Microlensing bias on the detection of strong lensing gravitational wave

TL;DR

This paper investigates how stellar microlensing within strong lensing galaxies affects the detection and parameter estimation of strong lensing gravitational wave events, revealing significant biases and reduced detection efficiency.

Contribution

It demonstrates that wave optical effects from microlensing can bias parameter estimation and decrease detection efficiency of SLGW events by up to 50%.

Findings

Detection efficiency drops from 10% to 5% at FAP of 10^-5.

Approximately 30% of events show a 1σ parameter bias.

Microlensing effects have minimal impact on extrinsic parameters.

Abstract

Identifying strong lensing gravitational wave (SLGW) events is of utmost importance in astrophysics as we approach the historic first detection of SLGW amidst the growing number of gravitational wave (GW) events. Currently, one crucial method for identifying SLGW signals involves assessing the overlap of parameters between two GWs. However, the distribution of discrete matter, such as stars and sub-halos, within the strong lensing galaxy can imprint a wave optical (WO) effect on the SLGW waveform. These frequency dependent imprints introduce biases in parameter estimation and impact SLGW identification. In this study, we assess the influence of the stellar microlensing field embedded in a strong lensing galaxy. Our finding demonstrate that the WO effect reduces the detection efficiency of SLGW by $5\%\sim 50\%$ for various false alarm probabilities per pair (${\rm FAP}_{\rm per~pair}$). Specifically, at an ${\rm FAP}_{\rm per~pair}$ of $10^{-5}$, the detection efficiency decreases from $\sim 10\%$ to $\sim 5\%$. Consequently, the presence of the microlensing field can result in missing half of the strong lensing candidates. Additionally, the microlensing WO effect introduces a noticeable bias in intrinsic parameters, particularly for chirp mass and mass ratio. However, it has tiny influence on extrinsic parameters. Considering all parameters, $\sim 30\%$ of events exhibit a $1\sigma$ parameter bias, $\sim 12\%$ exhibit a $2\sigma$ parameter bias, and $\sim 5\%$ exhibit a $3\sigma$ parameter bias.