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Found 6 papers, 0 papers with code
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds
In this work, we study TpopT (TemPlate OPTimization) as an alternative scalable framework for detecting low-dimensional families of signals which maintains high interpretability.
Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks
Various approaches have been proposed for improving the efficiency of the detection scheme, with hierarchical matched filtering being an important strategy.
Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually.
Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets
As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of observations.
Generalized Approach to Matched Filtering using Neural Networks
Moreover, we show that the proposed neural network architecture can outperform matched filtering, both with or without knowledge of a prior on the parameter distribution.
How to search for multiple messengers -- a general framework beyond two messengers
Quantification of the significance of a candidate multi-messenger detection of cosmic events is an emerging need in the astrophysics and astronomy communities.
High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics
