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Found 7 papers, 4 papers with code
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.
Predicting galaxy spectra from images with hybrid convolutional neural networks
Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms.
DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks
The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy.
Petabytes to Science
A Kavli foundation sponsored workshop on the theme \emph{Petabytes to Science} was held 12$^{th}$ to 14$^{th}$ of February 2019 in Las Vegas.
Instrumentation and Methods for Astrophysics
Do Androids Dream of Magnetic Fields? Using Neural Networks to Interpret the Turbulent Interstellar Medium
In this work we use density slices of magnetohydrodyanmic turbulence simulations to demonstrate that a modern tool, convolutional neural networks, can capture significant information encoded in the Fourier phases.
Instrumentation and Methods for Astrophysics
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
Revealing the Milky Way's Hidden Circumgalactic Medium with the COS Quasar Database for Galactic Absorption Lines
Our analyses show that there is likely to be a large amount of gas at $|v_{\rm LSR}|\leq100$ km s$^{-1}$ hidden in the MW's CGM.
Astrophysics of Galaxies
