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A new generation of sky surveys is poised to provide unprecedented volumes of data containing hundreds of thousands of new strong lensing systems in the coming years.
Influence of the atmospheric and instrumental dispersion on the brightness distribution in a galaxy
Sérsic, J. L · 1963
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Isothermal elliptical gravitational lens models
Kormann, R., Schneider, P., and Bartelmann, M · 1994
Earlier work this paper cites.
Gravitational detection of a low-mass dark satellite galaxy at cosmological distance
Vegetti, S., Lagattuta, D. J., McKean, J. P., Auger, M. W., Fassnacht, C. D., and Koopmans, L. V. E · 2012
Earlier work this paper cites.
CLASH: Three Strongly Lensed Images of a Candidate z ≈ \approx 11 Galaxy
Coe, D., Zitrin, A., Carrasco, M., Shu, X., Zheng, W., Postman, M., Bradley, L., Koekemoer, A., Bouwens, R., Broadhurst, T., Monna, A., Host, O., Moustakas, L. A., Ford, H., Moustakas, J., van der Wel, A., Donahue, M., Rodney, S. A., Benítez, N., Jouvel, S., Seitz, S., Kelson, D. D., and Rosati, P · 2013
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., and Hochreiter, S · 2015
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The Population of Galaxy-Galaxy Strong Lenses in Forthcoming Optical Imaging Surveys
Collett, T. E · 2015
Earlier work this paper cites.
Detection of Lensing Substructure Using ALMA Observations of the Dusty Galaxy SDP.81
Hezaveh, Y. D., Dalal, N., Marrone, D. P., Mao, Y.-Y., Morningstar, W., Wen, D., Blandford, R. D., Carlstrom, J. E., Fassnacht, C. D., Holder, G. P., Kemball, A., Marshall, P. J., Murray, N., Perreault Levasseur, L., Vieira, J. D., and Wechsler, R. H · 2016
Earlier work this paper cites.
Fast automated analysis of strong gravitational lenses with convolutional neural networks
Hezaveh, Y. D., Perreault Levasseur, L., and Marshall, P. J · 2017
Earlier work this paper cites.
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., and Póczos, B · 2018
Earlier work this paper cites.
Deep convolutional neural networks as strong gravitational lens detectors
Schaefer, C., Geiger, M., Kuntzer, T., and Kneib, J. P · 2018
Cited alongside, same era.
Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning
Brehmer, J., Mishra-Sharma, S., Hermans, J., Louppe, G., and Cranmer, K · 2019
Cited alongside, same era.
Extracting distribution parameters from multiple uncertain observations with selection biases
Mandel, I., Farr, W. M., and Gair, J. R · 2019
Cited alongside, same era.
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., Flamary, R., Gavazzi, R., Geiger, M., Hartley, P., Huertas-Company, M., Jackson, N., Jacobs, C., Jullo, E., Kneib, J. P., Koopmans, L. V. E., Lanusse, F., Li, C. L., Ma, Q., Makler, M., Li, N., Lightman, M., Petrillo, C. E., Serjeant, S., Schäfer, C., Sonnenfeld, A., Tagore, A., Tortora, C., Tuccillo, D., Valentín, M. B., Velasco-Forero, S., Verdoes Kleijn, G. A., and Vernardos, G · 2019
Cited alongside, same era.
Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies
Population-informed priors in gravitational-wave astronomy
Moore, C. J. and Gerosa, D · 2021
Later among the works it cites.
Large-scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
Park, J. W., Wagner-Carena, S., Birrer, S., Marshall, P. J., Lin, J. Y.-Y., Roodman, A., and LSST Dark Energy Science Collaboration · 2021
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Savary, E., Rojas, K., Maus, M., Clément, B., Courbin, F., Gavazzi, R., Chan, J. H. H., Lemon, C., Vernardos, G., Cañameras, R., Schuldt, S., Suyu, S. H., Cuillandre, J. C., Fabbro, S., Gwyn, S., Hudson, M. J., Kilbinger, M., Scott, D., and Stone, C · 2021
Later among the works it cites.
Statistical strong lensing. II. Cosmology and galaxy structure with time-delay lenses
Sonnenfeld, A · 2021
Later among the works it cites.
Statistical strong lensing. I. Constraints on the inner structure of galaxies from samples of a thousand lenses
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Coogan, A., Karchev, K., and Weniger, C · 2020
Cited alongside, same era.
New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey
Li, R., Napolitano, N. R., Tortora, C., Spiniello, C., Koopmans, L. V. E., Huang, Z., Roy, N., Vernardos, G., Chatterjee, S., Giblin, B., Getman, F., Radovich, M., Covone, G., and Kuijken, K · 2020
Cited alongside, same era.
H0LiCOW – XIII. A 2.4 per cent measurement of H 0
Wong, K. C., Suyu, S. H., Chen, G. C. F., Rusu, C. E., Millon, M., Sluse, D., Bonvin, V., Fassnacht, C. D., Taubenberger, S., Auger, M. W., Birrer, S., Chan, J. H. H., Courbin, F., Hilbert, S., Tihhonova, O., Treu, T., Agnello, A., Ding, X., Jee, I., Komatsu, E., Shajib, A. J., Sonnenfeld, A., Blandford, R. D., Koopmans, L. V. E., Marshall, P. J., and Meylan, G · 2020
Cited alongside, same era.
Unbiased likelihood-free inference of the Hubble constant from light standard sirens
Gerardi, F., Feeney, S. M., and Alsing, J · 2021
Cited alongside, same era.
Simulation-Based Inference of Strong Gravitational Lensing Parameters
Legin, R., Hezaveh, Y., Perreault Levasseur, L., and Wandelt, B · 2021
Cited alongside, same era.
Statistical strong lensing. III. Inferences with complete samples of lenses
Sonnenfeld, A
Cited in the paper.
Statistical strong lensing. IV. Inferences with no individual source redshifts
Sonnenfeld, A
Cited in the paper.
Sonnenfeld, A. and Cautun, M · 2021
Later among the works it cites.
Hierarchical Inference with Bayesian Neural Networks: An Application to Strong Gravitational Lensing
Wagner-Carena, S., Park, J. W., Birrer, S., Marshall, P. J., Roodman, A., Wechsler, R. H., and LSST Dark Energy Science Collaboration · 2021
Later among the works it cites.
GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling
Gu, A., Huang, X., Sheu, W., Aldering, G., Bolton, A. S., Boone, K., Dey, A., Filipp, A., Jullo, E., Perlmutter, S., Rubin, D., Schlafly, E. F., Schlegel, D. J., Shu, Y., and Suyu, S. H · 2022
Closest in time.
Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
Perreault Levasseur, L., Hezaveh, Y. D., and Wechsler, R. H · 2041
Closest in time.