An implementation of Galactic white dwarf binary data analysis for MLDC-3.1

TL;DR

This paper presents an efficient data analysis method for detecting double white dwarf binaries in gravitational wave data, combining coarse and fine search algorithms to handle large datasets effectively.

Contribution

The paper introduces an iterative combinatorial algorithm with novel data reduction and template bank techniques for rapid detection of white dwarf binaries in MLDC-3.1 data.

Findings

Detected approximately 10,000 double white dwarf signals

Validated the feasibility of the proposed analysis method

Achieved significant reduction in computation time

Abstract

The space-borne gravitational wave detectors will observe a large population of double white dwarf binaries in the Milky Way. However, the search for double white dwarfs in the gravitational wave data will be time-consuming due to the large number of templates involved and antenna response calculation. In this paper, we implement an iterative combinatorial algorithm to search for double white dwarfs in MLDC-3.1 data. To quickly determine the rough parameters of the target sources, the following algorithms are adopted in a coarse search process: (1) using the downsampling method to reduce the number of original data points; (2) using the undersampling method to speed up the generation of a single waveform template; (3) using the stochastic template bank method to quickly construct the waveform template bank while achieving high coverage of the parameter space; (4) Combining the FFT acceleration algorithm with the stochastic template bank to reduce the calculation time of a single template. A fine search process is applied to further determine the parameters of the signals based on the coarse search, for which we adopt the particle swarm optimization. Finally, we detected $\mathcal{O}(10^4)$ double white dwarf signals, validating the feasibility of our method.