| Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data C Hikage, M Oguri, T Hamana, S More, R Mandelbaum, M Takada, ... Publications of the Astronomical Society of Japan 71 (2), 43, 2019 | 731 | 2019 |
| Core cosmology library: Precision cosmological predictions for LSST NE Chisari, D Alonso, E Krause, CD Leonard, P Bull, J Neveu, A Villarreal, ... The Astrophysical Journal Supplement Series 242 (1), 2, 2019 | 382 | 2019 |
| The first-year shear catalog of the Subaru Hyper Suprime-Cam Subaru strategic program survey R Mandelbaum, H Miyatake, T Hamana, M Oguri, M Simet, R Armstrong, ... Publications of the Astronomical Society of Japan 70 (SP1), S25, 2018 | 299 | 2018 |
| CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding F Lanusse, Q Ma, N Li, TE Collett, CL Li, S Ravanbakhsh, R Mandelbaum, ... Monthly Notices of the Royal Astronomical Society 473 (3), 3895-3906, 2018 | 263 | 2018 |
| The strong gravitational lens finding challenge RB Metcalf, M Meneghetti, C Avestruz, F Bellagamba, CR Bom, E Bertin, ... Astronomy & Astrophysics 625, A119, 2019 | 187 | 2019 |
| Weak lensing shear calibration with simulations of the HSC survey R Mandelbaum, F Lanusse, A Leauthaud, R Armstrong, M Simet, ... Monthly Notices of the Royal Astronomical Society 481 (3), 3170-3195, 2018 | 179 | 2018 |
| CosmoDC2: A synthetic sky catalog for dark energy science with LSST D Korytov, A Hearin, E Kovacs, P Larsen, E Rangel, J Hollowed, ... The Astrophysical Journal Supplement Series 245 (2), 26, 2019 | 175 | 2019 |
| Likelihood-free inference with neural compression of DES SV weak lensing map statistics N Jeffrey, J Alsing, F Lanusse Monthly Notices of the Royal Astronomical Society 501 (1), 954-969, 2021 | 154 | 2021 |
| Dark Energy Survey Year 3 results: Curved-sky weak lensing mass map reconstruction N Jeffrey, M Gatti, C Chang, L Whiteway, U Demirbozan, A Kovács, ... Monthly Notices of the Royal Astronomical Society 505 (3), 4626-4645, 2021 | 119 | 2021 |
| Multiple physics pretraining for physical surrogate models M McCabe, BRS Blancard, LH Parker, R Ohana, M Cranmer, A Bietti, ... arXiv preprint arXiv:2310.02994, 2023 | 97 | 2023 |
| Jax-cosmo: An end-to-end differentiable and gpu accelerated cosmology library JE Campagne, F Lanusse, J Zuntz, A Boucaud, S Casas, M Karamanis, ... arXiv preprint arXiv:2302.05163, 2023 | 93 | 2023 |
| Deep generative models for galaxy image simulations F Lanusse, R Mandelbaum, S Ravanbakhsh, CL Li, P Freeman, B Póczos Monthly Notices of the Royal Astronomical Society 504 (4), 5543-5555, 2021 | 92 | 2021 |
| AstroCLIP: a cross-modal foundation model for galaxies L Parker, F Lanusse, S Golkar, L Sarra, M Cranmer, A Bietti, M Eickenberg, ... Monthly Notices of the Royal Astronomical Society 531 (4), 4990-5011, 2024 | 88 | 2024 |
| 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 | 87 | 2019 |
| Enabling dark energy science with deep generative models of galaxy images S Ravanbakhsh, F Lanusse, R Mandelbaum, J Schneider, B Poczos Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 85 | 2017 |
| A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations L Zanisi, M Huertas-Company, F Lanusse, C Bottrell, A Pillepich, ... Monthly Notices of the Royal Astronomical Society 501 (3), 4359-4382, 2021 | 78 | 2021 |
| High resolution weak lensing mass mapping combining shear and flexion F Lanusse, JL Starck, A Leonard, S Pires Astronomy & Astrophysics 591, A2, 2016 | 73 | 2016 |
| xval: A continuous number encoding for large language models S Golkar, M Pettee, M Eickenberg, A Bietti, M Cranmer, G Krawezik, ... arXiv preprint arXiv:2310.02989, 2023 | 72 | 2023 |
| Deep learning dark matter map reconstructions from DES SV weak lensing data N Jeffrey, F Lanusse, O Lahav, JL Starck Monthly Notices of the Royal Astronomical Society 492 (4), 5023-5029, 2020 | 69 | 2020 |
| The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys F Lanusse Publications of the Astronomical Society of Australia 40, e001, 2023 | 66 | 2023 |
