Fast resolving Galactic binaries in LISA data and its ability to study the Milky Way

Abstract

Resolving individual gravitational waves from tens of millions of double white dwarf (DWD) binaries in the Milky Way is a challenge for future space-based gravitational wave detection programs. By using previous data to define the priors for the next search, we propose an accelerated approach of searching the DWD binaries and demonstrate its efficiency based on the GBSIEVER detection pipeline. Compared to the traditional GBSIEVER method, our method can obtain $\sim 50\%$ of sources with 2.5\% of the searching time for LDC1-4 data. In addition, we find that both methods have a similar ability to detect the Milky Way structure by their confirmed sources. The relative error of distance and chirp mass is about 20\% for DWD binaries whose gravitational wave frequency is higher than $4\times 10^{-3}$ Hz, even if they are close to the Galactic center. Finally, we propose a signal-to-noise ratio (SNR) threshold for LISA to confirm the detection of DWD binaries. The threshold should be 16 when the gravitational wave frequency is lower than $4\times 10^{-3}$ Hz and 9 when the frequency range is from $4\times 10^{-3}$ Hz to $1.5\times 10^{-2}$ Hz.

16 pages, 19 figures

References in corpus (13)

• Gravitational-wave sensitivity curves
• The Evolution of Compact Binary Star Systems
• The TianQin project: current progress on science and technology
• The First Round Result from the TianQin-1 Satellite
• The Mock LISA Data Challenges: from Challenge 3 to Challenge 4
• Global Analysis of the Gravitational Wave Signal from Galactic Binaries
• The LISA verification binaries
• An overview of the second round of the Mock LISA Data Challenges
• LISACode : A scientific simulator of LISA
• Report on the second Mock LISA Data Challenge
• F-statistic search for white-dwarf binaries in the first Mock LISA Data Challenge
• Extracting galactic binary signals from the first round of Mock LISA Data Challenges
• Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets