Lanusse, François
Centre National de la Recherche Scientifique · Universidad de La Laguna · Université Paris Cité · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Université Paris Sciences et Lettres · Université Paris-Saclay · Observatoire de Paris · Astrophysique, Instrumentation et Modélisation · Sorbonne Paris Cité · Laboratoire d’Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères · Instituto de Astrofísica de Canarias
7 papers on CoChatORCID 0000-0001-7956-0542
Most cited papers
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CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
Lanusse, F., Ma, Q., Li, N., Collett, T. E., Li, C.-L., Ravanbakhsh, S., Mandelbaum, R., & Póczos, B. (2018) · Monthly Notices of the Royal Astronomical Society
Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations
Cited by 191
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The strong gravitational lens finding challenge
Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F., Davies, A., Decencière, E., et al. (2019) · Astronomy & Astrophysics
Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations
Cited by 152
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Likelihood-free inference with neural compression of DES SV weak lensing map statistics
Jeffrey, N., Alsing, J., & Lanusse, F. (2020) · Monthly Notices of the Royal Astronomical Society
Cosmology and Gravitation TheoriesGalaxies: Formation, Evolution, Phenomena
Cited by 123
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The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., & Lanusse, F. (2023) · Publications of the Astronomical Society of Australia
Galaxies: Formation, Evolution, PhenomenaAstronomical Observations and Instrumentation
Cited by 102
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A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations
Zanisi, L., Huertas-Company, M., Lanusse, F., Bottrell, C., Pillepich, A., Nelson, D., Rodriguez-Gomez, V., Shankar, F., Hernquist, L., Dekel, A., et al. (2021) · Monthly Notices of the Royal Astronomical Society
Galaxies: Formation, Evolution, PhenomenaAstrophysics and Star Formation Studies
Cited by 65
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Deep generative models for galaxy image simulations
Lanusse, F., Mandelbaum, R., Ravanbakhsh, S., Li, C.-L., Freeman, P., & Póczos, B. (2021) · Monthly Notices of the Royal Astronomical Society
Galaxies: Formation, Evolution, PhenomenaStellar, planetary, and galactic studies
Cited by 53
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Probabilistic mass-mapping with neural score estimation
Remy, B., Lanusse, F., Jeffrey, N., Liu, J., Starck, J.-L., Osato, K., & Schrabback, T. (2023) · Astronomy & Astrophysics
Galaxies: Formation, Evolution, PhenomenaCosmology and Gravitation Theories
Cited by 41
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