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Machine Learning and Cosmology
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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.
Forward citations
Cited by 3 Pith papers
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Deep Learning Analysis of Ions Accelerated at Shocks
A convolutional neural network can predict with >90% accuracy whether an ion at a collisionless shock is injected into acceleration, using only the local magnetic field time series from its first few gyrations.
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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks
Restoring RFI-contaminated pixels with the LaMa inpainting network reduces post-foreground-removal RMS and improves recovery of the large-scale 21-cm power spectrum in simulations.
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Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature
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.
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