相关论文: Classifying Complex Faraday Spectra with Convoluti…

Next-generation radio surveys will yield an unprecedented amount of data, warranting analysis by use of machine learning techniques. Convolutional neural networks are the deep learning technique that has proven to be the most successful in…

Deep radio observations at 5 and 8 GHz are presented of a complete sample of 11 radio galaxies with redshifts 0.85 < z < 1.5, selected from the 6C sample of Eales. The radio data, taken using the VLA in A, B and C array configurations,…

Convolutional Neural Networks (CNNs) have recently led to incredible breakthroughs on a variety of pattern recognition problems. Banks of finite impulse response filters are learned on a hierarchy of layers, each contributing more abstract…

Strong singly-ionized magnesium (MgII) absorption lines in quasar spectra typically serve as a proxy for intervening galaxies along the line of sight. Previous studies have found a correlation between the number of these MgII absorbers and…

Convolutional neural network (CNN) is one of the most widely-used successful architectures in the era of deep learning. However, the high-computational cost of CNN still hampers more universal uses to light devices. Fortunately, the Fourier…

Benefit from large-scale training datasets, deep Convolutional Neural Networks(CNNs) have achieved impressive results in face recognition(FR). However, tremendous scale of datasets inevitably lead to noisy data, which obviously reduce the…

We extend the rotation measure work of Burn (1966) to the cases of limited sampling of lambda squared space and non-constant emission spectra. We introduce the rotation measure transfer function (RMTF), which is an excellent predictor of…

This paper presents a data processing algorithm with machine learning for polarization extraction and event selection applied to photoelectron track images taken with X-ray polarimeters. The method uses a convolutional neural network (CNN)…

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very…

A primordial cosmological magnetic field induces Faraday rotation of the cosmic microwave background polarization. This rotation produces a curl-type polarization component even when the unrotated polarization possesses only gradient-type…

Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio galaxies. Different classes of radio galaxies can be used as tracers of the cosmic…

Magnetic fields play a crucial role in various astrophysical processes within the intracluster medium, including heat conduction, cosmic ray acceleration, and the generation of synchrotron radiation. However, measuring magnetic field…

Single-particle trajectories measured in microscopy experiments contain important information about dynamic processes undergoing in a range of materials including living cells and tissues. However, extracting that information is not a…

Aims - We report new Very Large Array polarimetric observations of Compact Steep-Spectrum (CSS) sources at 8.4, 15, and 23GHz. Methods - Using multi-frequency VLA observations we have derived sub-arcsecond resolution images of the total…

The forthcoming new-generation radio telescope SKA (Square Kilometre Array) and its precursors will provide a rapidly growing number of polarized radio sources. Our analysis aims on what can be learned from these sources concerning the…

We present polarization and Faraday rotation for the supernova remnants (SNRs) G46.8-0.3, G43.3-0.2, G41.1-0.3, and G39.2-0.3 in L-band (1-2 GHz) radio continuum in The HI/OH/Recombination line (THOR) survey. We detect polarization from…

Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional…

We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract…

We investigate whether the method of wavelet-based Faraday rotation measure (RM) Synthesis can help us to identify structures of regular and turbulent magnetic fields in extended magnetized objects, such as galaxies and galaxy clusters.…

Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the…