https://w3id.org/sciencelive/np/RA0uFwTDq3Ip2_M_ZeHxmfavOu3ZCx9ZEeYSbXE0quHQk
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this: a np:Nanopublication;
np:hasAssertion sub:assertion;
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np:hasPublicationInfo sub:pubinfo .
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sub:assertion {
sub:delouis-2022-lss-replication-study a <https://w3id.org/sciencelive/o/terms/FORRT-Replication-Study>,
<https://w3id.org/sciencelive/o/terms/Replication-Study>;
<http://www.w3.org/2000/01/rdf-schema#label> "Scattering synthesis on a cosmological LSS map (Delouis 2022 replication)";
<http://www.w3.org/2004/02/skos/core#related> <http://www.wikidata.org/entity/Q37547>,
<http://www.wikidata.org/entity/Q5629401>, <http://www.wikidata.org/entity/Q7048759>;
<https://w3id.org/sciencelive/o/terms/hasDeviationDescription> """Three substantive differences from Delouis et al. 2022:
1. Input data: cosmological large-scale-structure map (LSS_map_nside128.npy from the FOSCAT_DEMO repository) rather than Planck SRoll2 353 GHz dust polarisation Stokes Q and U maps.
2. Application: synthesis of a new map from random noise such that its scattering statistics match a target — rather than the FoCUS component-separation algorithm the paper used to denoise Planck observations.
3. Resolution: nside=32 (12,288 pixels), downgraded from the input map's native nside=128. The paper uses nside=256. The lower resolution allows execution on CPU rather than requiring GPU, which keeps the replication runnable on commodity hardware.
The library, the scattering-transform implementation, and the underlying methodology are identical to the paper's. The deviations are in the data, the target task (synthesis vs denoising), and the resolution.""";
<https://w3id.org/sciencelive/o/terms/hasDiscipline> <http://www.wikidata.org/entity/Q338>;
<https://w3id.org/sciencelive/o/terms/hasMethodologyDescription> "We use the FOSCAT Python package (github.com/jmdelouis/FOSCAT) authored by Delouis — the same software used in the original paper. Scattering coefficients are computed via FOSCAT's scat_cov.funct(NORIENT=4, KERNELSZ=3) on a HEALPix sphere; synthesis is run from random noise using foscat.Synthesis with an L-BFGS optimiser over 300 epochs. The loss is the mean squared difference between the target's scattering coefficients and those of the current synthesised map, normalised by the variance of the target coefficients. We evaluate the synthesised map against the target via three metrics: power spectrum ratio (mean of synthesised / target across multipoles), scattering coefficient improvement percentage, and pixel-level correlation. Inference runs on CPU.";
<https://w3id.org/sciencelive/o/terms/hasScopeDescription> "This study tests the generalisation claim of Delouis et al. 2022 — that the scattering-transform framework developed for Planck dust polarisation can be applied to other processes defined on the sphere. We test that claim on a cosmological large-scale-structure (LSS) map rather than the paper's Planck dust polarisation data. Specifically, we evaluate whether scattering-transform synthesis from random noise can produce a new astrophysical map whose multi-scale statistics (power spectrum and scattering coefficients) match those of a target input.";
<https://w3id.org/sciencelive/o/terms/targetsClaim> <https://w3id.org/sciencelive/np/RANzldYFCS3QYLbv8mQ7yCzVrr83sjOMU11GueTODExw4/delouis-2022-spherical-scattering-claim> .
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sub:pubinfo {
<https://orcid.org/0000-0002-1784-2920> <http://xmlns.com/foaf/0.1/name> "Anne Fouilloux" .
this: dc:created "2026-04-26T14:52:17.764Z"^^xsd:dateTime;
dc:creator <https://orcid.org/0000-0002-1784-2920>;
dc:license <https://creativecommons.org/licenses/by/4.0/>;
npx:introduces sub:delouis-2022-lss-replication-study;
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<http://www.w3.org/2000/01/rdf-schema#label> "NP created using Declaring a replication study design according to FORRT";
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