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Michelle Ntampaka
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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
1082015
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
862019
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
822016
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
752019
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
702020
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
642019
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
502020
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
392013
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
352019
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
332019
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
302020
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
122022
The next decade of astroinformatics and astrostatistics
A Siemiginowska, G Eadie, I Czekala, E Feigelson, EB Ford, V Kashyap, ...
Bulletin of the American Astronomical Society 51 (3), 355, 2019
122019
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
112019
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
92022
Predicting the impact of feedback on matter clustering with machine learning in CAMELS
AM Delgado, D Anglés-Alcázar, L Thiele, S Pandey, K Lehman, ...
Monthly Notices of the Royal Astronomical Society 526 (4), 5306-5325, 2023
82023
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
82017
Emulating Sunyaev–Zeldovich images of galaxy clusters using autoencoders
T Rothschild, D Nagai, H Aung, SB Green, M Ntampaka, J ZuHone
Monthly Notices of the Royal Astronomical Society 513 (1), 333-344, 2022
72022
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
72019
Benchmarks and explanations for deep learning estimates of X-ray galaxy cluster masses
M Ho, J Soltis, A Farahi, D Nagai, A Evrard, M Ntampaka
Monthly Notices of the Royal Astronomical Society 524 (3), 3289-3302, 2023
42023
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Articles 1–20