no code implementations • 19 Oct 2023 • D. Huppenkothen, M. Ntampaka, M. Ho, M. Fouesneau, B. Nord, J. E. G. Peek, M. Walmsley, J. F. Wu, C. Avestruz, T. Buck, M. Brescia, D. P. Finkbeiner, A. D. Goulding, T. Kacprzak, P. Melchior, M. Pasquato, N. Ramachandra, Y. -S. Ting, G. van de Ven, S. Villar, V. A. Villar, E. Zinger
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.
1 code implementation • 25 Sep 2020 • John F. Wu, J. E. G. Peek
Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms.
1 code implementation • 24 Apr 2020 • A. Ćiprijanović, G. F. Snyder, B. Nord, J. E. G. Peek
The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy.
no code implementations • 13 May 2019 • Amanda E. Bauer, Eric C. Bellm, Adam S. Bolton, Surajit Chaudhuri, A. J. Connolly, Kelle L. Cruz, Vandana Desai, Alex Drlica-Wagner, Frossie Economou, Niall Gaffney, J. Kavelaars, J. Kinney, Ting S. Li, B. Lundgren, R. Margutti, G. Narayan, B. Nord, Dara J. Norman, W. O'Mullane, S. Padhi, J. E. G. Peek, C. Schafer, Megan E. Schwamb, Arfon M. Smith, Erik J. Tollerud, Anne-Marie Weijmans, Alexander S. Szalay
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
1 code implementation • 2 May 2019 • J. E. G. Peek, Blakesley Burkhart
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
no code implementations • 26 Feb 2019 • Michelle Ntampaka, Camille Avestruz, Steven Boada, Joao Caldeira, Jessi Cisewski-Kehe, Rosanne Di Stefano, Cora Dvorkin, August E. Evrard, 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, Dougal J. Sutherland, Hy Trac, Benjamin Wandelt
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
1 code implementation • 29 Oct 2017 • Y. Zheng, J. E. G. Peek, M. E. Putman, J. K. Werk
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