Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
Abstract Machine learning (automated processes that learn by example in order to classify,
predict, discover, or generate new data) and artificial intelligence (methods by which a …
predict, discover, or generate new data) and artificial intelligence (methods by which a …
[HTML][HTML] Applications and techniques for fast machine learning in science
In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …
Properties of the binary black hole merger GW150914
BP Abbott, R Abbott, TD Abbott, MR Abernathy… - Physical review …, 2016 - APS
On September 14, 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO)
detected a gravitational-wave transient (GW150914); we characterize the properties of the …
detected a gravitational-wave transient (GW150914); we characterize the properties of the …
Quantum entanglement in neural network states
Machine learning, one of today's most rapidly growing interdisciplinary fields, promises an
unprecedented perspective for solving intricate quantum many-body problems …
unprecedented perspective for solving intricate quantum many-body problems …
Analysis framework for the prompt discovery of compact binary mergers in gravitational-wave data
We describe a stream-based analysis pipeline to detect gravitational waves from the merger
of binary neutron stars, binary black holes, and neutron-star–black-hole binaries within∼ 1 …
of binary neutron stars, binary black holes, and neutron-star–black-hole binaries within∼ 1 …
Enhancing gravitational-wave science with machine learning
Abstract Machine learning has emerged as a popular and powerful approach for solving
problems in astrophysics. We review applications of machine learning techniques for the …
problems in astrophysics. We review applications of machine learning techniques for the …
Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914
BP Abbott, R Abbott, TD Abbott… - … and Quantum Gravity, 2016 - iopscience.iop.org
A gravitational wave signal, denoted GW150914, has been detected by the Advanced LIGO
detectors [1]. The recovered waveform indicated the source was a binary black hole system …
detectors [1]. The recovered waveform indicated the source was a binary black hole system …
Characterization of the LIGO detectors during their sixth science run
J Aasi, J Abadie, BP Abbott, R Abbott… - … and Quantum Gravity, 2015 - iopscience.iop.org
Between July 2009 and October 2010, the Laser Interferometer Gravitational-Wave
Observatory (LIGO)[1] operated two 4 km laser interferometers as part of a global network …
Observatory (LIGO)[1] operated two 4 km laser interferometers as part of a global network …
Machine learning topological states
Artificial neural networks and machine learning have now reached a new era after several
decades of improvement where applications are to explode in many fields of science …
decades of improvement where applications are to explode in many fields of science …
[HTML][HTML] Omicron: a tool to characterize transient noise in gravitational-wave detectors
F Robinet, N Arnaud, N Leroy, A Lundgren, D Macleod… - SoftwareX, 2020 - Elsevier
The Omicron software is a tool developed to perform a multi-resolution time–frequency
analysis of data from gravitational-wave detectors: the LIGO, Virgo, and KAGRA detectors …
analysis of data from gravitational-wave detectors: the LIGO, Virgo, and KAGRA detectors …