scholarly journals Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events

2021 ◽  
Vol 103 (10) ◽  
Author(s):  
Rubén Arjona ◽  
Hai-Nan Lin ◽  
Savvas Nesseris ◽  
Li Tang
Keyword(s):  
Machine Learning ◽  
Gravitational Wave ◽  
Duality Relation

scholarly journals Optimization of model independent gravitational wave search for binary black hole mergers using machine learning

2021 ◽  
Vol 104 (2) ◽  
Author(s):  
T. Mishra ◽  
B. O’Brien ◽  
V. Gayathri ◽  
M. Szczepańczyk ◽  
S. Bhaumik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Black Hole ◽  
Gravitational Wave ◽  
Binary Black Hole ◽  
Model Independent

scholarly journals Classifying Lensed Gravitational Waves in the Geometrical Optics Limit with Machine Learning

2019 ◽  
Vol 16 (2) ◽  
pp. 5-16
Author(s):  
Amit Singh ◽  
Ivan Li ◽  
Otto Hannuksela ◽  
Tjonnie Li ◽  
Kyungmin Kim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Gravitational Wave ◽  
Gravitational Waves ◽  
Geometrical Optics ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Gravitational waves are theorized to be gravitationally lensed when they propagate near massive objects. Such lensing effects cause potentially detectable repeated gravitational wave patterns in ground- and space-based gravitational wave detectors. These effects are difficult to discriminate when the lens is small and the repeated patterns superpose. Traditionally, matched filtering techniques are used to identify gravitational-wave signals, but we instead aim to utilize machine learning techniques to achieve this. In this work, we implement supervised machine learning classifiers (support vector machine, random forest, multi-layer perceptron) to discriminate such lensing patterns in gravitational wave data. We train classifiers with spectrograms of both lensed and unlensed waves using both point-mass and singular isothermal sphere lens models. As the result, classifiers return F1 scores ranging from 0:852 to 0:996, with precisions from 0:917 to 0:992 and recalls ranging from 0:796 to 1:000 depending on the type of classifier and lensing model used. This supports the idea that machine learning classifiers are able to correctly determine lensed gravitational wave signals. This also suggests that in the future, machine learning classifiers may be used as a possible alternative to identify lensed gravitational wave events and to allow us to study gravitational wave sources and massive astronomical objects through further analysis. KEYWORDS: Gravitational Waves; Gravitational Lensing; Geometrical Optics; Machine Learning; Classification; Support Vector Machine; Random Tree Forest; Multi-layer Perceptron

scholarly journals Efficient gravitational-wave glitch identification from environmental data through machine learning

2020 ◽  
Vol 101 (10) ◽  
Author(s):  
Robert E. Colgan ◽  
K. Rainer Corley ◽  
Yenson Lau ◽  
Imre Bartos ◽  
John N. Wright ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Wave ◽  
Environmental Data

scholarly journals Machine-learning nonstationary noise out of gravitational-wave detectors

2020 ◽  
Vol 101 (4) ◽  
Author(s):  
G. Vajente ◽  
Y. Huang ◽  
M. Isi ◽  
J. C. Driggers ◽  
J. S. Kissel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Wave ◽  
Gravitational Wave Detectors

scholarly journals Improving the background of gravitational-wave searches for core collapse supernovae: a machine learning approach

2020 ◽  
Vol 1 (1) ◽  
pp. 015005 ◽  
Author(s):  
M Cavaglià ◽  
S Gaudio ◽  
T Hansen ◽  
K Staats ◽  
M Szczepańczyk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Wave ◽  
Learning Approach ◽  
Core Collapse ◽  
Machine Learning Approach

scholarly journals Rapid identification of strongly lensed gravitational-wave events with machine learning

2021 ◽  
Vol 104 (12) ◽  
Author(s):  
Srashti Goyal ◽  
Harikrishnan D. ◽  
Shasvath J. Kapadia ◽  
Parameswaran Ajith
Keyword(s):  
Machine Learning ◽  
Gravitational Wave ◽  
Rapid Identification

scholarly journals A semisupervised machine learning search for never-seen gravitational-wave sources

2020 ◽  
Vol 500 (4) ◽  
pp. 5408-5419
Author(s):  
Tom Marianer ◽  
Dovi Poznanski ◽  
J Xavier Prochaska
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Gravitational Wave ◽  
Outlier Detection ◽  
Predictive Models ◽  
Detection Rate ◽  
Detection Algorithms ◽  
Compact Binary ◽  
Strain Data ◽  
Cent Detection

ABSTRACT By now, tens of gravitational-wave (GW) events have been detected by the LIGO and Virgo detectors. These GWs have all been emitted by compact binary coalescence, for which we have excellent predictive models. However, there might be other sources for which we do not have reliable models. Some are expected to exist but to be very rare (e.g. supernovae), while others may be totally unanticipated. So far, no unmodelled sources have been discovered, but the lack of models makes the search for such sources much more difficult and less sensitive. We present here a search for unmodelled GW signals using semisupervised machine learning. We apply deep learning and outlier detection algorithms to labelled spectrograms of GW strain data, and then search for spectrograms with anomalous patterns in public LIGO data. We searched ${\sim}13{{\ \rm per\ cent}}$ of the coincident data from the first two observing runs. No candidates of GW signals were detected in the data analyzed. We evaluate the sensitivity of the search using simulated signals, we show that this search can detect spectrograms containing unusual or unexpected GW patterns, and we report the waveforms and amplitudes for which a $50{{\ \rm per\ cent}}$ detection rate is achieved.

Artificial Intelligence in Gravitational-Wave Science

2021 ◽  
Vol 30 (6) ◽  
pp. 14-19
Author(s):  
Kyungmin KIM
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gravitational Wave ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Engineering Industry ◽  
Current Status ◽  
Research Topics ◽  
Computational Engineering ◽  
Fundamental Science

Artificial intelligence gaining popularity not only in the computational engineering industry but also in fundamental science. For the realization of artificial intelligence, numerous machine learning algorithms have been introduced and tested for their applicability. Even in the field of gravitational-wave science, the application of machine learning has been widely studied to enhance conventional analyses in all disciplines from searching for gravitational-wave signals to characterizing noise transients. In this article, I briefly introduce the current status of gravitational-wave science and summarize research topics in which machine learning is applied to each discipline of gravitational-wave science.

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