| 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 | 603 | 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 | 246 | 2018 |
| 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 | 229 | 2019 |
| 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 | 210 | 2018 |
| 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 | 145 | 2018 |
| The strong gravitational lens finding challenge RB Metcalf, M Meneghetti, C Avestruz, F Bellagamba, CR Bom, E Bertin, ... Astronomy & Astrophysics 625, A119, 2019 | 130 | 2019 |
| 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 | 114 | 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 | 92 | 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 | 85 | 2021 |
| 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 | 72 | 2017 |
| 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 | 66 | 2019 |
| High resolution weak lensing mass mapping combining shear and flexion F Lanusse, JL Starck, A Leonard, S Pires Astronomy & Astrophysics 591, A2, 2016 | 60 | 2016 |
| 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 | 57 | 2021 |
| 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 | 55 | 2021 |
| 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 | 55 | 2020 |
| Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: application to DES SV N Jeffrey, FB Abdalla, O Lahav, F Lanusse, JL Starck, A Leonard, D Kirk, ... Monthly Notices of the Royal Astronomical Society 479 (3), 2871-2888, 2018 | 54 | 2018 |
| Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey A Peel, CA Lin, F Lanusse, A Leonard, JL Starck, M Kilbinger Astronomy & Astrophysics 599, A79, 2017 | 51 | 2017 |
| Spherical 3D isotropic wavelets F Lanusse, A Rassat, JL Starck Astronomy & Astrophysics 540, A92, 2012 | 50 | 2012 |
| GLIMPSE: accurate 3D weak lensing reconstructions using sparsity A Leonard, F Lanusse, JL Starck Monthly Notices of the Royal Astronomical Society 440 (2), 1281-1294, 2014 | 47 | 2014 |
| Adaptive wavelet distillation from neural networks through interpretations W Ha, C Singh, F Lanusse, S Upadhyayula, B Yu Advances in Neural Information Processing Systems 34, 20669-20682, 2021 | 43 | 2021 |
