Machine learning in astronomy

被引：6

作者：

Kembhavi, Ajit ^{[1
]}

Pattnaik, Rohan ^{[2
]}

机构：

[1] Interuniv Ctr Astron & Astrophys IUCAA, Pune 411007, Maharashtra, India

[2] Rochester Inst Technol, Sch Phys & Astron, Rochester, NY 14623 USA

来源：

JOURNAL OF ASTROPHYSICS AND ASTRONOMY | 2022年 / 43卷 / 02期

关键词：

Low-mass X-ray binaries; star-galaxy classification; machine learning; classification; CONVOLUTIONAL NEURAL-NETWORKS; X-RAY-BURSTS; CLASSIFICATION;

D O I：

10.1007/s12036-022-09871-2

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

Artificial intelligence techniques like machine learning and deep learning are being increasingly used in astronomy to address the vast quantities of data, which are now widely available. We briefly introduce some of these techniques and then describe their use through the examples of star-galaxy classification and the classification of low-mass X-ray binaries into binaries, which host a neutron star and those which host a black hole. This paper is based on a talk given by one of the authors and reviews previously published work and some new results.

引用

页数：10

共 50 条

[31] Semantic speech analysis using machine learning and deep learning techniques: a comprehensive review
Tyagi, Suryakant
Szenasi, Sandor
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (29) : 73427 - 73456
[32] Machine-learning-based diagnostics of EEG pathology
Gemein, Lukas A. W.
Schirrmeister, Robin T.
Chrabaszcz, Patryk
Wilson, Daniel
Boedecker, Joschka
Schulze-Bonhage, Andreas
Hutter, Frank
Ball, Tonio
NEUROIMAGE, 2020, 220
[33] Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review
Azad, Muhammad Muzammil
Kim, Sungjun
Cheon, Yu Bin
Kim, Heung Soo
ADVANCED COMPOSITE MATERIALS, 2024, 33 (02) : 162 - 188
[34] Machine Learning in Healthcare
Habehh, Hafsa
Gohel, Suril
CURRENT GENOMICS, 2021, 22 (04) : 291 - 300
[35] Transfer learning features for predicting aesthetics through a novel hybrid machine learning method
Adrian Carballal
Carlos Fernandez-Lozano
Jonathan Heras
Juan Romero
Neural Computing and Applications, 2020, 32 : 5889 - 5900
[36] Machine Learning in Bioelectrocatalysis
Huang, Jiamin
Gao, Yang
Chang, Yanhong
Peng, Jiajie
Yu, Yadong
Wang, Bin
ADVANCED SCIENCE, 2024, 11 (02)
[37] Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality
Nazar, Wojciech
Nazar, Krzysztof
Danilowicz-Szymanowicz, Ludmila
LIFE-BASEL, 2024, 14 (06):
[38] A survey on machine learning models for financial time series forecasting
Tang, Yajiao
Song, Zhenyu
Zhu, Yulin
Yuan, Huaiyu
Hou, Maozhang
Ji, Junkai
Tang, Cheng
Li, Jianqiang
NEUROCOMPUTING, 2022, 512 : 363 - 380
[39] Speech emotion recognition using machine learning - A systematic review
Madanian, Samaneh
Chen, Talen
Adeleye, Olayinka
Templeton, John Michael
Poellabauer, Christian
Parry, Dave
Schneidere, Sandra L.
INTELLIGENT SYSTEMS WITH APPLICATIONS, 2023, 20
[40] Evaluation of machine learning algorithms for health and wellness applications: A tutorial
Tohka, Jussi
van Gils, Mark
COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 132 (132)

← 1 2 3 4 5 →