Abstract
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.
Highlights
The advent of instruments that have large fields of view in relatively unexplored frequency domains has boosted the interest for blind transient searches (Shin et al, 2009; Bannister et al, 2011; Bower et al, 2011; Thyagarajan et al, 2011; Hoffman et al, 2012; Franzen et al, 2014; Bell et al, 2014; Ivezić et al, 2019; Kuiack et al, 2020b; Villar et al, 2021)
Much work has been done on detecting fast radio transients (Cordes and McLaughlin, 2003; Lorimer et al, 2013; Coenen et al, 2014; Amiri et al, 2018) that occur on millisecond time scales
One of the key features of fast radio transients is the dispersion of the observed emission in time and frequency, in which emission at lower radio frequencies arrive later in time than the emission at higher radio frequencies (Taylor and Cordes, 1993)
Summary
The advent of instruments that have large fields of view in relatively unexplored frequency domains has boosted the interest for blind transient searches (Shin et al, 2009; Bannister et al, 2011; Bower et al, 2011; Thyagarajan et al, 2011; Hoffman et al, 2012; Franzen et al, 2014; Bell et al, 2014; Ivezić et al, 2019; Kuiack et al, 2020b; Villar et al, 2021). Tingay et al (2015) piloted a search for dispersed fast radio bursts in high time and frequency resolution imaging data covering a 400 square degree field of view obtained using the MWA. As an answer to the aforementioned, we develop a pipeline that considers the spatial, time, and frequency domain simultaneously and can detect dispersed transients of intermediate length. It scales to real-time analysis by its ability to run on GPUs. Five sequential steps are performed: quality control, source detection, source association, flux measurement, and physical parameter inference. We propose an end-to-end GPU-accelerated pipeline that can take streaming multi-frequency image data and output alerts in real time It contains source detection, tracking (i.e., association) and analysis..
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