Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFaceting for direction-dependent spectral deconvolution
127
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The new generation of radio interferometers is characterized by high sensitivity, wide fields of view and large fractional bandwidth. To synthesize the deepest images enabled by the high dynamic range of these instruments requires us to take into account the direction-dependent Jones matrices, while estimating the spectral properties of the sky in the imaging and deconvolution algorithms. In this paper we discuss and implement a wide-band wide-field spectral deconvolution framework (DDFacet) based on image plane faceting, that takes into account generic direction-dependent effects. Specifically, we present a wide-field co-planar faceting scheme, and discuss the various effects that need to be taken into account to solve for the deconvolution problem (image plane normalization, position-dependent PSF, etc). We discuss two wide-band spectral deconvolution algorithms based on hybrid matching pursuit and sub-space optimisation respectively. A few interesting technical features incorporated in our imager are discussed, including baseline dependent averaging, which has the effect of improving computing efficiency. The version of DDFacet presented here can account for any externally defined Jones matrices and/or beam patterns.
rate research
Read More
Astronomical imaging using aperture synthesis telescopes requires deconvolution of the point spread function as well as calibration of instrumental and atmospheric effects. In general, such effects are time-variable and vary across the field of view as well, resulting in direction-dependent (DD), time-varying gains. Most existing imaging and calibration algorithms assume that the corruptions are direction independent, preventing even moderate dynamic range full-beam, full-Stokes imaging. We present a general framework for imaging algorithms which incorporate DD errors. We describe as well an iterative deconvolution algorithm that corrects known DD errors due to the antenna power patterns and pointing errors for high dynamic range full-beam polarimetric imaging. Using simulations we demonstrate that errors due to realistic primary beams as well as antenna pointing errors will limit the dynamic range of upcoming higher sensitivity instruments and that our new algorithm can be used to correct for such errors. We have applied this algorithm to VLA 1.4 GHz observations of a field that contains two ``4C sources and have obtained Stokes-I and -V images with systematic errors that are one order of magnitude lower than those obtained with conventional imaging tools. Our simulations show that on data with no other calibration errors, the algorithm corrects pointing errors as well as errors due to known asymmetries in the antenna pattern.
One of the main goals of modern observational cosmology is to map the large scale structure of the Universe. A potentially powerful approach for doing this would be to exploit three-dimensional spectral maps, i.e. the specific intensity of extragalactic light as a function of wavelength and direction on the sky, to measure spatial variations in the total extragalactic light emission and use these as a tracer of the clustering of matter. A main challenge is that the observed intensity as a function of wavelength is a convolution of the source luminosity density with the rest-frame spectral energy distribution. In this paper, we introduce the method of spectral deconvolution as a way to invert this convolution and extract the clustering information. We show how one can use observations of the mean and angular fluctuations of extragalactic light as a function of wavelength, assuming statistical isotropy, to reconstruct jointly the rest-frame spectral energy distribution of the sources and the source spatial density fluctuations. This method is more general than the well known line mapping technique as it does not rely on spectral lines in the emitted spectra. After introducing the general formalism, we discuss its implementation and limitations. This formal paper sets the stage for future more practical studies.
The ambitious scientific goals of the SKA require a matching capability for calibration of atmospheric propagation errors, which contaminate the observed signals. We demonstrate a scheme for correcting the direction-dependent ionospheric and instrumental phase effects at the low frequencies and with the wide fields of view planned for SKA-Low. It leverages bandwidth smearing, to filter-out signals from off-axis directions, allowing the measurement of the direction-dependent antenna-based gains in the visibility domain; by doing this towards multiple directions it is possible to calibrate across wide fields of view. This strategy removes the need for a global sky model, therefore all directions are independent. We use MWA results at 88 and 154 MHz under various weather conditions to characterise the performance and applicability of the technique. We conclude that this method is suitable to measure and correct for temporal fluctuations and direction-dependent spatial ionospheric phase distortions on a wide range of scales: both larger and smaller than the array size. The latter are the most intractable and pose a major challenge for future instruments. Moreover this scheme is an embarrassingly parallel process, as multiple directions can be processed independently and simultaneously. This is an important consideration for the SKA, where the current planned architecture is one of compute-islands with limited interconnects. Current implementation of the algorithm and on-going developments are discussed.
X-ray spectral fitting of astronomical sources requires convolving the intrinsic spectrum or model with the instrumental response. Standard forward modeling techniques have proven success in recovering the underlying physical parameters in moderate to high signal-to-noise regimes; however, they struggle to achieve the same level of accuracy in low signal-to-noise regimes. Additionally, the use of machine learning techniques on X-ray spectra requires access to the intrinsic spectrum. Therefore, the measured spectrum must be effectively deconvolved from the instrumental response. In this note, we explore numerical methods for inverting the matrix equation describing X-ray spectral convolution. We demonstrate that traditional methods are insufficient to recover the intrinsic X-ray spectrum and argue that a novel approach is required.
We consider the nonparametric estimation of the density function of weakly and strongly dependent processes with noisy observations. We show that in the ordinary smooth case the optimal bandwidth choice can be influenced by long range dependence, as opposite to the standard case, when no noise is present. In particular, if the dependence is moderate the bandwidth, the rates of mean-square convergence and, additionally, central limit theorem are the same as in the i.i.d. case. If the dependence is strong enough, then the bandwidth choice is influenced by the strength of dependence, which is different when compared to the non-noisy case. Also, central limit theorem are influenced by the strength of dependence. On the other hand, if the density is supersmooth, then long range dependence has no effect at all on the optimal bandwidth choice.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
