A real-time Automated Glitch Detection Pipeline at Ooty Radio Telescope

TL;DR

This paper presents a real-time automated glitch detection pipeline for pulsars at the Ooty Radio Telescope, utilizing algorithms like MAD and polynomial regression to improve detection accuracy and enable timely follow-up observations.

Contribution

The paper introduces a novel real-time glitch detection pipeline optimized with simulations, demonstrating polynomial regression's effectiveness over MAD for pulsar glitch detection.

Findings

Polynomial regression outperforms MAD in real-time detection accuracy.

The pipeline successfully detected known glitches in test data.

Implementation at Ooty Radio Telescope enables prompt glitch alerts.

Abstract

Glitches are the observational manifestations of superfluidity inside neutron stars. The aim of this paper is to describe an automated glitch detection pipeline, which can alert the observers on possible real-time detection of rotational glitches in pulsars. Post alert, the pulsars can be monitored at a higher cadence to measure the post-glitch recovery phase. Two algorithms namely, Median Absolute Deviation (MAD) and polynomial regression have been explored to detect glitches in real time. The pipeline has been optimized with the help of simulated timing residuals for both the algorithms. Based on the simulations, we conclude that the polynomial regression algorithm is significantly more effective for real time glitch detection. The pipeline has been tested on a few published glitches. This pipeline is presently implemented at the Ooty Radio Telescope. In the era of upcoming large telescopes like SKA, several hundreds of pulsars will be observed regularly and such a tool will be useful for both real-time detection as well as optimal utilization of observation time for such glitching pulsars.