Radio Searches for Pulsars and Short-Duration Transients

I discuss methods and current software packages for radio searches for pulsars and short-duration transients. I then describe the properties of the current pulsar population and the status of and predictions for ongoing and future surveys. The presently observed pulsar population numbers around 2000 and is expected to roughly double over the next five years, with the number of millisecond pulsars expected to more than triple. Finally, I discuss individual objects discovered in the Green Bank Telescope 350-MHz Drift-Scan Survey and the Arecibo Pulsar ALFA Survey.Comment: 8 pages, 3 figures, submitted to the proceedings for the ASTRONS 2010 meetin

Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification

Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a new, two-stage approach for identifying and classifying dispersed pulse groups (DPGs) in single-pulse search output. The first stage identified DPGs and extracted features to characterize them using a new peak identification algorithm which tracks sloping tendencies around local maxima in plots of signal-to-noise ratio vs. dispersion measure. The second stage used supervised machine learning to classify DPGs. We created four benchmark data sets: one unbalanced and three balanced versions using three different imbalance treatments.We empirically evaluated 48 classifiers by training and testing binary and multiclass versions of six machine learning algorithms on each of the four benchmark versions. While each classifier had advantages and disadvantages, all classifiers with imbalance treatments had higher recall values than those with unbalanced data, regardless of the machine learning algorithm used. Based on the benchmarking results, we selected a subset of classifiers to classify the full, unlabelled data set of over 1.5 million DPGs identified in 42,405 observations made by the Green Bank Telescope. Overall, the classifiers using a multiclass ensemble tree learner in combination with two oversampling imbalance treatments were the most efficient; they identified additional known pulsars not in the benchmark data set and provided six potential discoveries, with significantly less false positives than the other classifiers.Comment: 13 pages, accepted for publication in MNRAS, ref. MN-15-1713-MJ.R

Probing fundamental physics with pulsars

Pulsars provide a wealth of information about General Relativity, the equation of state of superdense matter, relativistic particle acceleration in high magnetic fields, the Galaxy's interstellar medium and magnetic field, stellar and binary evolution, celestial mechanics, planetary physics and even cosmology. The wide variety of physical applications currently being investigated through studies of radio pulsars rely on: (i) finding interesting objects to study via large-scale and targeted surveys; (ii) high-precision timing measurements which exploit their remarkable clock-like stability. We review current surveys and the principles of pulsar timing and highlight progress made in the rotating radio transients, intermittent pulsars, tests of relativity, understanding pulsar evolution, measuring neutron star masses and the pulsar timing array.Comment: 6 pages, 1 figure, to appear in the proceedings of IAU XXVII GA - JD3 - Neutron Stars: Timing in Extreme Environments XXVII IAU General Assembly, Rio de Janeiro, Brazil, 3-14 August 200

A Study of Single Pulses in the Parkes Multibeam Pulsar Survey

We reprocessed the Parkes Multibeam Pulsar Survey, searching for single pulses out to a DM of 5000 pc cm$^{-3}$ with widths of up to one second. We recorded single pulses from 264 known pulsars and 14 Rotating Radio Transients. We produced amplitude distributions for each pulsar which we fit with log-normal distributions, power-law tails, and a power-law function divided by an exponential function, finding that some pulsars show a deviation from a log-normal distribution in the form of an excess of high-energy pulses. We found that a function consisting of a power-law divided by an exponential fit the distributions of most pulsars better than either log-normal or power-law functions. For pulsars that were detected in a periodicity search, we computed the ratio of their single-pulse signal-to-noise ratios to their signal-to-noise ratios from a Fourier transform and looked for correlations between this ratio and physical parameters of the pulsars. The only correlation found is the expected relationship between this ratio and the spin period. Fitting log-normal distributions to the amplitudes of pulses from RRATs showed similar behaviour for most RRATs. Here, however, there seem to be two distinct distributions of pulses, with the lower-energy distribution being consistent with noise. Pulse-energy distributions for two of the RRATS processed were consistent with those found for normal pulsars, suggesting that pulsars and RRATs have a common emission mechanism, but other factors influence the specific emission properties of each source class.Comment: 11 pages, 6 figures, 3 tables, accepted for publication in MNRA