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dc.contributor.author
Fluri J.
dc.contributor.author
Kacprzak T.
dc.contributor.author
Lucchi A.
dc.contributor.author
Schneider A.
dc.contributor.author
Refregier A.
dc.contributor.author
Hofmann T.
dc.date.accessioned
2022-05-14T03:54:12Z
dc.date.available
2022-05-14T03:54:12Z
dc.date.issued
2022-04-15
dc.identifier.issn
1550-7998
dc.identifier.issn
0556-2821
dc.identifier.issn
1550-2368
dc.identifier.other
10.1103/PhysRevD.105.083518
dc.identifier.uri
http://hdl.handle.net/20.500.11850/547183
dc.description.abstract
We present a full forward-modeled wCDM analysis of the KiDS-1000 weak lensing maps using graph-convolutional neural networks (GCNN). Utilizing the cosmogrid, a novel massive simulation suite spanning six different cosmological parameters, we generate almost one million tomographic mock surveys on the sphere. Due to the large dataset size and survey area, we perform a spherical analysis while limiting our map resolution to HEALPix nside=512. We marginalize over systematics such as photometric redshift errors, multiplicative calibration and additive shear bias. Furthermore, we use a map-level implementation of the nonlinear intrinsic alignment model along with a novel treatment of baryonic feedback to incorporate additional astrophysical nuisance parameters. We also perform a spherical power spectrum analysis for comparison. The constraints of the cosmological parameters are generated using a likelihood-free inference method called Gaussian process approximate Bayesian computation (GPABC). Finally, we check that our pipeline is robust against choices of the simulation parameters. We find constraints on the degeneracy parameter of S8σ8ωM/0.3=0.78-0.06+0.06 for our power spectrum analysis and S8=0.79-0.05+0.05 for our GCNN analysis, improving the former by 16%. This is consistent with earlier analyses of the 2-point function, albeit slightly higher. Baryonic corrections generally broaden the constraints on the degeneracy parameter by about 10%. These results offer great prospects for full machine learning based analyses of ongoing and future weak lensing surveys.
dc.title
Full wCDM analysis of KiDS-1000 weak lensing maps using deep learning
dc.type
Journal Article
ethz.journal.title
Physical Review D
ethz.journal.volume
105
ethz.journal.issue
8
ethz.journal.abbreviated
Phys. rev. D.
ethz.identifier.scopus
ethz.date.deposited
2022-05-14T03:54:17Z
ethz.source
SCOPUS
ethz.rosetta.exportRequired
true
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