mK at 1.14
"pseudo" Mpc, where the "pseudo" emphasises that these limits are to
be interpreted as approximations to the actual distance scales and brightness
temperatures. Using a fiducial EoR model, we demonstrate the feasibility of
detecting the EoR with the full array. Compared to standard methods, the
closure phase processing is relatively simple, thereby providing an important
independent check on results derived using visibility intensities, or related.Comment: 16 pages, 14 figures, accepted for publication by MNRA
Revival of the Magnetar PSR J1622-4950: Observations with MeerKAT, Parkes, XMM-Newton, Swift, Chandra, and NuSTAR
New radio (MeerKAT and Parkes) and X-ray (XMM-Newton, Swift, Chandra, and NuSTAR) observations of PSR J1622-4950 indicate that the magnetar, in a quiescent state since at least early 2015, reactivated between 2017 March 19 and April 5. The radio flux density, while variable, is approximately 100 larger than during its dormant state. The X-ray flux one month after reactivation was at least 800 larger than during quiescence, and has been decaying exponentially on a 111 19 day timescale. This high-flux state, together with a radio-derived rotational ephemeris, enabled for the first time the detection of X-ray pulsations for this magnetar. At 5%, the 0.3-6 keV pulsed fraction is comparable to the smallest observed for magnetars. The overall pulsar geometry inferred from polarized radio emission appears to be broadly consistent with that determined 6-8 years earlier. However, rotating vector model fits suggest that we are now seeing radio emission from a different location in the magnetosphere than previously. This indicates a novel way in which radio emission from magnetars can differ from that of ordinary pulsars. The torque on the neutron star is varying rapidly and unsteadily, as is common for magnetars following outburst, having changed by a factor of 7 within six months of reactivation
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 k ≤ 0.2 h cMpc−1) and a baseline-dependent periodic signal with a period of ∼1 MHz (dominating over k ∼ 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 MH