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Register a new userAll Transients, All the Time: Real-Time Radio Transient Detection with Interferometric Closure Quantities
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We demonstrate a new technique for detecting radio transients based on interferometric closure quantities. The technique uses the bispectrum, the product of visibilities around a closed-loop of baselines of an interferometer. The bispectrum is calibration independent, resistant to interference, and computationally efficient, so it can be built into correlators for real-time transient detection. Our technique could find celestial transients anywhere in the field of view and localize them to arcsecond precision. At the Karl G. Jansky Very Large Array (VLA), such a system would have a high survey speed and a 5-sigma sensitivity of 38 mJy on 10 ms timescales with 1 GHz of bandwidth. The ability to localize dispersed millisecond pulses to arcsecond precision in large volumes of interferometer data has several unique science applications. Localizing individual pulses from Galactic pulsars will help find X-ray counterparts that define their physical properties, while finding host galaxies of extragalactic transients will measure the electron density of the intergalactic medium with a single dispersed pulse. Exoplanets and active stars have distinct millisecond variability that can be used to identify them and probe their magnetospheres. We use millisecond time scale visibilities from the Allen Telescope Array (ATA) and VLA to show that the bispectrum can detect dispersed pulses and reject local interference. The computational and data efficiency of the bispectrum will help find transients on a range of time scales with next-generation radio interferometers.
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The future of radio astronomy will require instruments with large collecting areas for higher sensitivity, wide fields of view for faster survey speeds, and efficient computing and data rates relative to current capabilities. We describe the first successful deployment of the E-field Parallel Imaging Correlator (EPIC) on the LWA station in Sevilleta, New Mexico, USA (LWA-SV). EPIC is a solution to the computational problem of large interferometers. By gridding and spatially Fourier transforming channelised electric fields from the antennas in real-time, EPIC removes the explicit cross multiplication of all pairs of antenna voltages to synthesize an aperture, reducing the computational scaling from $mathcal{O}(n_a^2)$ to $mathcal{O}(n_g log_2 n_g)$, where $n_a$ is the number of antennas and $n_g$ is the number of grid points. Not only does this save computational costs for dense arrays but it produces very high time resolution images in real time. The GPU-based implementation uses existing LWA-SV hardware and the high performance streaming framework, Bifrost. We examine the practical details of the EPIC deployment and verify the imaging performance by detecting a meteor impact on the atmosphere using continuous all-sky imaging at 50 ms time resolution.
An apparatus to search for optical flashes in the sky is described. It has been optimized for gamma ray bursts (GRB) optical counterparts. It consists of 2x16 cameras covering all the sky. The sky is monitored continuously and the data are analysed on-line. It has self-triggering capability and can react to external triggers with negative delay. The prototype with two cameras has been installed at Las Campanas (Chile) and is operational from July 2004. The paper presents general idea and describes the apparatus in detail. Performance of the prototype is briefly reviewed and perspectives for the future are outlined.
We present a methodology for automated real-time analysis of a radio image data stream with the goal to find transient sources. Contrary to previous works, the transients we are interested in occur on a time-scale where dispersion starts to play a role, so we must search a higher-dimensional data space and yet work fast enough to keep up with the data stream in real time. The approach consists of five main steps: quality control, source detection, association, flux measurement, and physical parameter inference. We present parallelized methods based on convolutions and filters that can be accelerated on a GPU, allowing the pipeline to run in real-time. In the parameter inference step, we apply a convolutional neural network to dynamic spectra that were obtained from the preceding steps. It infers physical parameters, among which the dispersion measure of the transient candidate. Based on critical values of these parameters, an alert can be sent out and data will be saved for further investigation. Experimentally, the pipeline is applied to simulated data and images from AARTFAAC (Amsterdam Astron Radio Transients Facility And Analysis Centre), a transients facility based on the Low-Frequency Array (LOFAR). Results on simulated data show the efficacy of the pipeline, and from real data it discovered dispersed pulses. The current work targets transients on time scales that are longer than the fast transients of beam-formed search, but shorter than slow transients in which dispersion matters less. This fills a methodological gap that is relevant for the upcoming Square-Kilometer Array (SKA). Additionally, since real-time analysis can be performed, only data with promising detections can be saved to disk, providing a solution to the big-data problem that modern astronomy is dealing with.
Fast variability of optical objects is an interesting though poorly explored subject in modern astronomy. Real-time data processing and identification of transient celestial events in the images is very important for such study as it allows rapid follow-up with more sensitive instruments. We discuss an approach which we have developed for the RAPTOR project, a pioneering closed-loop system combining real-time transient detection with rapid follow-up. RAPTORs data processing pipeline is able to identify and localize an optical transient within seconds after the observation. The testing we performed so far have been confirming the effectiveness of our method for the optical transient detection. The software pipeline we have developed for RAPTOR can easily be applied to the data from other experiments.
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.
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