mlsanityData to 7 Oct 2026

Machine Learning and Cosmology

arXiv 2203.08056 ↗First posted 15 Mar 2022hep-ph astro-ph.CO astro-ph.IMcited by 6

Not on the map. Why?

Abstract (arXiv)

Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped. In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 48 of its 148 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

48 of 148 references.

References · 48

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  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  3. 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
  4. 2019The frontier of simulation-based inferencecited by 490
  5. 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 335
  6. 2021Real-time gravitational-wave science with neural posterior estimationcited by 76
  7. 2022Rediscovering orbital mechanics with machine learningcited by 71
  8. 2018DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applicationscited by 61
  9. 2018Learning to Predict the Cosmological Structure Formationcited by 38
  10. 2020Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithmscited by 34
  11. 2021Adaptive wavelet distillation from neural networks through interpretationscited by 32
  12. 2020Solving high-dimensional parameter inference: marginal posterior densities & Moment Networkscited by 22
36 more
  1. 2021The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligencecited by 18
  2. 2019RAPID: Early Classification of Explosive Transients using Deep Learningcited by 17
  3. 2022Probabilistic Mass Mapping with Neural Score Estimationcited by 17
  4. 2019Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learningcited by 15
  5. 2020AI-assisted super-resolution cosmological simulationscited by 15
  6. 2020Towards constraining warm dark matter with stellar streams through neural simulation-based inferencecited by 14
  7. 2019Convolutional neural networks: a magic bullet for gravitational-wave detection?cited by 12
  8. 2021Multifield Cosmology with Artificial Intelligencecited by 12
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  10. 2018DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networkscited by 11
  11. 2019deepCR: Cosmic Ray Rejection with Deep Learningcited by 11
  12. 2022Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysiscited by 11
  13. 2020Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensingcited by 10
  14. 2021DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domainscited by 10
  15. 2021DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classificationcited by 10
  16. 2020Transformation Importance with Applications to Cosmologycited by 8
  17. 2020Learning a Probabilistic Strategy for Computational Imaging Sensor Selectioncited by 8
  18. 2021Mining for Strong Gravitational Lenses with Self-supervised Learningcited by 8
  19. 2020Fast and Accurate Non-Linear Predictions of Universes with Deep Learningcited by 7
  20. 2021Unsupervised Resource Allocation with Graph Neural Networkscited by 7
  21. 2020Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel'dovich Effectcited by 6
  22. 2020deep21: a Deep Learning Method for 21cm Foreground Removalcited by 6
  23. 2021Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transformscited by 6
  24. 2021Variational Inference for Deblending Crowded Starfieldscited by 5
  25. 2020Constraining the Reionization History using Bayesian Normalizing Flowscited by 4
  26. 2020Mass Estimation of Galaxy Clusters with Deep Learning II: CMB Cluster Lensingcited by 4
  27. 2020Flow-Based Likelihoods for Non-Gaussian Inferencecited by 4
  28. 2020Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentationcited by 4
  29. 2020Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constantcited by 4
  30. 2021Inferring dark matter substructure with astrometric lensing beyond the power spectrumcited by 4
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  35. 2021Reconstructing Cosmic Polarization Rotation with ResUNet-CMBcited by 2
  36. 2018Weak-lensing shear measurement with machine learning: teaching artificial neural networks about feature noisecited by 1

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 2.

6 papers.

  1. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  2. 2023AI for Science: An Emerging Agendacited by 5
  3. 2022CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networkscited by 3
  4. 2023Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networkscited by 2
  5. 2025Bayesian Inference of Primordial Magnetic Field Parameters from CMB with Spherical Graph Neural Networkscited by 0
  6. 2025Deep Learning for Primordial B-mode Extractioncited by 0