no code implementations • 3 Oct 2024 • Alan Hsu, Matthew Ho, Joyce Lin, Carleen Markey, Michelle Ntampaka, Hy Trac, Barnabás Póczos
We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling.
1 code implementation • 30 May 2024 • John F. Wu, Alina Hyk, Kiera McCormick, Christine Ye, Simone Astarita, Elina Baral, Jo Ciuca, Jesse Cranney, Anjalie Field, Kartheik Iyer, Philipp Koehn, Jenn Kotler, Sandor Kruk, Michelle Ntampaka, Charles O'Neill, Joshua E. G. Peek, Sanjib Sharma, Mikaeel Yunus
It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them.
no code implementations • 5 Nov 2019 • Brian Nord, Andrew J. Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua E. G. Peek, Chad Schafer, Erik J. Tollerud, Camille Avestruz, G. Jogesh Babu, Simon Birrer, Douglas Burke, João Caldeira, Douglas A. Caldwell, Joleen K. Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric D. Feigelson, V. Zach Golkhou, Vinay Kashyap, T. S. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey S. Mandel, J. R. Martínez-Galarza, Adam A. Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta M. Sipőcz, Arfon M. Smith, Nhan Tran, Ricardo Vilalta, Lucianne M. Walkowicz, John ZuHone
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
no code implementations • 23 Sep 2019 • Michelle Ntampaka, Daniel J. Eisenstein, Sihan Yuan, Lehman H. Garrison
We present a deep machine learning (ML)-based technique for accurately determining $\sigma_8$ and $\Omega_m$ from mock 3D galaxy surveys.
Cosmology and Nongalactic Astrophysics
no code implementations • 7 Aug 2019 • Sheridan B. Green, Michelle Ntampaka, Daisuke Nagai, Lorenzo Lovisari, Klaus Dolag, Dominique Eckert, John A. ZuHone
This procedure is performed for two different mock observation series in an effort to bracket the potential enhancement in mass predictions that can be made possible by including dynamical state information.
Cosmology and Nongalactic 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 • 15 Feb 2019 • Matthew Ho, Markus Michael Rau, Michelle Ntampaka, Arya Farahi, Hy Trac, Barnabas Poczos
Our first model, CNN$_\text{1D}$, infers cluster mass directly from the distribution of member galaxy line-of-sight velocities.
Cosmology and Nongalactic Astrophysics
no code implementations • 2 Oct 2014 • Michelle Ntampaka, Hy Trac, Dougal J. Sutherland, Nicholas Battaglia, Barnabas Poczos, Jeff Schneider
In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v.
Cosmology and Nongalactic Astrophysics
no code implementations • 5 Mar 2013 • Xiaoying Xu, Shirley Ho, Hy Trac, Jeff Schneider, Barnabas Poczos, Michelle Ntampaka
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties.
Cosmology and Nongalactic Astrophysics