Michelle Ntampaka
Cited by
Cited by
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
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
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
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
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
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
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
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
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
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
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
The next decade of astroinformatics and astrostatistics
A Siemiginowska, M Kuhn, M Graham, AA Mahabal, SR Taylor
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
The dynamical mass of the Coma cluster from deep learning
M Ho, M Ntampaka, MM Rau, M Chen, A Lansberry, F Ruehle, H Trac
Nature Astronomy 6 (8), 936-941, 2022
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
The importance of being interpretable: Toward an understandable machine learning encoder for galaxy cluster cosmology
M Ntampaka, A Vikhlinin
The Astrophysical Journal 926 (1), 45, 2022
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
A Machine-learning Approach to Enhancing eROSITA Observations
J Soltis, M Ntampaka, JF Wu, J ZuHone, A Evrard, A Farahi, M Ho, ...
The Astrophysical Journal 940 (1), 60, 2022
LoVoCCS. I. Survey Introduction, Data Processing Pipeline, and Early Science Results
S Fu, I Dell’Antonio, RR Chary, D Clowe, MC Cooper, M Donahue, ...
The Astrophysical Journal 933 (1), 84, 2022
Astro2020 Science White Paper: The Next Decade of Astroinformatics and Astrostatistics
A Siemiginowska, G Eadie, I Czekala, E Feigelson, EB Ford, V Kashyap, ...
arXiv preprint arXiv:1903.06796, 2019
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