Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Advanced LIGO Data
By Daniel George, E. A. Huerta. 2017
340 citations recorded by the source corpus.
Abstract The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering , was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.
Subfields: Astronomy and Astrophysics, Atmospheric Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Astronomy and Astrophysics
- Arvid Lundervold
- Enrico Camporeale
- Hongyu Shen
- L. Kim
- Tak Hur
- Daniel K. Park
- Daniel George
- E. A. Huerta
In Atmospheric Science
- Arvid Lundervold
- Enrico Camporeale
- Hongyu Shen
- L. Kim
- Tak Hur
- Daniel K. Park
- E. A. Huerta
- Daniel George
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “learning has been used for classification [9], anomaly detection [10] and”
- Glitch Classification and Clustering for LIGO with Deep Transfer Learning (2017, methodology): “Deep learning algorithms have been recently applied for GW signal detection and parameter estimation [22] as well as for denoising LIGO data [23].”
- The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting (2019, methodology): “…automatic identification of exoplanets from the Kepler catalog (Kielty et al., 2018; Pearson et al., 2017; Shallue & Vanderburg, 2018), the analysis of stellar spectra from Gaia (Fabbro et al., 2017; Li et al., 2017), and the detection of gravitational waves in LIGO signals (George & Huerta, 2018).”
- Quantum convolutional neural network for classical data classification (2021, methodology): “CNNs are also rising as a useful tool for scientific research, such as in high-energy physics (Aurisano et al. 2016; Acciarri et al. 2017), gravitational wave detection (George and Huerta 2018) and statistical physics (Tanaka and Tomiya 2017).”