Detection of Einstein telescope gravitational wave signals from binary black holes using deep learning
出版年份 2022 全文链接
标题
Detection of Einstein telescope gravitational wave signals from binary black holes using deep learning
作者
关键词
-
出版物
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 519, Issue 3, Pages 3843-3850
出版商
Oxford University Press (OUP)
发表日期
2022-12-23
DOI
10.1093/mnras/stac3797
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Identify real gravitational wave events in the LIGO-Virgo catalog GWTC-1 and GWTC-2 with convolutional neural network
- (2022) Meng-Qin Jiang et al. Frontiers of Physics
- Exploring compact binary populations with the Einstein Telescope
- (2022) N. Singh et al. ASTRONOMY & ASTROPHYSICS
- Forecasting the Detection Capabilities of Third-generation Gravitational-wave Detectors Using GWFAST
- (2022) Francesco Iacovelli et al. ASTROPHYSICAL JOURNAL
- Exploring gravitational-wave detection and parameter inference using deep learning methods
- (2021) João D Álvares et al. CLASSICAL AND QUANTUM GRAVITY
- Science case for the Einstein telescope
- (2020) Michele Maggiore et al. JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
- Photometric redshift estimation via deep learning
- (2018) A. D’Isanto et al. ASTRONOMY & ASTROPHYSICS
- Photometric redshift estimation via deep learning
- (2018) A. D’Isanto et al. ASTRONOMY & ASTROPHYSICS
- The FIRST Classifier: compact and extended radio galaxy classification using deep Convolutional Neural Networks
- (2018) Wathela Alhassan et al. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
- Automated morphological classification of galaxies based on projection gradient nonnegative matrix factorization algorithm
- (2017) I. M. Selim et al. EXPERIMENTAL ASTRONOMY
- Planck2015 results
- (2016) et al. ASTRONOMY & ASTROPHYSICS
- Star–galaxy classification using deep convolutional neural networks
- (2016) Edward J. Kim et al. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
- Observation of Gravitational Waves from a Binary Black Hole Merger
- (2016) B. P. Abbott et al. PHYSICAL REVIEW LETTERS
- Planck2015 results
- (2016) et al. ASTRONOMY & ASTROPHYSICS
- Deep learning in neural networks: An overview
- (2015) Jürgen Schmidhuber NEURAL NETWORKS
- A comparison of classification techniques for seismic facies recognition
- (2015) Tao Zhao et al. Interpretation-A Journal of Subsurface Characterization
- Selection of optimal window length using STFT for quantitative SNR analysis of LFM signal
- (2014) Qingbo Yin et al. Journal of Systems Engineering and Electronics
- Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
- (2013) Rahul Biswas et al. PHYSICAL REVIEW D
- Sensitivity studies for third-generation gravitational wave observatories
- (2011) S Hild et al. CLASSICAL AND QUANTUM GRAVITY
- The Einstein Telescope: a third-generation gravitational wave observatory
- (2010) M Punturo et al. CLASSICAL AND QUANTUM GRAVITY
- Photometric redshift estimation using Gaussian processes
- (2010) D. G. Bonfield et al. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
- LIGO: the Laser Interferometer Gravitational-Wave Observatory
- (2009) B P Abbott et al. REPORTS ON PROGRESS IN PHYSICS
- Space-based gravitational wave detection with LISA
- (2008) D A Shaddock CLASSICAL AND QUANTUM GRAVITY
- Stabilized lasers for advanced gravitational wave detectors
- (2008) B Willke et al. CLASSICAL AND QUANTUM GRAVITY
- Morphology classification and photometric redshift measurement of galaxies
- (2008) Yanxia Zhang et al. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started