東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Gravitational wave science with machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Gravitational wave science with machine learning/ edited by Elena Cuoco.
其他作者:	Cuoco, Elena.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xxv, 289 p. :ill. (chiefly color), digital ;24 cm.
內容註:	1. Neural network time-series classifiers for gravitational-wave searches in single-detector periods -- 2. A simple self similarity-based unsupervised noise monitor for gravitational-wave detectors -- 3 Simulation of transient noise bursts in gravitational wave interferometers -- 4. Efficient ML Algorithms for Detecting Glitches and Data Patterns in LIGO Time Series -- 5. Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder.
Contained By:	Springer Nature eBook
標題:	Gravitational waves - Detection. -
電子資源:	https://doi.org/10.1007/978-981-96-1737-1
ISBN:	9789819617371

Gravitational wave science with machine learning
Gravitational wave science with machine learning[electronic resource] /edited by Elena Cuoco. - Singapore :Springer Nature Singapore :2025. - xxv, 289 p. :ill. (chiefly color), digital ;24 cm. - Springer series in astrophysics and cosmology,2731-7358. - Springer series in astrophysics and cosmology..

1. Neural network time-series classifiers for gravitational-wave searches in single-detector periods -- 2. A simple self similarity-based unsupervised noise monitor for gravitational-wave detectors -- 3 Simulation of transient noise bursts in gravitational wave interferometers -- 4. Efficient ML Algorithms for Detecting Glitches and Data Patterns in LIGO Time Series -- 5. Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder.

This book highlights the state of the art of machine learning applied to the science of gravitational waves. The main topics of the book range from the search for astrophysical gravitational wave signals to noise suppression techniques and control systems using machine learning-based algorithms. During the four years of work in the COST Action CA17137-A network for Gravitational Waves, Geophysics and Machine Learning (G2net), the collaboration produced several original publications as well as tutorials and lectures in the training schools we organized. The book encapsulates the immense amount of finding and achievements. It is a timely reference for young researchers approaching the analysis of data from gravitational wave experiments, with alternative approaches based on the use of artificial intelligence techniques.

ISBN: 9789819617371

Standard No.: 10.1007/978-981-96-1737-1doiSubjects--Topical Terms:

3784379
Gravitational waves
--Detection.

LC Class. No.: QC179

Dewey Class. No.: 539.754

Gravitational wave science with machine learning
LDR:02363nmm a2200337 a 4500 001 2410472
003 DE-He213
005 20250411151346.0
006 m d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819617371 $q (electronic bk.)
020 $a 9789819617364 $q (paper)
024 7 $a 10.1007/978-981-96-1737-1 $2 doi
035 $a 978-981-96-1737-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QC179
072 7 $a PHVB $2 bicssc
072 7 $a SCI005000 $2 bisacsh
072 7 $a PHVB $2 thema
082 0 4 $a 539.754 $2 23
090 $a QC179 $b .G777 2025
245 0 0 $a Gravitational wave science with machine learning $h [electronic resource] / $c edited by Elena Cuoco.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xxv, 289 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Springer series in astrophysics and cosmology, $x 2731-7358
505 0 $a 1. Neural network time-series classifiers for gravitational-wave searches in single-detector periods -- 2. A simple self similarity-based unsupervised noise monitor for gravitational-wave detectors -- 3 Simulation of transient noise bursts in gravitational wave interferometers -- 4. Efficient ML Algorithms for Detecting Glitches and Data Patterns in LIGO Time Series -- 5. Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder.
520 $a This book highlights the state of the art of machine learning applied to the science of gravitational waves. The main topics of the book range from the search for astrophysical gravitational wave signals to noise suppression techniques and control systems using machine learning-based algorithms. During the four years of work in the COST Action CA17137-A network for Gravitational Waves, Geophysics and Machine Learning (G2net), the collaboration produced several original publications as well as tutorials and lectures in the training schools we organized. The book encapsulates the immense amount of finding and achievements. It is a timely reference for young researchers approaching the analysis of data from gravitational wave experiments, with alternative approaches based on the use of artificial intelligence techniques.
650 0 $a Gravitational waves $x Detection. $3 3784379
650 0 $a Machine learning. $3 533906
650 1 4 $a Astrophysics. $3 535904
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a General Relativity. $3 3593594
650 2 4 $a Computational Physics and Simulations. $3 3538874
700 1 $a Cuoco, Elena. $3 3784378
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer series in astrophysics and cosmology. $3 3632681
856 4 0 $u https://doi.org/10.1007/978-981-96-1737-1
950 $a Physics and Astronomy (SpringerNature-11651)