Resolving multiple supermassive black hole binaries with pulsar timing arrays II: genetic algorithm implementation

TL;DR

This paper presents a genetic algorithm-based method to detect and resolve multiple supermassive black hole binaries in pulsar timing array data, achieving accurate parameter estimation and source localization in simulated noisy datasets.

Contribution

It extends a maximum-likelihood method with a genetic algorithm to efficiently identify and characterize multiple MBH binaries in PTA data, improving detection capabilities.

Findings

Successfully recovered all injected sources in simulations

Estimated source parameters within few percent accuracy

Achieved sky localization within a few degrees

Abstract

Pulsar timing arrays (PTAs) might detect gravitational waves (GWs) from massive black hole (MBH) binaries within this decade. The signal is expected to be an incoherent superposition of several nearly-monochromatic waves of different strength. The brightest sources might be individually resolved, and the overall deconvolved, at least partially, in its individual components. In this paper we extend the maximum-likelihood based method developed in Babak & Sesana 2012, to search for individual MBH binaries in PTA data. We model the signal as a collection of circular monochromatic binaries, each characterized by three free parameters: two angles defining the sky location, and the frequency. We marginalize over all other source parameters and we apply an efficient multi-search genetic algorithm to maximize the likelihood function and look for sources in synthetic datasets. On datasets characterized by white Gaussian noise plus few injected sources with signal-to-noise ratio (SNR) in the range 10-60, our search algorithm performs well, recovering all the injections with no false positives. Individual source SNRs are estimated within few % of the injected values, sky locations are recovered within few degrees, and frequencies are determined with sub-Fourier bin precision.