| 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 | 101 | 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 | 83 | 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 | 80 | 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 | 69 | 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 | 62 | 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 | 60 | 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 | 48 | 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 | 38 | 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 | 33 | 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 | 28 | 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 | 27 | 2020 |
| The next decade of astroinformatics and astrostatistics A Siemiginowska, M Kuhn, M Graham, AA Mahabal, SR Taylor | 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 |
| 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 | 8 | 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 |
| 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 | 6 | 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 | 6 | 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 | 2 | 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 | 2 | 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 | 2 | 2019 |
Hy TracAssociate Professor, Department of Physics, McWilliams Center for Cosmology, Carnegie MellonVerified email at andrew.cmu.edu
