International Astronomy and Astrophysics Research Journal

Deep Learning Techniques to Make Gravitational Wave Detections from Weak Time-series Data

Yash Chauhan *

The International School of Bangalore (TISB), Bangalore, India.

*Author to whom correspondence should be addressed.

---

Abstract

Gravitational waves are ripples in the space time fabric when high energy events such as black hole mergers or neutron star collisions take place. The first Gravitational Wave (GW) detection (GW150914) was made by the Laser Interferometer Gravitational-wave Observatory (LIGO) and Virgo Collaboration on September 14, 2015. Furthermore, the proof of the existence of GWs had countless implications from Stellar Evolution to General Relativity. Gravitational waves detection requires multiple filters and the filtered data has to be studied intensively to come to conclusions on whether the data is a just a glitch or an actual gravitational wave detection. However, with the use of Deep Learning the process is simplified heavily, as it reduces the level of filtering greatly, and the output is more definitive, even though the model produces a probabilistic result. Our technique, Deep Learning, utilizes a different implementation of a one-dimensional convolutional neural network (CNN). The model is trained by a composite of real LIGO noise, and injections of GW waveform templates. The CNN effectively uses classification to differentiate weak GW time series from non-gaussian noise from glitches in the LIGO data stream. In addition, we are the first study to utilize fine-tuning as a means to train the model with a second pass of data, while maintaining all the learned features from the initial training iteration. This enables our model to have a sensitivity of 100%, higher than all prior studies in this field, when making real-time detections of GWs at an extremely low Signal-to-noise ratios (SNR), while still being less computationally expensive. This sensitivity, in part, is also achieved through the use of deep signal manifolds from both the Hanford and Livingston detectors, which enable the neural network to be responsive to false positives.

Keywords: Deep learning, neural networks, convolutional neural networks, gravitational waves, LIGO, classification, time-series, astronomical data analysis

---

---

---

References

Einstein. Königlich Preussische Akademie der Wissenschaften Zu Berlin, Sitzungsberichte. 1915;844.

Sengupta, Dhurandhar, Lazzarini. Faster Implementation of the Hierarchical Search Algorithm for Detection of Gravitational Waves from Inspiraling Compact Binaries. Physical Review. 2003;67.8.

Razzano, Massimiliano, Cuoco. Image-Based Deep Learning for Classification of Noise Transients in Gravitational Wave Detectors. Classical and Quantum Gravity. 2018;35.9.

Calzavarini, et al. Matched Filters for Coalescing Binaries Detection on Massively Parallel Computers. Computer Physics Communications. 2003;152.3: 295–306.

Gayathri, et al. Astrophysical Signal Consistency Test Adapted for Gravitational-Wave Transient Searches. Physical Review D. 2019;100:12.

Goodfellow, Bengio, Courville. Deep Learning (MIT Press); 2016.

Samsing, MacLeod, Ramirez-Ruiz. Astrophysical Journal. 2014;784:71.

Rampone, Salvatore et al. Neural Network Aided Glitch-Burst Discrimination and Glitch Classification. International Journal of Modern Physics C. 2013;24:11.

Chua, Vallisneri. Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference. Physical Review Letters. 2020;124.4.

George, Huerta. Deep Learning for Real-Time Gravitational Wave Detection and in Estimation: Results with Advanced LIGO Data. Physics Letters B. 2018;778.

George, Shen, Huerta. Classification and Unsupervised Clustering of LIGO Data with Deep Transfer Learning. Physical Review D. 2018;97:10.

Aasi, et al. Enhanced Sensitivity of the LIGO Gravitational Wave Detector by Using Squeezed States of Light, Nature Photonics. 2013;7:8.

Schmidhuber. Neural Networks. 2015; 61:85.

Belczynski, Holz, Bulik, O’Shaughnessy Nature. 2016;534:512.

Krastev. Real-Time Detection of Gravitational Waves from Binary Neutron Stars Using Artificial Neural Networks. Physics Letters B. 2020;803.

Rebei et al. Fusing Numerical Relativity and Deep Learning to Detect Higher-Order Multipole Waveforms from Eccentric Binary Black Hole Mergers. Physical Review D. 2019;100:4.

The LIGO Scientific Collaboration and The Virgo Collaboration. ArXiv e-prints, ar Xiv:1710.05832 [grqc]; 2017.

Roulet et al. Template Bank for Compact Binary Coalescence Searches in Gravitational Wave Data: A General Geometric Placement Algorithm. Physical Review D. 2019;99.12.

Sengupta. The LIGO Scientific Collaboration. LIGO-Virgo Searches for Gravitational Waves from Coalescing Binaries: A Status Update. Journal of Physics: Conference Series. 2010;228.

Wen, et al. GRID: A Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era. Experimental Astronomy. 2019;48:1.

Zevin, et al. Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science. Classical and Quantum Gravity. 2017;34:6.

Soares-Santos, et al. First Measurement of the Hubble Constant from a Dark Standard Siren Using the Dark Energy Survey Galaxies and the LIGO/Virgo Binary–Black-Hole Merger GW170814. The Astrophysical Journal. 2019;876:1.

Suvorova, et al. H-idden Markov Model Tracking of Continuous Gravitational Waves from a Neutron Star with Wandering Spin. Physical Review D. 2016;93:12.

The LIGO Scientific Collaboration, Aasi, et al. Classical and Quantum Gravity. 2015; 32:074001.

Venumadhav, et al. New Search Pipeline for Compact Binary Mergers: Results for Binary Black Holes in the First Observing Run of Advanced LIGO. Physical Review D. 2019;100.2.

Venumadhav et al. A Practical Theorem on Using Interferometry to Measure the Global 21 cm Signal. The Astrophysical Journal. 2016;826:2.

Beheshtipour, Papa. Deep Learning for Clustering of Continuous Gravitational Wave Candidates. Physical Review D. 2020;101:6.

Abbott et al. All-Sky Search for Continuous Gravitational Waves from Isolated Neutron Stars Using Advanced LIGO O2 Data. Physical Review D. 2019;100:2.

Aasi et al. Improved Upper Limits on the Stochastic Gravitational-Wave Background from 2009–2010 LIGO and Virgo Data. Physical Review Letters. 2014;113:23.

Rodriguez, Morscher, Pattabiraman, Chatterjee, Haster, Rasio Physical Review Letters. 2015;115:051101.