{"id":"https://openalex.org/W7140213864","doi":"https://doi.org/10.48550/arxiv.2603.22006","title":"A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping","display_name":"A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140213864","doi":"https://doi.org/10.48550/arxiv.2603.22006"},"language":"en","primary_location":{"id":"pmh:oai:HAL:hal-05586347v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05586347","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Astronomy and Astrophysics, In press","raw_type":"info:eu-repo/semantics/article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.22006","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020124529","display_name":"Hubert Leterme","orcid":"https://orcid.org/0000-0002-4840-5299"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leterme, Hubert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116128731","display_name":"Andreas Tersenov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tersenov, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5146643774","display_name":"Jalal Fadili","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fadili, Jalal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5146616594","display_name":"Jean-Luc Starck","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Starck, Jean-Luc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30019148,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10026","display_name":"Galaxies: Formation, Evolution, Phenomena","score":0.8891000151634216,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10026","display_name":"Galaxies: Formation, Evolution, Phenomena","score":0.8891000151634216,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12450","display_name":"Radio Astronomy Observations and Technology","score":0.014700000174343586,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12917","display_name":"Astronomy and Astrophysical Research","score":0.013199999928474426,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/weak-gravitational-lensing","display_name":"Weak gravitational lensing","score":0.5439000129699707},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.46619999408721924},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43070000410079956},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4251999855041504},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.40619999170303345},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.392300009727478},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.38109999895095825},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.36160001158714294},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.35589998960494995}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5440999865531921},{"id":"https://openalex.org/C190670322","wikidata":"https://www.wikidata.org/wiki/Q13073796","display_name":"Weak gravitational lensing","level":4,"score":0.5439000129699707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5325999855995178},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43070000410079956},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4251999855041504},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.40619999170303345},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.38109999895095825},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.35409998893737793},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3314000070095062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3273000121116638},{"id":"https://openalex.org/C159249277","wikidata":"https://www.wikidata.org/wiki/Q79925","display_name":"Dark matter","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26589998602867126},{"id":"https://openalex.org/C134222618","wikidata":"https://www.wikidata.org/wiki/Q2713132","display_name":"Mass distribution","level":3,"score":0.2597000002861023},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.25870001316070557},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.2574999928474426},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:HAL:hal-05586347v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05586347","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Astronomy and Astrophysics, In press","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.2603.22006","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22006","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.22006","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22006","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Upcoming":[0],"stage-IV":[1],"surveys":[2],"such":[3],"as":[4],"Euclid":[5],"and":[6,91,140,190,218,239],"Rubin":[7],"will":[8],"deliver":[9],"vast":[10],"amounts":[11],"of":[12,34,77,201,227,231],"high-precision":[13],"data,":[14],"opening":[15],"new":[16,60,85],"opportunities":[17],"to":[18,180,199],"constrain":[19],"cosmological":[20,108],"models":[21],"with":[22,100,133,145,172],"unprecedented":[23],"accuracy.":[24],"A":[25],"key":[26],"step":[27,132,143],"in":[28,213],"this":[29],"process":[30],"is":[31,88,103],"the":[32,35,202,228,232],"reconstruction":[33,50,240],"dark":[36],"matter":[37],"distribution":[38],"from":[39],"noisy":[40],"weak-lensing":[41,118,256],"shear":[42],"measurements.":[43],"Current":[44],"deep-learning-based":[45],"mass-mapping":[46,192],"methods":[47,206],"achieve":[48],"high":[49],"accuracy,":[51],"but":[52],"either":[53],"require":[54],"retraining":[55],"a":[56,84,106,114,129,134,141,146,162,196,215,222],"model":[57,149],"for":[58,83,105,117,254],"each":[59],"observed":[61],"sky":[62],"region":[63],"(limiting":[64],"practicality)":[65],"or":[66],"rely":[67],"on":[68,151,169],"slow":[69],"Markov":[70],"chain":[71],"Monte":[72],"Carlo":[73],"sampling.":[74],"Efficient":[75],"exploitation":[76],"future":[78],"survey":[79],"data":[80,137,153],"therefore":[81,235],"calls":[82],"method":[86],"that":[87,200],"accurate,":[89],"flexible,":[90],"fast":[92,163,209],"at":[93],"inference.":[94,210],"In":[95],"addition,":[96],"an":[97],"uncertainty":[98,165],"quantification":[99,166],"coverage":[101,182],"guarantees":[102],"essential":[104],"reliable":[107],"parameter":[109],"estimation.":[110],"We":[111,159],"introduce":[112],"PnPMass,":[113],"plug-and-play":[115],"approach":[116],"mass":[119],"mapping.":[120],"The":[121],"algorithm":[122],"produces":[123],"point":[124],"estimates":[125],"by":[126,155],"alternating":[127],"between":[128],"gradient":[130],"descent":[131],"carefully":[135],"chosen":[136],"fidelity":[138],"term":[139],"denoising":[142],"implemented":[144],"single":[147,223],"deep-learning":[148,205],"trained":[150],"simulated":[152],"corrupted":[154],"Gaussian":[156],"white":[157],"noise.":[158],"also":[160],"propose":[161],"sampling-free":[164],"scheme":[167],"based":[168],"moment":[170],"networks,":[171],"calibrated":[173],"error":[174,245],"bars":[175,246],"obtained":[176],"through":[177],"conformal":[178],"prediction":[179],"ensure":[181],"guarantees.":[183],"Finally,":[184],"we":[185],"benchmark":[186],"PnPMass":[187,194],"against":[188],"model-driven":[189],"data-driven":[191],"techniques.":[193],"achieves":[195],"performance":[197],"close":[198],"currently":[203],"best":[204],"while":[207,242],"offering":[208],"It":[211,234],"converges":[212],"just":[214],"few":[216],"iterations,":[217],"it":[219,251],"requires":[220],"only":[221],"training":[224],"phase,":[225],"regardless":[226],"noise":[229],"covariance":[230],"observations.":[233],"combines":[236],"flexibility,":[237],"efficiency,":[238],"accuracy":[241],"delivering":[243],"tighter":[244],"than":[247],"existing":[248],"approaches,":[249],"making":[250],"well":[252],"suited":[253],"upcoming":[255],"surveys.":[257]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2026-03-25T00:00:00"}