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

N Jeffrey, L Whiteway, M Gatti, J Williamson… - arXiv preprint arXiv …, 2024 - arxiv.org
Astrophysics paper astro-ph.CO Suggest

… (2021a) used both simulation-based inference and … are developing a simulationbased inference pipeline that uses … In section 2 we introduce simulation-based …

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@article{2403.02314v1,
Author        = {N. Jeffrey and L. Whiteway and M. Gatti and J. Williamson and J. Alsing and A. Porredon and J. Prat and C. Doux and B. Jain and C. Chang and T. -Y. Cheng and T. Kacprzak and P. Lemos and A. Alarcon and A. Amon and K. Bechtol and M. R. Becker and G. M. Bernstein and A. Campos and A. Carnero Rosell and R. Chen and A. Choi and J. DeRose and A. Drlica-Wagner and K. Eckert and S. Everett and A. Ferté and D. Gruen and R. A. Gruendl and K. Herner and M. Jarvis and J. McCullough and J. Myles and A. Navarro-Alsina and S. Pandey and M. Raveri and R. P. Rollins and E. S. Rykoff and C. Sánchez and L. F. Secco and I. Sevilla-Noarbe and E. Sheldon and T. Shin and M. A. Troxel and I. Tutusaus and T. N. Varga and B. Yanny and B. Yin and J. Zuntz and M. Aguena and S. S. Allam and O. Alves and D. Bacon and S. Bocquet and D. Brooks and L. N. da Costa and T. M. Davis and J. De Vicente and S. Desai and H. T. Diehl and I. Ferrero and J. Frieman and J. García-Bellido and E. Gaztanaga and G. Giannini and G. Gutierrez and S. R. Hinton and D. L. Hollowood and K. Honscheid and D. Huterer and D. J. James and O. Lahav and S. Lee and J. L. Marshall and J. Mena-Fernández and R. Miquel and A. Pieres and A. A. Plazas Malagón and A. Roodman and M. Sako and E. Sanchez and D. Sanchez Cid and M. Smith and E. Suchyta and M. E. C. Swanson and G. Tarle and D. L. Tucker and N. Weaverdyck and J. Weller and P. Wiseman and M. Yamamoto},
Title         = {Dark Energy Survey Year 3 results: likelihood-free, simulation-based
  $w$CDM inference with neural compression of weak-lensing map statistics},
Eprint        = {2403.02314v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
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
$-1<w<-\frac{1}{3}$, 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.},
Year          = {2024},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2403.02314v1},
File          = {2403.02314v1.pdf}
}

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