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Simultaneously search for multi-target Galactic binary gravitational waves

Pin Gao, Xilong Fan, Zhoujian Cao

Abstract

The search for Galactic binary gravitational waves is a critical challenge for future space-based gravitational wave detectors, such as LISA. We propose an innovative approach to simultaneously explore gravitational waves originating from Galactic binaries by developing a new Local Maxima Particle Swarm Optimization (LMPSO) algorithm. This new approach effectively addresses the inaccuracies often associated with signal subtraction contamination, a challenge for traditional iterative subtraction methods, particularly when dealing with low signal-to-noise ratio (SNR) signals (e.g., SNR $<$ 15). We also account for the effects of overlapping signals and degeneracy noise. To demonstrate the effectiveness of our approach, we use residuals from the LISA mock data challenge (LDC1-4), where 10,982 injected sources with SNR $\ge$ 15 have been removed. For the remaining sources with SNR $<$ 15, our method successfully identifies 6,508 signals, yielding a false alarm rate of $\text{FAS}_{0.8} = 36.8\%$. By focusing on a subset of sources-specifically, those with $f > 3$ mHz and those with $f \le 3$ mHz but SNR $\ge 13$-we identify 3,406 signals, with a reduced false alarm rate of $\text{FAS}_{0.8} = 22.5\%$. We further demonstrate that, within the same detection SNR range, our method achieves a comparable or lower $\text{FAS}$ than other existing methods.

Simultaneously search for multi-target Galactic binary gravitational waves

Abstract

The search for Galactic binary gravitational waves is a critical challenge for future space-based gravitational wave detectors, such as LISA. We propose an innovative approach to simultaneously explore gravitational waves originating from Galactic binaries by developing a new Local Maxima Particle Swarm Optimization (LMPSO) algorithm. This new approach effectively addresses the inaccuracies often associated with signal subtraction contamination, a challenge for traditional iterative subtraction methods, particularly when dealing with low signal-to-noise ratio (SNR) signals (e.g., SNR 15). We also account for the effects of overlapping signals and degeneracy noise. To demonstrate the effectiveness of our approach, we use residuals from the LISA mock data challenge (LDC1-4), where 10,982 injected sources with SNR 15 have been removed. For the remaining sources with SNR 15, our method successfully identifies 6,508 signals, yielding a false alarm rate of . By focusing on a subset of sources-specifically, those with mHz and those with mHz but SNR -we identify 3,406 signals, with a reduced false alarm rate of . We further demonstrate that, within the same detection SNR range, our method achieves a comparable or lower than other existing methods.
Paper Structure (33 sections, 26 equations, 25 figures, 6 tables)

This paper contains 33 sections, 26 equations, 25 figures, 6 tables.

Figures (25)

  • Figure 1: Galactic binaries distribution in the galactic coordinate system of LDC1-4 (SNR $>$ 7). The color bar represents the number of binaries within one square degree on the celestial sphere. The two red lines mark the area where the Galactic Latitude is between -0.5 rad and 0.5 rad ($-28.65 \degree \sim 28.65 \degree$), and there are 27,436 binaries in this area out of 27,680 binaries in total. This prior information will be used in the data analysis (See Sec. \ref{['sec:Restrict_the_Galactic_latitude']} for details).
  • Figure 2: Distribution of Galactic binaries in different frequency bins of the LDC1-4 data. Each bin has a width of 0.01 mHz. We categorize the signals into two groups based on SNR: those with SNR $>$ 15 and those with 7 $<$ SNR $<$ 15. In the figure, we observe that binaries are more concentrated at low frequencies ($f < 1 \times 10^{-3}$ Hz) and medium frequencies ($1 \times 10^{-3}$ Hz $<$$f$$<$$4 \times 10^{-3}$ Hz) when considering signals with 7 $<$ SNR $<$ 15.
  • Figure 3: The $x-y$ coordinate plane represents the ecliptic plane, with the $x$-axis directed toward the vernal equinox. LISA orbits the SSB within the ecliptic plane at a speed denoted by $\vec{\nu}$, and the origin $o$ corresponds to the SSB's position. Within the LISA data, there exists a GW signal originating from binary $A$ with parameters $f_{A}$, $\beta_A$ and $\lambda_A$. Employing a match filtering method, for detection involves assuming that the match waveform corresponds to binary $A^{\prime}$ ($f_{A^{\prime}},~\beta_{A^{\prime}},~\lambda_{A^{\prime}}$).
  • Figure 4: The parameter degeneracy from binary $A$ is derived according to Eq. \ref{['con:3']}. The parameters of binary $A$—$f_{A},~\beta_A,~\lambda_A$—are known, and the position of binary $A$ is denoted by red cross marks. In Fig. \ref{['fig:degeneracy_noise1']}, $\beta_A^{\prime}=\beta_A$, and in Fig. \ref{['fig:degeneracy_noise2']}, $\lambda_A^{\prime}=\lambda_A$. The horizontal axis represents the frequency ($f_{A^{\prime}}$) of the matched waveform, binary $A^{\prime}$, while the vertical axis represents its Ecliptic Longitude or Ecliptic Latitude ($\lambda_A^{\prime}$ or $\beta_A^{\prime}$). $\theta$ denotes the angle between LISA and binary $A$, with different $\theta$ values representing various positions of LISA relative to binary $A$.
  • Figure 5: Slices of the $\mathcal{F}$-statistic in three-dimensional parameter space for binary $A$. The parameter position of binary $A$ is marked in the subfigure (2, 5) with a red cross. The frequency intervals between two adjacent subfigures correspond to the minimum frequency resolution $\mathrm{d}f=1.59\times10^{-8}$ Hz. In each subfigure, the horizontal coordinate represents Ecliptic Longitude $\lambda$, and the vertical coordinate is Ecliptic Latitude $\beta$. The figure does not encompass the entire frequency range of the degeneracy noise distribution.
  • ...and 20 more figures