By classifying ten equations of state based on noisy data from several detectors, the study shows that the geometry of the network of observatories has a significant impact on the result.
The collapse of the heart of a massive star emits gravitational waves that bear the signature of ultra-dense nuclear material, whose state equation remains poorly known. A team of A. Akhmetali, Y. S. Abylkairov, J. A. Font, Mr. Zanolin and E. Abdikamalov had already shown that a network of neurons could distinguish these state equations with a single detector, for supernovae in rapid rotation. It now extends the approach to detector networks.
The position of observatories counts
Convolutive networks rank directly, among ten candidate state equations, signals drowned in the simulated noise of current and third generation observatories. The effect of sensor configuration, source distance, time of arrival, and position in the sky is examined. For a supernova in our galaxy, current networks are already getting promising results, but very dependent on the directional sensitivity of instruments.
Multiplying sensors improves average performance and homogenizes it across the sky. The future third-generation observatories would increase the scope of classification up to 100 kiloparsecs for favourable orientations, without eliminating the influence of network geometry.
Read the full article on the original source
Go to the source →