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Machine learning cosmological structure formation

Published:2018-06-29 Issue:3 Volume:479 Page:3405-3414
ISSN:0035-8711
Container-title:Monthly Notices of the Royal Astronomical Society
language:en
Short-container-title:

Author:

Lucie-Smith Luisa¹^ORCID,Peiris Hiranya V¹²,Pontzen Andrew¹,Lochner Michelle¹³⁴

Affiliation:

1. Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK

2. The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden

3. African Institute for Mathematical Sciences, 6 Melrose Road, Muizenberg 7945, South Africa

4. SKA SA, The Park, Park Road, Cape Town 7405, South Africa

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

Link

http://academic.oup.com/mnras/article-pdf/479/3/3405/25161668/sty1719.pdf

Reference39 articles.

1. Painting galaxies into dark matter haloes using machine learning

2. DATA MINING AND MACHINE LEARNING IN ASTRONOMY

3. The statistics of peaks of Gaussian random fields

4. The Peak-Patch Picture of Cosmic Catalogs. I. Algorithms

5. Excursion set mass functions for hierarchical Gaussian fluctuations

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1. Kinematic Sunyaev-Zel′dovich pairwise velocity reconstruction with machine learning;Physical Review D;2024-06-14

2. The nonlinear stability of (n + 1)-dimensional FLRW spacetimes;Journal of Hyperbolic Differential Equations;2024-06

3. Characterizing structure formation through instance segmentation;Astronomy & Astrophysics;2024-05

4. Deep learning insights into cosmological structure formation;Physical Review D;2024-03-14

5. Accurate halo mass functions from the simplest excursion set theory;Monthly Notices of the Royal Astronomical Society;2024-01-12

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