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Free keywords:
Astrophysics, Solar and Stellar Astrophysics, astro-ph.SR, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM,General Relativity and Quantum Cosmology, gr-qc
Abstract:
The early inspiral from stellar-mass binary black holes can emit milli-Hertz
gravitational wave signals, making them detectable sources for space-borne
gravitational wave missions like TianQin. However, the traditional matched
filtering technique poses a significant challenge for analyzing this kind of
signals, as it requires an impractically high number of templates ranging from
$10^{31}$ to $10^{40}$. We propose a search strategy that involves two main
parts: initially, we reduce the dimensionality of the simulated signals using
incremental principal component analysis (IPCA). Subsequently we train the
convolutional neural networks (CNNs) based on the compressed TianQin data
obtained from IPCA, aiming to develop both a detection model and a point
parameter estimation model. The compression efficiency for the trained IPCA
model achieves a cumulative variance ratio of 95.6% when applied to $10^6$
simulated signals. To evaluate the performance of CNN we generate the receiver
operating characteristic curve for the detection model which is applied to the
test data with varying signal-to-noise ratios. At a false alarm probability of
5% the corresponding true alarm probability for signals with a signal-to-noise
ratio of 50 is 86.5%. Subsequently, we introduce the point estimation model to
evaluate the value of the chirp mass of corresponding sBBH signals with an
error. For signals with a signal-to-noise ratio of 50, the trained point
estimation CNN model can estimate the chirp mass of most test events, with a
standard deviation error of 2.48 $M_{\odot}$ and a relative error precision of
0.12.
