Michelle Ntampaka
Title
Cited by
Cited by
Year
A machine learning approach for dynamical mass measurements of galaxy clusters
M Ntampaka, H Trac, DJ Sutherland, N Battaglia, B Póczos, J Schneider
The Astrophysical Journal 803 (2), 50, 2015
712015
Dynamical mass measurements of contaminated galaxy clusters using machine learning
M Ntampaka, H Trac, DJ Sutherland, S Fromenteau, B Póczos, ...
The Astrophysical Journal 831 (2), 135, 2016
602016
A deep learning approach to galaxy cluster x-ray masses
M Ntampaka, J ZuHone, D Eisenstein, D Nagai, A Vikhlinin, L Hernquist, ...
The Astrophysical Journal 876 (1), 82, 2019
362019
A first look at creating mock catalogs with machine learning techniques
X Xu, S Ho, H Trac, J Schneider, B Poczos, M Ntampaka
The Astrophysical Journal 772 (2), 147, 2013
282013
The role of machine learning in the next decade of cosmology
M Ntampaka, C Avestruz, S Boada, J Caldeira, J Cisewski-Kehe, ...
arXiv preprint arXiv:1902.10159, 2019
242019
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
M Ho, MM Rau, M Ntampaka, A Farahi, H Trac, B Póczos
The Astrophysical Journal 887 (1), 25, 2019
212019
Machine Learning Applied to the Reionization History of the Universe in the 21 cm Signal
P La Plante, M Ntampaka
The Astrophysical Journal 880 (2), 110, 2019
192019
Using X-ray morphological parameters to strengthen galaxy cluster mass estimates via machine learning
SB Green, M Ntampaka, D Nagai, L Lovisari, K Dolag, D Eckert, ...
The Astrophysical Journal 884 (1), 33, 2019
112019
A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys
M Ntampaka, DJ Eisenstein, S Yuan, LH Garrison
The Astrophysical Journal 889 (2), 151, 2020
92020
SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae
VA Villar, G Hosseinzadeh, E Berger, M Ntampaka, DO Jones, P Challis, ...
The Astrophysical Journal 905 (2), 94, 2020
82020
A deep learning view of the census of galaxy clusters in IllustrisTNG
Y Su, Y Zhang, G Liang, JA ZuHone, DJ Barnes, NB Jacobs, M Ntampaka, ...
Monthly Notices of the Royal Astronomical Society 498 (4), 5620-5628, 2020
82020
The next decade of astroinformatics and astrostatistics
A Siemiginowska, M Kuhn, M Graham, AA Mahabal, SR Taylor
72019
Cluster Cosmology with the Velocity Distribution Function of the HeCS-SZ Sample
M Ntampaka, K Rines, H Trac
The Astrophysical Journal 880 (2), 154, 2019
62019
The velocity distribution function of galaxy clusters as a cosmological probe
M Ntampaka, H Trac, J Cisewski, LC Price
The Astrophysical Journal 835 (1), 106, 2017
42017
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
B Nord, AJ Connolly, J Kinney, J Kubica, G Narayan, JEG Peek, ...
arXiv preprint arXiv:1911.02479, 2019
12019
Galaxy Cluster Membership with Machine Learning
S Narayanan, M Ntampaka
American Astronomical Society Meeting Abstracts# 235 235, 386.05, 2020
2020
Toward 1% calibration of CMB Lensing Cluster Mass Estimate with Deep Learning
S Nakoneczny, D Nagai, M Ntampaka, S Hassan, F Lanusse
2019
The Early Career Perspective on the Coming Decade, Astrophysics Career Paths, and the Decadal Survey Process
E Moravec, I Czekala, K Follette, M Alpasian, A Amon, W Armentrout, ...
Bulletin of the American Astronomical Society 51 (7), 8, 2019
2019
Astro2020 APC white paper: State of the profession consideration the early career perspective on the coming decade, astrophysics career paths, and the decadal survey process
E Moravec, I Czekala, K Follette, Z Ahmed, M Alpaslan, A Amon, ...
Unknown Journal, 2019
2019
Astro2020 APC White Paper: The Early Career Perspective on the Coming Decade, Astrophysics Career Paths, and the Decadal Survey Process
E Moravec, I Czekala, K Follette, Z Ahmed, M Alpaslan, A Amon, ...
arXiv preprint arXiv:1907.01676, 2019
2019
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Articles 1–20