Artificial neural networks for selection of pulsar candidates from the radio continuum surveys

Pulsar search with time-domain observation is very computationally expensive and data volume will be enormous with the next generation telescopes such as the Square Kilometre Array. We apply artificial neural networks (ANNs), a machine learning method, for efficient selection of pulsar candidates from radio continuum surveys, which are much cheaper than time-domain observation. With observed quantities such as radio fluxes, sky position and compactness as inputs, our ANNs output the "score" that indicates the degree of likeliness of an object to be a pulsar. We demonstrate ANNs based on existing survey data by the TIFR GMRT Sky Survey (TGSS) and the NRAO VLA Sky Survey (NVSS) and test their performance. Precision, which is the ratio of the number of pulsars classified correctly as pulsars to that of any objects classified as pulsars, is about 96$\%$. Finally, we apply the trained ANNs to unidentified radio sources and our fiducial ANN with five inputs (the galactic longitude and latitude, the TGSS and NVSS fluxes and compactness) generates 2,436 pulsar candidates from 456,866 unidentified radio sources. These candidates need to be confirmed if they are truly pulsars by time-domain observations. More information such as polarization will narrow the candidates down further.Comment: 11 pages, 13 figures, 3 tables, accepted for publication in MNRA

An Iterative Reconstruction Algorithm for Faraday Tomography

Faraday tomography offers crucial information on the magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters, by observing its magnetoionic media. The observed linear polarization spectrum is inverse Fourier transformed to obtain the Faraday dispersion function (FDF), providing us a tomographic distribution of the magnetoionic media along the line of sight. However, this transform gives a poor reconstruction of the FDF because of the instrument's limited wavelength coverage. The current Faraday tomography techniques' inability to reliably solve the above inverse problem has noticeably plagued cosmic magnetism studies. We propose a new algorithm inspired by the well-studied area of signal restoration, called the Constraining and Restoring iterative Algorithm for Faraday Tomography (CRAFT). This iterative model-independent algorithm is computationally inexpensive and only requires weak physically-motivated assumptions to produce high fidelity FDF reconstructions. We demonstrate an application for a realistic synthetic model FDF of the Milky Way, where CRAFT shows greater potential over other popular model-independent techniques. The dependence of observational frequency coverage on the various techniques' reconstruction performance is also demonstrated for a simpler FDF. CRAFT exhibits improvements even over model-dependent techniques (i.e., QU-fitting) by capturing complex multi-scale features of the FDF amplitude and polarization angle variations within a source. The proposed approach will be of utmost importance for future cosmic magnetism studies, especially with broadband polarization data from the Square Kilometre Array and its precursors. We make the CRAFT code publicly available.Comment: Accepted for publication in MNRAS. 13 pages and 12 figure

Faraday dispersion functions of galaxies

The Faraday dispersion function (FDF), which can be derived from an observed polarization spectrum by Faraday rotation measure synthesis, is a profile of polarized emissions as a function of Faraday depth. We study intrinsic FDFs along sight lines through face-on Milky Way like galaxies by means of a sophisticated galactic model incorporating three-dimensional MHD turbulence, and investigate how much information the FDF intrinsically contains. Since the FDF reflects distributions of thermal and cosmic-ray electrons as well as magnetic fields, it has been expected that the FDF could be a new probe to examine internal structures of galaxies. We, however, find that an intrinsic FDF along a sight line through a galaxy is very complicated, depending significantly on actual configurations of turbulence. We perform 800 realizations of turbulence and find no universal shape of the FDF even if we fix the global parameters of the model. We calculate the probability distribution functions of the standard deviation, skewness, and kurtosis of FDFs and compare them for models with different global parameters. Our models predict that the presence of vertical magnetic fields and the large-scale height of cosmic-ray electrons tend to make the standard deviation relatively large. In contrast, the differences in skewness and kurtosis are relatively less significant.open0

Statistical methods for the analysis of rotation measure grids in large scale structures in the SKA era

To better understand the origin and properties of cosmological magnetic fields, a detailed knowledge of magnetic fields in the large-scale structure of the Universe (galaxy clusters, filaments) is crucial. We propose a new statistical approach to study magnetic fields on large scales with the rotation measure grid data that will be obtained with the new generation of radio interferometers.Comment: 9 pages; to appear as part of 'Cosmic Magnetism' in Proceedings 'Advancing Astrophysics with the SKA (AASKA14)', PoS(AASKA14)11