Search

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 (ANNs), support vector machines (SVMs) and constrained subspace classifiers (CSCs). The objective of this study is to compare the detection efficiency that MLAs can achieve with the efficiency of conventional detection algorithms discussed in an earlier paper. Comparisons are made using 2 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 machine learning algorithms are suitable for the detection of long-lived gravitational-wave transients and that when assuming knowledge of the distance to the source, MLAs are at least as efficient as conventional methods., Comment: Accepted for publication in Physical Review D

This paper consists of two related parts: In the first part we derive an expression of the moment of inertia (MOI) of a neutron star as a function of observables from a hypothetical r-mode gravitational wave detection. For a given r-mode detection we show how the value of the MOI of a neutron star constrains the equation of state (EOS) of the matter in the core of the neutron star. Subsequently, for each candidate EOS, we derive a possible value of the saturation amplitude, \alpha, of the r-mode oscillations on the neutron star. Additionally, we argue that a r-mode detection will provide clues about the cooling rate mechanism of the neutron star. The above physics that can be derived from a hypothetical r-mode detection constitute our motivation for the second part of the paper. In that part we present a detection strategy to efficiently search for r-modes in gravitational-wave data. R-mode signals were injected into simulated noise colored with the advanced LIGO (aLIGO) and Einstein Telescope (ET) sensitivity curves. The r-mode waveforms used are those predicted by early theories based on a polytropic equation of state (EOS) neutron star matter. In our best case scenario \alpha of order 10^{-1}, the maximum detection distance when using the aLIGO sensitivity curve is 1 Mpc (supernova event rate of 3-4 per century) while the maximum detection distance when using the ET sensitivity curve is 10 Mpc (supernova event rate of 1-2 per year).

Searches for gravitational waves (GWs) traditionally focus on persistent sources (e.g., pulsars or the stochastic background) or on transients sources (e.g., compact binary inspirals or core-collapse supernovae), which last for timescales of milliseconds to seconds. We explore the possibility of long GW transients with unknown waveforms lasting from many seconds to weeks. We propose a novel analysis technique to bridge the gap between short O(s) burst analyses and persistent stochastic analyses. Our technique utilizes frequency-time maps of GW strain cross-power between two spatially separated terrestrial GW detectors. The application of our cross-power statistic to searches for GW transients is framed as a pattern recognition problem, and we discuss several pattern-recognition techniques. We demonstrate these techniques by recovering simulated GW signals in simulated detector noise. We also recover environmental noise artifacts, thereby demonstrating a novel technique for the identification of such artifacts in GW interferometers. We compare the efficiency of this framework to other techniques such as matched filtering.