{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generative-models-of-multi-channel-data-from","title":"Generative Models of Multi-channel Data from a Single Example -- Application to Dust Emission","arxiv_id":"2208.03538","date":"2022-08-06","proceeding":null,"authors":["Bruno Régaldo-Saint Blancard","Erwan Allys","Constant Auclair","François Boulanger","Michael Eickenberg","François Levrier","Léo Vacher","Sixin Zhang"],"abstract":"The quest for primordial $B$-modes in the cosmic microwave background has emphasized the need for refined models of the Galactic dust foreground. Here, we aim at building a realistic statistical model of the multi-frequency dust emission from a single example. We introduce a generic methodology relying on microcanonical gradient descent models conditioned by an extended family of wavelet phase harmonic (WPH) statistics. To tackle the multi-channel aspect of the data, we define cross-WPH statistics, quantifying non-Gaussian correlations between maps. Our data-driven methodology could apply to various contexts, and we have updated the software PyWPH, on which this work relies, accordingly. Applying this to dust emission maps built from a magnetohydrodynamics simulation, we construct and assess two generative models of: 1) a $(I, E, B)$ multi-observable input, 2) a $\\{I_\\nu\\}_\\nu$ multi-frequency input. The samples exhibit consistent features compared to the original maps. A statistical analysis of 1) shows that the power spectra, distributions of pixels, and Minkowski functionals are captured to a good extent. We analyze 2) by fitting the spectral energy distribution (SED) of both the synthetic and original maps with a modified blackbody (MBB) law. The maps are equally well fitted, and a comparison of the MBB parameters shows that our model succeeds in capturing the spatial variations of the SED from the data. Besides the perspectives of this work for dust emission modeling, the introduction of cross-WPH statistics opens a new avenue to characterize non-Gaussian interactions across different maps, which we believe will be fruitful for astrophysics.","url_abs":"https://arxiv.org/abs/2208.03538v1","url_pdf":"https://arxiv.org/pdf/2208.03538v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"generative-models-of-multi-channel-data-from","repo_url":"https://github.com/bregaldo/dust_genmodels","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"generative-models-of-multi-channel-data-from","repo_url":"https://github.com/bregaldo/pywph","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2208.03538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.03538"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/bregaldo/dust_genmodels","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/bregaldo/pywph","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"ran_violates":1,"ran_honours":1,"ran_fixture":1,"unverified":3},"by_repo_kind":{"official":{"samples":6,"ran":3,"repositories":2}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"ad969a44def0d1b2","entry":"compute_autocovariance","repo":"bregaldo/dust_genmodels","repo_kind":"official","path":"code/synth.py","file_url":"https://github.com/bregaldo/dust_genmodels/blob/HEAD/code/synth.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"ad969a44def0d1b2"}},{"code_sha256_prefix":"8fea25193f9ae8da","entry":"generate_grf","repo":"bregaldo/dust_genmodels","repo_kind":"official","path":"code/synth.py","file_url":"https://github.com/bregaldo/dust_genmodels/blob/HEAD/code/synth.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"8fea25193f9ae8da"}},{"code_sha256_prefix":"1a5d7a535b01b3ce","entry":"get_initialization","repo":"bregaldo/dust_genmodels","repo_kind":"official","path":"code/synth.py","file_url":"https://github.com/bregaldo/dust_genmodels/blob/HEAD/code/synth.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"1a5d7a535b01b3ce"}},{"code_sha256_prefix":"132046f8f80629ec","entry":"get_precision_type","repo":"bregaldo/pywph","repo_kind":"official","path":"pywph/utils.py","file_url":"https://github.com/bregaldo/pywph/blob/HEAD/pywph/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"132046f8f80629ec"}},{"code_sha256_prefix":"6eac1adbd2c368c0","entry":"to_numpy","repo":"bregaldo/pywph","repo_kind":"official","path":"pywph/utils.py","file_url":"https://github.com/bregaldo/pywph/blob/HEAD/pywph/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"6eac1adbd2c368c0"}},{"code_sha256_prefix":"1fd693e0f7b46ef2","entry":"to_torch","repo":"bregaldo/pywph","repo_kind":"official","path":"pywph/utils.py","file_url":"https://github.com/bregaldo/pywph/blob/HEAD/pywph/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"1fd693e0f7b46ef2"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}