Efficiency of nonspinning templates in gravitational wave searches for aligned-spin binary black holes

TL;DR

This study evaluates how well nonspinning waveform templates detect aligned-spin binary black hole signals, revealing biases and limitations in parameter estimation especially for certain spin ranges.

Contribution

It provides a detailed analysis of the effectiveness of nonspinning templates for aligned-spin BBH signals using the PhenomD model, highlighting detection thresholds and parameter biases.

Findings

Negative spins yield higher fitting factors than positive spins.

Detection efficiency depends on mass ratio and spin, with thresholds met only for certain conditions.

Systematic biases significantly affect recovered parameters, impacting parameter estimation accuracy.

Abstract

We study the efficiency of nonspinning waveform templates in gravitational wave searches for aligned-spin binary black holes (BBHs). We use PhenomD, which is the most recent phenomenological waveform model designed to generate the full inspiral-merger-ringdown waveforms emitted from BBHs with the spins aligned with the orbital angular momentum. Here, we treat the effect of aligned-spins with a single spin parameter $\chi$. We consider the BBH signals with moderately small spins in the range of $-0.4\leq \chi \leq 0.4$. Using nonspinning templates, we calculate fitting factors of the aligned-spin signals in a wide mass range up to $\sim 100 M_{\odot}$. We find that the signals with negative spins can have higher fitting factors than those with positive spins. If $\chi = 0.3$, only the highly asymmetric-mass signals can have the fitting factors exceeding the threshold of 0.965, while the fitting factors for all of the signals can be larger than the threshold if $\chi = -0.3$. We demonstrate that the discrepancy between the regions of a positive and a negative spins is due to the physical boundary ($\eta \leq 0.25$) of the template parameter space. We also show that the recovered mass parameters can be significantly biased from the true parameters. We examine the impact of the systematic bias on the parameter estimation by comparing the bias with the statistical error.