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.
no code implementations • 10 Jul 2020 • Y. Su, Y. Zhang, G. Liang, J. A. ZuHone, D. J. Barnes, N. B. Jacobs, M. Ntampaka, W. R. Forman, P. E. J. Nulsen, R. P. Kraft, C. Jones
From this analysis, we observe that the network has utilized regions from cluster centers out to r~300 kpc and r~500 kpc to identify CC and NCC clusters, respectively.
Cosmology and Nongalactic Astrophysics
no code implementations • 17 Oct 2018 • M. Ntampaka, J. ZuHone, D. Eisenstein, D. Nagai, A. Vikhlinin, L. Hernquist, F. Marinacci, D. Nelson, R. Pakmor, A. Pillepich, P. Torrey, M. Vogelsberger
Despite our simplifying assumption to neglect spectral information, the resulting mass values estimated by the CNN exhibit small bias in comparison to the true masses of the simulated clusters (-0. 02 dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our best fold and 12% averaging over all.
Cosmology and Nongalactic Astrophysics
no code implementations • 17 Sep 2015 • M. Ntampaka, H. Trac, D. J. Sutherland, S. Fromenteau, B. Poczos, J. Schneider
We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach.
Cosmology and Nongalactic Astrophysics