Sensitivity study using machine learning algorithms on simulated<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>r</mml:mi></mml:math>-mode gravitational wave signals from newborn neutron stars

This is a follow-up sensitivity study on $r$-mode gravitational wave signals from newborn neutron stars illustrating the applicability of machine learning algorithms for the detection of long-lived gravitational wave transients. In this sensitivity study, we examine three machine learning algorithms (MLAs): artificial neural networks, support vector machines, and constrained subspace classifiers. The objective of this study is to compare the detection efficiencies that MLAs can achieve to the efficiency of the conventional (seedless clustering) detection algorithm discussed in an earlier paper. Comparisons are made using two distinct $r$-mode waveforms. For the training of the MLAs, we assumed that some information about the distance to the source is given so that the training was performed over distance ranges not wider than half an order of magnitude. The results of this study suggest that we can use the machine learning algorithms as part of an investigative stage in the pipeline that would be able to provide very fast and solid triggers for further, and more intense, investigation.