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 | 109 | 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 | 90 | 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 | 82 | 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 | 75 | 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 | 72 | 2020 |
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 | 64 | 2019 |
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 | 51 | 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 | 39 | 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 | 35 | 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 | 32 | 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 | 31 | 2020 |
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 | 12 | 2022 |
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 | 12 | 2019 |
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 | 11 | 2019 |
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 | 9 | 2023 |
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 | 9 | 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 | 8 | 2017 |
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 | 7 | 2019 |
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 | 6 | 2023 |
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 | 6 | 2022 |