Sensitivity study using machine learning algorithms on simulated r-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 (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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSensitivitySimilar Papers 제목 키워드 기반
A Study of Left Before Treatment Complete Emergency Department Patients: An Optimized Explanatory Machine Learning Framework
The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause pat…
feature selectionHyperparameter OptimizationMetaheuristic OptimizationSpecificitySensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning
The abundance of dark matter (DM) subhalos orbiting a host galaxy is a generic prediction of the cosmological framework, and is a promising way to constrain the nature of DM. In this paper, we investigate the use of mach…
Anomaly DetectionSensitivityMachine learning for automated quality control in injection moulding manufacturing
Machine learning (ML) may improve and automate quality control (QC) in injection moulding manufacturing. As the labelling of extensive, real-world process data is costly, however, the use of simulated process data may of…
BIG-bench Machine LearningSensitivitySpecificityValidation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysis
Some popular Machine Learning Uncertainty Quantification (ML-UQ) calibration statistics do not have predefined reference values and are mostly used in comparative studies. In consequence, calibration is almost never vali…
DiagnosticSensitivityUncertainty QuantificationPractical Recommendations for the Design of Automatic Fault Detection Algorithms Based on Experiments with Field Monitoring Data
Automatic fault detection (AFD) is a key technology to optimize the Operation and Maintenance of photovoltaic (PV) systems portfolios. A very common approach to detect faults in PV systems is based on the comparison betw…
ClusteringFault DetectionSensitivitySpecificity