학술논문
Dark Energy Survey Year 3 results: simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps II. Cosmological results
Document Type
Working Paper
Author
Gatti, M.; Campailla, G.; Jeffrey, N.; Whiteway, L.; Porredon, A.; Prat, J.; Williamson, J.; Raveri, M.; Jain, B.; Ajani, V.; Giannini, G.; Yamamoto, M.; Zhou, C.; Blazek, J.; Anbajagane, D.; Samuroff, S.; Kacprzak, T.; Alarcon, A.; Amon, A.; Bechtol, K.; Becker, M.; Bernstein, G.; Campos, A.; Chang, C.; Chen, R.; Choi, A.; Davis, C.; Derose, J.; Diehl, H. T.; Dodelson, S.; Doux, C.; Eckert, K.; Elvin-Poole, J.; Everett, S.; Ferte, A.; Gruen, D.; Gruendl, R.; Harrison, I.; Hartley, W. G.; Herner, K.; Huff, E. M.; Jarvis, M.; Kuropatkin, N.; Leget, P. F.; MacCrann, N.; McCullough, J.; Myles, J.; Navarro-Alsina, A.; Pandey, S.; Rollins, R. P.; Roodman, A.; Sanchez, C.; Secco, L. F.; Sevilla-Noarbe, I.; Sheldon, E.; Shin, T.; Troxel, M.; Tutusaus, I.; Varga, T. N.; Yanny, B.; Yin, B.; Zhang, Y.; Zuntz, J.; Abbott, T. M. C.; Aguena, M.; Allam, S. S.; Alves, O.; Andrade-Oliveira, F.; Bacon, D.; Bocquet, S.; Brooks, D.; Rosell, A. Carnero; Carretero, J.; da Costa, L. N.; Pereira, M. E. S.; De Vicente, J.; Ferrero, I.; Frieman, J.; García-Bellido, J.; Gaztanaga, E.; Gutierrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; James, D. J.; Kuehn, K.; Lahav, O.; Lee, S.; Marshall, J. L.; Mena-Fernández, J.; Miquel, R.; Pieres, A.; Malagón, A. A. Plazas; Sanchez, E.; Cid, D. Sanchez; Schubnell, M.; Smith, M.; Suchyta, E.; Tarle, G.; Weaverdyck, N.; Weller, J.; Wiseman, P.
Source
Subject
Language
Abstract
We present a simulation-based cosmological analysis using a combination of Gaussian and non-Gaussian statistics of the weak lensing mass (convergence) maps from the first three years (Y3) of the Dark Energy Survey (DES). We implement: 1) second and third moments; 2) wavelet phase harmonics; 3) the scattering transform. Our analysis is fully based on simulations, spans a space of seven $\nu w$CDM cosmological parameters, and forward models the most relevant sources of systematics inherent in the data: masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration. We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a simulation-based inference approach. Including and combining different non-Gaussian statistics is a powerful tool that strongly improves constraints over Gaussian statistics (in our case, the second moments); in particular, the Figure of Merit $\textrm{FoM}(S_8, \Omega_{\textrm{m}})$ is improved by 70 percent ($\Lambda$CDM) and 90 percent ($w$CDM). When all the summary statistics are combined, we achieve a 2 percent constraint on the amplitude of fluctuations parameter $S_8 \equiv \sigma_8 (\Omega_{\textrm{m}}/0.3)^{0.5}$, obtaining $S_8 = 0.794 \pm 0.017$ ($\Lambda$CDM) and $S_8 = 0.817 \pm 0.021$ ($w$CDM). The constraints from different statistics are shown to be internally consistent (with a $p$-value>0.1 for all combinations of statistics examined). We compare our results to other weak lensing results from the DES Y3 data, finding good consistency; we also compare with results from external datasets, such as \planck{} constraints from the Cosmic Microwave Background, finding statistical agreement, with discrepancies no greater than $<2.2\sigma$.
Comment: 24 pages, 13 figures, to be submitted to PRD. Comments welcome!
Comment: 24 pages, 13 figures, to be submitted to PRD. Comments welcome!