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Register a new userAdaptive Filters Revisited - RFI Mitigation in pulsar observations
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Pulsar detection and timing experiments are applications where adaptive filters seem eminently suitable tools for radio-frequency-interference (RFI) mitigation. We describe a novel variant which works well in field trials of pulsar observations centred on an observing frequency of 675 MHz, a bandwidth of 64 MHz and with 2-bit sampling. Adaptive filters have generally received bad press for RFI mitigation in radio astronomical observations with their most serious drawback being a spectral echo of the RFI embedded in the filtered signals. Pulsar observations are intrinsically less sensitive to this as they operate in the (pulsar period) time domain. The field trials have allowed us to identify those issues which limit the effectiveness of the adaptive filter. We conclude that adaptive filters can significantly improve pulsar observations in the presence of RFI.
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RFI mitigation in Radioastronomy can be achieved adopting cryogenic filters in appropriate typologies. A study has been conducted in L, C and X band with the evaluation of the filter architecture in copper, with theoretical estimation, computer simulations, prototypes realization, laboratory measurements. Such work has been preliminary to the realization of HTS samples with the purpose of a similar complete characterization approach.
In a search for short timescale astrophysical transients in time-domain data, radio-frequency interference (RFI) causes both large quantities of false positive candidates and a significant reduction in sensitivity if not correctly mitigated. Here we propose an algorithm that infers a time-variable frequency channel mask directly from short-duration ($sim$1 s) data blocks: the method consists of computing a spectral statistic that correlates well with the presence of RFI, and then finding high outliers among the resulting values. For the latter task, we propose an outlier detection algorithm called Inter-Quartile Range Mitigation (IQRM), that is both non-parametric and robust to the presence of a trend in sequential data. The method requires no training and can in principle adapt to any telescope and RFI environment; its efficiency is shown on data from both the MeerKAT and Lovell 76-m radio telescopes. IQRM is fast enough to be used in a streaming search and has been integrated into the MeerTRAP real-time transient search pipeline. Open-source Python and C++ implementations are provided.
Detection and mitigation of radio frequency interference (RFI) is the first and also the key step for data processing in radio observations, especially for ongoing low frequency radio experiments towards the detection of the cosmic dawn and epoch of reionization (EoR). In this paper we demonstrate the technique and efficiency of RFI identification and mitigation for the 21 Centimeter Array (21CMA), a radio interferometer dedicated to the statistical measurement of EoR. For terrestrial, man-made RFI, we concentrate mainly on a statistical approach by identifying and then excising non-Gaussian signatures, in the sense that the extremely weak cosmic signal is actually buried under thermal and therefore Gaussian noise. We also introduce the so-called visibility correlation coefficient instead of conventional visibility, which allows a further suppression of rapidly time-varying RFI. Finally, we briefly discuss removals of the sky RFI, the leakage of sidelobes from off-field strong radio sources with time-invariant power and a featureless spectrum. It turns out that state of the art technique should allow us to detect and mitigate RFI to a satisfactory level in present low frequency interferometer observations such as those acquired with the 21CMA, and the accuracy and efficiency can be greatly improved with the employment of low-cost, high-speed computing facilities for data acquisition and processing.
We present a global kinetic plasma simulation of an axisymmetric pulsar magnetosphere with self-consistent $e^pm$ pair production. We use the particle-in-cell method and log-spherical coordinates with a grid size $4096times 4096$. This allows us to achieve a high voltage induced by the pulsar rotation and investigate pair creation in a young pulsar far from the death line. We find the following. (1) The energy release and $e^pm$ creation are strongly concentrated in the thin, Y-shaped current sheet, with a peak localized in a small volume at the Y-point. (2) The Y-point is shifted inward from the light cylinder by $sim 15%$, and breathes with a small amplitude. (3) The dense $e^pm$ cloud at the Y-point is in ultra-relativistic rotation, which we call super-rotation, because it exceeds co-rotation with the star. The cloud receives angular momentum flowing from the star along the poloidal magnetic lines. (4) Gamma-ray emission peaks at the Y-point and is collimated in the azimuthal direction, tangent to the Y-point circle. (5) The separatrix current sheet between the closed magnetosphere and the open magnetic field lines is sustained by the electron backflow from the Y-point cloud. Its thickness is self-regulated to marginal charge starvation. (6) Only a small fraction of dissipation occurs in the separatrix inward of the Y-point. A much higher power is released in the equatorial plane, especially at the Y-point where the created dense $e^pm$ plasma is spun up and intermittently ejected through the nozzle between the two open magnetic fluxes.
Popular network pruning algorithms reduce redundant information by optimizing hand-crafted models, and may cause suboptimal performance and long time in selecting filters. We innovatively introduce adaptive exemplar filters to simplify the algorithm design, resulting in an automatic and efficient pruning approach called EPruner. Inspired by the face recognition community, we use a message passing algorithm Affinity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved filters. EPruner breaks the dependency on the training data in determining the important filters and allows the CPU implementation in seconds, an order of magnitude faster than GPU based SOTAs. Moreover, we show that the weights of exemplars provide a better initialization for the fine-tuning. On VGGNet-16, EPruner achieves a 76.34%-FLOPs reduction by removing 88.80% parameters, with 0.06% accuracy improvement on CIFAR-10. In ResNet-152, EPruner achieves a 65.12%-FLOPs reduction by removing 64.18% parameters, with only 0.71% top-5 accuracy loss on ILSVRC-2012. Our code can be available at https://github.com/lmbxmu/EPruner.
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