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The Role of Machine Learning in the Next Decade of Cosmology

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arxiv 1902.10159 v2 pith:XAEN6U3Z submitted 2019-02-26 astro-ph.IM astro-ph.CO

Michelle Ntampaka , Camille Avestruz , Steven Boada , Joao Caldeira , Jessi Cisewski-Kehe , Rosanne Di Stefano , Cora Dvorkin , August E. Evrard
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Arya Farahi Doug Finkbeiner Shy Genel Alyssa Goodman Andy Goulding Shirley Ho Arthur Kosowsky Paul La Plante Francois Lanusse Michelle Lochner Rachel Mandelbaum Daisuke Nagai Jeffrey A. Newman Brian Nord J. E. G. Peek Austin Peel Barnabas Poczos Markus Michael Rau Aneta Siemiginowska Danica J. Sutherland Hy Trac Benjamin Wandelt
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In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 37 citations worldwide. Full citation record

  1. Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton

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    A ~4,000-parameter recurrent local network correcting the Zeldovich approximation reaches percent-level matter power-spectrum accuracy at k≲0.5 h/Mpc at z=0, matching larger U-Net emulators on Quijote N-body tests.

  2. DeepCHART: Mapping the 3D dark matter density field from Ly$\alpha$ forest surveys using deep learning

    astro-ph.CO 2025-06 conditional novelty 6.0 of 10

    A 3D neural network reconstructs the dark matter density field at redshift 2.5 from simulated Lyman-alpha forest spectra, reaching voxel-wise correlation of roughly 0.77 for current surveys and 0.90 for denser future ...

  3. Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature

    astro-ph.IM 2025-08 reject novelty 3.0 of 10

    A stacked LSTM-GRU-CNN emulator reportedly reconstructs the global 21-cm brightness temperature with 99.93% accuracy, but a residual feature derived from the target values makes the reported accuracy invalid.

  4. Anisotropic cosmology using observational datasets: exploring via machine learning approaches

    gr-qc 2025-07 conditional novelty 3.0 of 10

    The authors constrain a Bianchi I anisotropic model to near-isotropy (Omega_sigma0 about 0.0009) and show polynomial regression tracks the fitted Hubble curve better than linear regression or ANN.

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