| The quijote simulations F Villaescusa-Navarro, CH Hahn, E Massara, A Banerjee, AM Delgado, ... The Astrophysical Journal Supplement Series 250 (1), 2, 2020 | 267 | 2020 |
| Fast likelihood-free cosmology with neural density estimators and active learning J Alsing, T Charnock, S Feeney, B Wandelt Monthly Notices of the Royal Astronomical Society 488 (3), 4440-4458, 2019 | 231 | 2019 |
| Tension between the power spectrum of density perturbations measured on large and small scales RA Battye, T Charnock, A Moss Physical Review D 91 (10), 103508, 2015 | 188 | 2015 |
| Deep recurrent neural networks for supernovae classification T Charnock, A Moss The Astrophysical Journal Letters 837 (2), L28, 2017 | 141 | 2017 |
| CMB constraints on cosmic strings and superstrings T Charnock, A Avgoustidis, EJ Copeland, A Moss Physical Review D 93 (12), 123503, 2016 | 134 | 2016 |
| Automatic physical inference with information maximizing neural networks T Charnock, G Lavaux, BD Wandelt Physical Review D 97 (8), 083004, 2018 | 103 | 2018 |
| Super-resolution emulator of cosmological simulations using deep physical models D Kodi Ramanah, T Charnock, F Villaescusa-Navarro, BD Wandelt Monthly Notices of the Royal Astronomical Society 495 (4), 4227-4236, 2020 | 65 | 2020 |
| Super-resolution emulator of cosmological simulations using deep physical models D Kodi Ramanah, T Charnock, F Villaescusa-Navarro, BD Wandelt Monthly Notices of the Royal Astronomical Society 495 (4), 4227-4236, 2020 | 65 | 2020 |
| Planck data versus large scale structure: Methods to quantify discordance T Charnock, RA Battye, A Moss Physical Review D 95 (12), 123535, 2017 | 55 | 2017 |
| Detecting outliers in astronomical images with deep generative networks B Margalef-Bentabol, M Huertas-Company, T Charnock, ... Monthly Notices of the Royal Astronomical Society 496 (2), 2346-2361, 2020 | 45 | 2020 |
| Lossless, scalable implicit likelihood inference for cosmological fields TL Makinen, T Charnock, J Alsing, BD Wandelt Journal of Cosmology and Astroparticle Physics 2021 (11), 049, 2021 | 43 | 2021 |
| Painting halos from cosmic density fields of dark matter with physically motivated neural networks DK Ramanah, T Charnock, G Lavaux Physical Review D 100 (4), 043515, 2019 | 42 | 2019 |
| Painting halos from cosmic density fields of dark matter with physically motivated neural networks DK Ramanah, T Charnock, G Lavaux Physical Review D 100 (4), 043515, 2019 | 42 | 2019 |
| The Astrophysical Journal Letters, 837 T Charnock, A Moss L28, 2017 | 23 | 2017 |
| The cosmic graph: Optimal information extraction from large-scale structure using catalogues TL Makinen, T Charnock, P Lemos, N Porqueres, A Heavens, BD Wandelt arXiv preprint arXiv:2207.05202, 2022 | 21 | 2022 |
| Bayesian neural networks T Charnock, L Perreault-Levasseur, F Lanusse Artificial Intelligence for High Energy Physics, 663-713, 2022 | 21 | 2022 |
| Neural physical engines for inferring the halo mass distribution function T Charnock, G Lavaux, BD Wandelt, S Sarma Boruah, J Jasche, ... Monthly Notices of the Royal Astronomical Society 494 (1), 50-61, 2020 | 17 | 2020 |
| Catalog-free modeling of galaxy types in deep images-Massive dimensional reduction with neural networks F Livet, T Charnock, D Le Borgne, V de Lapparent Astronomy & Astrophysics 652, A62, 2021 | 7 | 2021 |
| supernovae: Photometric classification of supernovae T Charnock, A Moss Astrophysics Source Code Library, ascl: 1705.017, 2017 | 4 | 2017 |
| Planck confronts large scale structure: methods to quantify discordance T Charnock, RA Battye, A Moss arXiv preprint arXiv:1703.05959, 2017 | 4 | 2017 |
