Improving Pulsar Timing Precision with Single Pulse Fluence Clustering

TL;DR

This paper introduces a clustering-based method to improve pulsar timing precision by accounting for variations in single pulse morphology, leading to more accurate time of arrival measurements.

Contribution

The study presents a novel approach that classifies single pulses into different states using clustering algorithms to enhance pulsar timing accuracy.

Findings

TOA uncertainties reduced at 820 MHz and 1400 MHz bands

Clustering improves timing precision over traditional averaging methods

Method tailored for bright pulsars in NANOGrav dataset

Abstract

Traditional pulsar timing techniques involve averaging large numbers of single pulses to obtain a high signal-to-noise (S/N) profile, which is matched to a template to measure a time of arrival (TOA). However, the morphology of individual single pulses varies greatly due to pulse jitter. Pulses of different fluence contribute differently to the S/N of the pulse average. Our study proposes a method that accounts for these variations by identifying a range of states and timing each separately. We selected two 1-hour observations of PSR J2145-0750, each in a different frequency band with the Green Bank Telescope. We normalized the pulse amplitudes to account for scintillation effects and probed different excision algorithms to reduce radio-frequency interference. We then measured four pulse parameters (amplitude, position, width, and energy) to classify the single pulses using automated clustering algorithms. For each cluster, we calculated an average pulse profile and template and used both to obtain a TOA and TOA error. Finally, we computed the weighted average TOA and TOA error, the latter as a metric of the total timing precision for the epoch. The TOA is shifted relative to the one obtained without clustering, and we can estimate the shift with this weighting using the same data. For the 820 MHz and 1400 MHz bands, we obtained TOA uncertainties of 0.057 microseconds and 0.46 microseconds, compared to 0.066 microseconds and 0.74 microseconds when no clustering is applied. We conclude that tailoring this method could help improve the timing precision for certain bright pulsars in NANOGrav's dataset.