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arxiv: 2403.02314 · v1 · pith:SX5I2YCRnew · submitted 2024-03-04 · 🌌 astro-ph.CO

Dark Energy Survey Year 3 results: likelihood-free, simulation-based wCDM inference with neural compression of weak-lensing map statistics

N. Jeffrey , L. Whiteway , M. Gatti , J. Williamson , J. Alsing , A. Porredon , J. Prat , C. Doux
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B. Jain C. Chang T.-Y. Cheng T. Kacprzak P. Lemos A. Alarcon A. Amon K. Bechtol M. R. Becker G. M. Bernstein A. Campos A. Carnero Rosell R. Chen A. Choi J. DeRose A. Drlica-Wagner K. Eckert S. Everett A. Fert\'e D. Gruen R. A. Gruendl K. Herner M. Jarvis J. McCullough J. Myles A. Navarro-Alsina S. Pandey M. Raveri R. P. Rollins E. S. Rykoff C. S\'anchez L. F. Secco I. Sevilla-Noarbe E. Sheldon T. Shin M. A. Troxel I. Tutusaus T. N. Varga B. Yanny B. Yin J. Zuntz M. Aguena S. S. Allam O. Alves D. Bacon S. Bocquet D. Brooks L. N. da Costa T. M. Davis J. De Vicente S. Desai H. T. Diehl I. Ferrero J. Frieman J. Garc\'ia-Bellido E. Gaztanaga G. Giannini G. Gutierrez S. R. Hinton D. L. Hollowood K. Honscheid D. Huterer D. J. James O. Lahav S. Lee J. L. Marshall J. Mena-Fern\'andez R. Miquel A. Pieres A. A. Plazas Malag\'on A. Roodman M. Sako E. Sanchez D. Sanchez Cid M. Smith E. Suchyta M. E. C. Swanson G. Tarle D. L. Tucker N. Weaverdyck J. Weller P. Wiseman M. Yamamoto
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keywords inferencedatadarkcompressionenergymap-levelmockneural
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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 $-1<w<-\frac{1}{3}$, our most constraining result uses power spectra combined with map-level (CNN) inference. Using gravitational lensing data only, this map-level combination gives $\Omega_{\rm m} = 0.283^{+0.020}_{-0.027}$, ${S_8 = 0.804^{+0.025}_{-0.017}}$, and $w < -0.80$ (with a 68 per cent credible interval); compared to the power spectrum inference, this is more than a factor of two improvement in dark energy parameter ($\Omega_{\rm DE}, w$) precision.

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