{"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/alma-imaging-and-gravitational-lens-models-of","title":"ALMA Imaging and Gravitational Lens Models of South Pole Telescope-Selected Dusty, Star-Forming Galaxies at High Redshifts","arxiv_id":"1604.05723","date":"2016-04-19","proceeding":null,"authors":["Justin Spilker","Daniel Marrone","Manuel Aravena","Matthieu Bethermin","Matt Bothwell","John Carlstrom","Scott Chapman","Tom Crawford","Carlos de Breuck","Chris Fassnacht","Anthony Gonzalez","Thomas Greve","Yashar Hezaveh","Katrina Litke","Jingzhe Ma","Matt Malkan","Kaja Rotermund","Maria Strandet","Joaquin Vieira","Axel Weiss","Niraj Welikala"],"abstract":"The South Pole Telescope has discovered one hundred gravitationally lensed, high-redshift, dusty, star-forming galaxies (DSFGs). We present 0.5\" resolution 870um Atacama Large Millimeter/submillimeter Array imaging of a sample of 47 DSFGs spanning z=1.9-5.7, and construct gravitational lens models of these sources. Our visibility-based lens modeling incorporates several sources of residual interferometric calibration uncertainty, allowing us to properly account for noise in the observations. At least 70% of the sources are strongly lensed by foreground galaxies (mu_870um > 2), with a median magnification mu_870um = 6.3, extending to mu_870um > 30. We compare the intrinsic size distribution of the strongly lensed sources to a similar number of unlensed DSFGs and find no significant differences in spite of a bias between the magnification and intrinsic source size. This may indicate that the true size distribution of DSFGs is relatively narrow. We use the source sizes to constrain the wavelength at which the dust optical depth is unity and find this wavelength to be correlated with the dust temperature. This correlation leads to discrepancies in dust mass estimates of a factor of 2 compared to estimates using a single value for this wavelength. We investigate the relationship between the [CII] line and the far-infrared luminosity and find that the same correlation between the [CII]L_FIR ratio and Sigma_FIR found for low-redshift star-forming galaxies applies to high-redshift galaxies and extends at least two orders of magnitude higher in Sigma_FIR. This lends further credence to the claim that the compactness of the IR-emitting region is the controlling parameter in establishing the \"[CII] deficit.\"","url_abs":"http://arxiv.org/abs/1604.05723v1","url_pdf":"http://arxiv.org/pdf/1604.05723v1.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":"alma-imaging-and-gravitational-lens-models-of","repo_url":"https://github.com/jspilker/visilens","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.05723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.05723"}},"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/jspilker/visilens","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":14},"by_repo_kind":{"official":{"samples":14,"ran":0,"repositories":1}},"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":"aa7d776e7198b253","entry":"GenerateLensingGrid","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/lensing.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/lensing.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"aa7d776e7198b253"}},{"code_sha256_prefix":"638d530755e28947","entry":"LensRayTrace","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/lensing.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/lensing.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"638d530755e28947"}},{"code_sha256_prefix":"b64e5ae768ccd364","entry":"SourceProfile","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/calc_likelihood.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/calc_likelihood.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b64e5ae768ccd364"}},{"code_sha256_prefix":"e74f0fb6d2c60583","entry":"TrianglePlot_MCMC","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/triangleplot.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/triangleplot.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e74f0fb6d2c60583"}},{"code_sha256_prefix":"6d9e9c0c063acef5","entry":"bin_visibilities","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/class_utils.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/class_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6d9e9c0c063acef5"}},{"code_sha256_prefix":"45f52d762752536d","entry":"box","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/utils.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"45f52d762752536d"}},{"code_sha256_prefix":"76b6ed6840c04c0b","entry":"calc_vis_lnlike","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/calc_likelihood.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/calc_likelihood.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"76b6ed6840c04c0b"}},{"code_sha256_prefix":"c7a20bf2893441bc","entry":"cart2pol","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/utils.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c7a20bf2893441bc"}},{"code_sha256_prefix":"e0082627f29e9fd3","entry":"concatvis","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/class_utils.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/class_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e0082627f29e9fd3"}},{"code_sha256_prefix":"dfbe9c4048b72abf","entry":"marginalize_2d","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/triangleplot.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/triangleplot.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dfbe9c4048b72abf"}},{"code_sha256_prefix":"9c8471f16830c9ae","entry":"pass_priors","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/calc_likelihood.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/calc_likelihood.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9c8471f16830c9ae"}},{"code_sha256_prefix":"fffa74f268492710","entry":"pol2cart","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/utils.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fffa74f268492710"}},{"code_sha256_prefix":"024a12327c0fef62","entry":"read_visdata","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/class_utils.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/class_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"024a12327c0fef62"}},{"code_sha256_prefix":"8fd5d5b31aca8c81","entry":"uvimageslow","repo":"jspilker/visilens","repo_kind":"official","path":"visilens/plot_images.py","file_url":"https://github.com/jspilker/visilens/blob/HEAD/visilens/plot_images.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8fd5d5b31aca8c81"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}