# Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks

arXiv: https://arxiv.org/abs/2207.11583 · HTML: https://mlsanity.com/paper/2207.11583/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 106 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data](https://mlsanity.com/paper/1711.03121/) (2017)
- [Deep Neural Networks to Enable Real-time Multimessenger Astrophysics](https://mlsanity.com/paper/1701.00008/) (2016)
- [Gravitational-wave parameter estimation with autoregressive neural network flows](https://mlsanity.com/paper/2002.07656/) (2020)
- [Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization](https://mlsanity.com/paper/2010.12931/) (2020)
- [Convolutional neural networks: a magic bullet for gravitational-wave detection?](https://mlsanity.com/paper/1904.08693/) (2019)
- [Image-based deep learning for classification of noise transients in gravitational wave detectors](https://mlsanity.com/paper/1803.09933/) (2018)
- [Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders](https://mlsanity.com/paper/1903.03105/) (2019)
- [Detection of Gravitational Waves Using Bayesian Neural Networks](https://mlsanity.com/paper/2007.04176/) (2020)
- [Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO](https://mlsanity.com/paper/1706.07446/) (2017)
- [Generalized Approach to Matched Filtering using Neural Networks](https://mlsanity.com/paper/2104.03961/) (2021)
- [Improved deep learning techniques in gravitational-wave data analysis](https://mlsanity.com/paper/2011.04418/) (2020)
- [Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets](https://mlsanity.com/paper/2202.13486/) (2022)
- [Detection of gravitational waves using topological data analysis and convolutional neural network: An improved approach](https://mlsanity.com/paper/1910.08245/) (2019)
- [Gravitational Wave Detection and Information Extraction via Neural Networks](https://mlsanity.com/paper/2003.09995/) (2020)
- [Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning](https://mlsanity.com/paper/2203.05086/) (2022)

## Built on by (most cited first, 1 of 1)

- [TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds](https://mlsanity.com/paper/2310.10039/) (2023)

## Abstract (arXiv)

Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have been proposed for improving the efficiency of the detection scheme, with hierarchical matched filtering being an important strategy. Meanwhile, deep learning methods have recently demonstrated both consistency with matched filtering methods and remarkable statistical performance. In this work, we propose Hierarchical Detection Network (HDN), a novel approach to efficient detection that combines ideas from hierarchical matching and deep learning. The network is trained using a novel loss function, which encodes simultaneously the goals of statistical accuracy and efficiency. We discuss the source of complexity reduction of the proposed model, and describe a general recipe for initialization with each layer specializing in different regions. We demonstrate the performance of HDN with experiments using open LIGO data and synthetic injections, and observe with two-layer models a 79% efficiency gain compared with matched filtering at an equal error rate of 0.2%. Furthermore, we show how training a three-layer HDN initialized using two-layer model can further boost both accuracy and efficiency, highlighting the power of multiple simple layers in efficient detection.