A long period transient search method for the Murchison Widefield Array

TL;DR

This study develops and tests a model-subtracted, image-domain search pipeline for minute-scale radio transients with the MWA, targeting long-period transients (LPTs) in the 70–300 MHz band. It combines three time-domain filters—Spike, Time Correlated Gaussian (TCG), and RMS—with island detection, catalogue cross-matching, artefact flagging, grouping, and a human-in-the-loop classification interface to robustly identify real transients amid a sea of artefacts from ionospheric scintillation, sidelobes, and RFI. On 7099 GLEAM-X observations, the method recovered 7 real transients, including a new LPT GLEAM-X J0704−37 with $P \,=\, 2.9$ h and a DM of $\mathrm{DM} \,=\, 36.54(1) \, \mathrm{pc\,cm^{-3}}$, plus several pulsars; performance tests with injected transients show recall up to ~64% depending on band and pulse width. The results demonstrate the viability of minute-timescale LPT searches with the MWA, highlighting the key roles of scintillation-based flagging and candidate grouping, and paving the way for future ML-enhanced automated classification in larger surveys.

Abstract

We present an automated search method for radio transients on the minute timescale focused on the emerging long period transients (LPTs) in image-plane radio data. The method is tuned for use with the Murchison Widefield Array (MWA) and tested on archival observations from the GaLactic and Extragalactic All-Sky MWA Extended Survey (GLEAM-X) in the 70--300 MHz range. The images are formed from model-subtracted visibilities, before applying three filters to the time series of each pixel in an image, with each filter designed to be sensitive to a different transient behaviour. Due to the nature of radio interferometry and the refraction of the fluctuating ionosphere, the vast majority of candidates at this stage are artefacts which we identify and remove using a set of flagging measures. Of the 336 final candidates, 7 were genuine transients; 1 new LPT, 1 new pulsar, and 5 known pulsars. The performance of the method is analysed by injecting modelled transient pulses into a subset of the observations and applying the method to the result.

Figures (9)

• Figure 1: Pixel brightness distribution of a representative 154 MHz model-subtracted data cube, as well as the same data with a 4$'$ mask applied around the sources in the GLEAM catalogue. The thermal noise dominating the data is approximately normally distributed with $\sigma = \text{RMS}$. The deviation from normal above $\sim5\sigma$ is primarily due ionospheric scintillation of real radio sources.
• Figure 2: Examples of candidates which were excluded due to the invalid_majmin, scintil_dist, or scintil_corr flags alone. At left are 0.5° cutouts of the time-step of the transient cubes corresponding with the maximum brightness of the candidates. The blue cross marks the coordinates of the peak, the red and green crosses mark the two nearest known sources. At right are the lightcurves at the marked coordinates. The dark blue contour marks the flood-filled island, to which the light blue ellipse is fitted. The horizontal dotted lines mark the mean and RMS of the transient cube.
• Figure 3: Distribution of mean pixel fluence and peak pixel brightness S/N statistics against group size. The cutoff curve is drawn on both. The groups marked in red are identified as the following real sources: a. PSR J0630$-$2834; b. PSR J0031$-$57; c. PSR J0437$-$4715; d. PSR J0410$-$31; e. PSR J2048$-$1616; f. PSR J0034$-$0721; g. PSR J2241$-$5236; h. PSR J0502$-$6617; i. PSR J1244$-$1812; j. GLEAM-X J0704$-$37.
• Figure 4: Histograms of injected modelled transients which were recovered after rejecting flagged candidates, as a percentage of the total number injected to estimate the recall. The injected pulse profiles have a Gaussian shape with standard deviation $\sigma_\text{time}$ and peak pixel brightness $I_\text{peak}$.
• Figure 5: Diagnostic plot for the detection of GLEAM-X J0704$-$37. The top panel is the lightcurve of the detected island (blue), and the lightcurves of the two nearest known sources (red and green). The dashed horizontal lines are the observation mean pixel brightness (centre line) and the positive and negative RMS. In the left column are 1° cutouts from the multi-frequency synthesis (MFS) image formed during the routine GLEAM-X imaging (top), peak time-step from the model-subtracted data cube (middle), and GLEAM image cutout (bottom). The blue contour marks the candidate island, and the crosses are the nearest known sources. At the top of the right column is a histogram of the pixel values in the model-subtracted cube with the candidate peak marked in red. Below are 1° cutouts from the RMS, Spike, and TCG filter maps, with the filter that triggered the detection in bold (in this case TCG). The numbers following the filter name in the vertical labels are the peak filter value followed by the filter value divided by the upper threshold from \ref{['tab:filter_settings']} in parentheses.