학술논문
Dark Energy Survey Year 3 results: likelihood-free, simulation-based $w$CDM inference with neural compression of weak-lensing map statistics
Document Type
Working Paper
Author
Jeffrey, N.; Whiteway, L.; Gatti, M.; Williamson, J.; Alsing, J.; Porredon, A.; Prat, J.; Doux, C.; Jain, B.; Chang, C.; Cheng, T. -Y.; Kacprzak, T.; Lemos, P.; Alarcon, A.; Amon, A.; Bechtol, K.; Becker, M. R.; Bernstein, G. M.; Campos, A.; Rosell, A. Carnero; Chen, R.; Choi, A.; DeRose, J.; Drlica-Wagner, A.; Eckert, K.; Everett, S.; Ferté, A.; Gruen, D.; Gruendl, R. A.; Herner, K.; Jarvis, M.; McCullough, J.; Myles, J.; Navarro-Alsina, A.; Pandey, S.; Raveri, M.; Rollins, R. P.; Rykoff, E. S.; Sánchez, C.; Secco, L. F.; Sevilla-Noarbe, I.; Sheldon, E.; Shin, T.; Troxel, M. A.; Tutusaus, I.; Varga, T. N.; Yanny, B.; Yin, B.; Zuntz, J.; Aguena, M.; Allam, S. S.; Alves, O.; Bacon, D.; Bocquet, S.; Brooks, D.; da Costa, L. N.; Davis, T. M.; De Vicente, J.; Desai, S.; Diehl, H. T.; Ferrero, I.; Frieman, J.; García-Bellido, J.; Gaztanaga, E.; Giannini, G.; Gutierrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; Huterer, D.; James, D. J.; Lahav, O.; Lee, S.; Marshall, J. L.; Mena-Fernández, J.; Miquel, R.; Pieres, A.; Malagón, A. A. Plazas; Roodman, A.; Sako, M.; Sanchez, E.; Cid, D. Sanchez; Smith, M.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Tucker, D. L.; Weaverdyck, N.; Weller, J.; Wiseman, P.; Yamamoto, M.
Source
Subject
Language
Abstract
We present simulation-based cosmological $w$CDM inference using Dark Energy Survey Year 3 weak-lensing maps, via neural data compression of weak-lensing map summary statistics: power spectra, peak counts, and direct map-level compression/inference with convolutional neural networks (CNN). Using simulation-based inference, also known as likelihood-free or implicit inference, we use forward-modelled mock data to estimate posterior probability distributions of unknown parameters. This approach allows all statistical assumptions and uncertainties to be propagated through the forward-modelled mock data; these include sky masks, non-Gaussian shape noise, shape measurement bias, source galaxy clustering, photometric redshift uncertainty, intrinsic galaxy alignments, non-Gaussian density fields, neutrinos, and non-linear summary statistics. We include a series of tests to validate our inference results. This paper also describes the Gower Street simulation suite: 791 full-sky PKDGRAV dark matter simulations, with cosmological model parameters sampled with a mixed active-learning strategy, from which we construct over 3000 mock DES lensing data sets. For $w$CDM inference, for which we allow $-1