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
收藏
页数：10
相关论文
共 50 条
  • [21] A systematic review of machine learning and deep learning approaches in plant species detection
    Barhate, Deepti
    Pathak, Sunil
    Singh, Bhupesh Kumar
    Jain, Amit
    Dubey, Ashutosh Kumar
    SMART AGRICULTURAL TECHNOLOGY, 2024, 9
  • [22] Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
    Kremer, Jan
    Stensbo-Smidt, Kristoffer
    Gieseke, Fabian
    Pedersen, Kim Steenstrup
    Igel, Christian
    IEEE INTELLIGENT SYSTEMS, 2017, 32 (02) : 16 - 22
  • [23] Decoding Intracranial EEG With Machine Learning: A Systematic Review
    Mirchi, Nykan
    Warsi, Nebras M.
    Zhang, Frederick
    Wong, Simeon M.
    Suresh, Hrishikesh
    Mithani, Karim
    Erdman, Lauren
    Ibrahim, George M.
    FRONTIERS IN HUMAN NEUROSCIENCE, 2022, 16
  • [24] Identification of Surface Deformation in InSAR Using Machine Learning
    Brengman, Clayton M. J.
    Barnhart, William D.
    GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS, 2021, 22 (03)
  • [25] State of the Art: Machine Learning Applications in Glioma Imaging
    Lotan, Eyal
    Lotan, Eyal
    Jain, Rajan
    Razavian, Narges
    Fatterpekar, Girish M.
    Lui, Yvonne W.
    AMERICAN JOURNAL OF ROENTGENOLOGY, 2019, 212 (01) : 26 - 37
  • [26] Image driven machine learning methods for microstructure recognition
    Chowdhury, Aritra
    Kautz, Elizabeth
    Yener, Bulent
    Lewis, Daniel
    COMPUTATIONAL MATERIALS SCIENCE, 2016, 123 : 176 - 187
  • [27] Transfer learning features for predicting aesthetics through a novel hybrid machine learning method
    Carballal, Adrian
    Fernandez-Lozano, Carlos
    Heras, Jonathan
    Romero, Juan
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (10) : 5889 - 5900
  • [28] AI in Endoscopic Gastrointestinal Diagnosis: A Systematic Review of Deep Learning and Machine Learning Techniques
    Lewis, Jovita Relasha
    Pathan, Sameena
    Kumar, Preetham
    Dias, Cifha Crecil
    IEEE ACCESS, 2024, 12 : 163764 - 163786
  • [29] A hybrid framework for glaucoma detection through federated machine learning and deep learning models
    Aljohani, Abeer
    Aburasain, Rua Y.
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2024, 24 (01)
  • [30] Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques
    Kode, Hepseeba
    Elleithy, Khaled
    Almazaydeh, Laiali
    IEEE ACCESS, 2024, 12 : 80657 - 80668
12345