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Compact binaries in our galaxy are expected to be one of the main sources ofgravitational waves for the future eLISA mission. During the mission lifetime,many thousands of galactic binaries should be individually resolved. However,the identification of the sources, and the extraction of the signal parametersin a noisy environment are real challenges for data analysis. So far,stochastic searches have proven to be the most successful for this problem. Inthis work we present the first application of a swarm-based algorithm combiningParticle Swarm Optimization and Differential Evolution. These algorithms havebeen shown to converge faster to global solutions on complicated likelihoodsurfaces than other stochastic methods. We first demonstrate the effectivenessof the algorithm for the case of a single binary in a 1 mHz search bandwidth.This interesting problem gave the algorithm plenty of opportunity to fail, asit can be easier to find a strong noise peak rather than the signal itself.After a successful detection of a fictitious low-frequency source, as well asthe verification binary RXJ0806.3+1527, we then applied the algorithm to thedetection of multiple binaries, over different search bandwidths, in the casesof low and mild source confusion. In all cases, we show that we cansuccessfully identify the sources, and recover the true parameters within a99\% credible interval.

Detecting compact galactic binaries using a hybrid swarm-based algorithm