Fast wavelet basis search for generic gravitational wave bursts in Pulsar Timing Array data

TL;DR

This paper introduces QuickBurst, an efficient algorithm that accelerates the search for generic gravitational-wave bursts in pulsar timing array data, making such analyses computationally feasible.

Contribution

The paper presents QuickBurst, a novel likelihood redefinition that speeds up Markov chain Monte Carlo sampling by approximately 200 times for gravitational wave burst searches.

Findings

Achieves a speedup factor of ~200 on simulated data.

Enables feasible generic burst searches with current datasets.

Reduces computational cost significantly.

Abstract

As we move into an era of more sensitive pulsar timing array data sets, we may be able to resolve individual gravitational wave sources from the stochastic gravitational wave background. While some of these sources, like orbiting massive black hole binaries, have well-defined waveform models, there could also be signals present with unknown morphology. This motivates the search for generic gravitational-wave bursts in a signal-agnostic way. However, these searches are computationally prohibitive due to the expansive parameter space. In this paper we present QuickBurst, an algorithm with a re-defined likelihood that lets us expedite Markov chain Monte Carlo sampling for a subset of the signal parameters by avoiding repeated calculations of costly inner products. This results in an overall speedup factor of $\sim$200 on realistic simulated datasets, which is sufficient to make generic gravitational-wave burst searches feasible on current and growing pulsar timing array datasets.