The UTMOST Survey for Magnetars, Intermittent pulsars, RRATs and FRBs I: System description and overview

We describe the ongoing `Survey for Magnetars, Intermittent pulsars, Rotating radio transients and Fast radio bursts' (SMIRF), performed using the newly refurbished UTMOST telescope. SMIRF repeatedly sweeps the southern Galactic plane performing real-time periodicity and single-pulse searches, and is the first survey of its kind carried out with an interferometer. SMIRF is facilitated by a robotic scheduler which is capable of fully autonomous commensal operations. We report on the SMIRF observational parameters, the data analysis methods, the survey's sensitivities to pulsars, techniques to mitigate radio frequency interference and present some early survey results. UTMOST's wide field of view permits a full sweep of the Galactic plane to be performed every fortnight, two orders of magnitude faster than previous surveys. In the six months of operations from January to June 2018, we have performed $\sim 10$ sweeps of the Galactic plane with SMIRF. Notable blind re-detections include the magnetar PSR J1622$-$4950, the RRAT PSR J0941$-$3942 and the eclipsing pulsar PSR J1748$-$2446A. We also report the discovery of a new pulsar, PSR J1705$-$54. Our follow-up of this pulsar with the UTMOST and Parkes telescopes at an average flux limit of $\leq 20$ mJy and $\leq 0.16$ mJy respectively, categorizes this as an intermittent pulsar with a high nulling fraction of $< 0.002$Comment: Submitted to MNRAS, comments welcom

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

Forecasting with time series imaging

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset

Solar feature tracking in both spatial and temporal domains

A new method for automated coronal loop tracking, in both spatial and temporal domains, is presented. The reliability of this technique was tested with TRACE 171A observations. The application of this technique to a flare-induced kink-mode oscillation, revealed a 3500 km spatial periodicity which occur along the loop edge. We establish a reduction in oscillatory power, for these spatial periodicities, of 45% over a 322 s interval. We relate the reduction in oscillatory power to the physical damping of these loop-top oscillations

The first INTEGRAL-OMC catalogue of optically variable sources

The Optical Monitoring Camera (OMC) onboard INTEGRAL provides photometry in the Johnson V-band. With an aperture of 50 mm and a field of view of 5deg x 5deg, OMC is able to detect optical sources brighter than V~18, from a previously selected list of potential targets of interest. After more than nine years of observations, the OMC database contains light curves for more than 70000 sources (with more than 50 photometric points each). The objectives of this work have been to characterize the potential variability of the objects monitored by OMC, to identify periodic sources and to compute their periods, taking advantage of the stability and long monitoring time of the OMC. To detect potential variability, we have performed a chi-squared test, finding 5263 variable sources out of an initial sample of 6071 objects with good photometric quality and more than 300 data points each. We have studied the periodicity of these sources using a method based on the phase dispersion minimization technique, optimized to handle light curves with very different shapes.In this first catalogue of variable sources observed by OMC, we provide for each object the median of the visual magnitude, the magnitude at maximum and minimum brightness in the light curve during the window of observations, the period, when found, as well as the complete intrinsic and period-folded light curves, together with some additional ancillary data.Comment: Accepted by Astronomy & Astrophysics; 13 pages, 16 figures. Figures' resolution has been degraded to fit astro-ph constraint