{"id":"https://openalex.org/W7224108686","doi":"https://doi.org/10.48550/arxiv.2610.37254","title":"Global Analysis of Nuclear Parton Distribution Functions using Markov Chain Monte Carlo","display_name":"Global Analysis of Nuclear Parton Distribution Functions using Markov Chain Monte Carlo","publication_year":2026,"publication_date":"2026-10-27","ids":{"openalex":"https://openalex.org/W7224108686","doi":"https://doi.org/10.48550/arxiv.2610.37254"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.37254","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.37254","pdf_url":"https://arxiv.org/pdf/2610.37254","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.37254","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092491160","display_name":"N. Derakhshanian","orcid":"https://orcid.org/0000-0003-0962-631X","observed_orcids":["https://orcid.org/0000-0003-0962-631X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Derakhshanian, Nasim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5092491160"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"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/T10224","display_name":"Quantum Chromodynamics and Particle Interactions","score":0.9726999998092651,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"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/T10224","display_name":"Quantum Chromodynamics and Particle Interactions","score":0.9726999998092651,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"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/T10048","display_name":"Particle physics theoretical and experimental studies","score":0.009100000374019146,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"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/T10527","display_name":"High-Energy Particle Collisions Research","score":0.007199999876320362,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"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/nuclear-parton-distribution-functions","display_name":"nuclear parton distribution functions","score":0.9990000128746033},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.9139999747276306},{"id":"https://openalex.org/keywords/adaptive-metropolis-hastings","display_name":"adaptive Metropolis-Hastings","score":0.8349999785423279},{"id":"https://openalex.org/keywords/lead-nuclei","display_name":"lead nuclei","score":0.5400000214576721},{"id":"https://openalex.org/keywords/non-gaussian-posterior","display_name":"non-Gaussian posterior","score":0.5799999833106995},{"id":"https://openalex.org/keywords/multimodal-posterior","display_name":"multimodal posterior","score":0.492000013589859},{"id":"https://openalex.org/keywords/valence-quark-distributions","display_name":"valence quark distributions","score":0.7910000085830688},{"id":"https://openalex.org/keywords/gluon-distribution-function","display_name":"gluon distribution","score":0.8230000138282776},{"id":"https://openalex.org/keywords/mass-number-dependence","display_name":"mass number dependence","score":0.7739999890327454},{"id":"https://openalex.org/keywords/nuclear-effects-in-deep-inelastic-scattering","display_name":"nuclear effects in deep inelastic scattering","score":0.5889999866485596}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7444999814033508},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.7365999817848206},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.6116999983787537},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5327000021934509},{"id":"https://openalex.org/C180566339","wikidata":"https://www.wikidata.org/wiki/Q117483","display_name":"Parton","level":3,"score":0.4876999855041504},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4717000126838684},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4489000141620636},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.41940000653266907},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.41269999742507935},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.3944000005722046},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.3878999948501587},{"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/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.367900013923645},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.33869999647140503},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3287000060081482},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3142000138759613},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3089999854564667},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2948000133037567},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C132725507","wikidata":"https://www.wikidata.org/wiki/Q39879","display_name":"Monte Carlo integration","level":5,"score":0.2596000134944916},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.2581999897956848},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25699999928474426},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C186603090","wikidata":"https://www.wikidata.org/wiki/Q55505926","display_name":"Distribution function","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.37254","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.37254","pdf_url":"https://arxiv.org/pdf/2610.37254","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.37254","doi":"https://doi.org/10.48550/arxiv.2610.37254","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.37254","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.37254","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.37254","pdf_url":"https://arxiv.org/pdf/2610.37254","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322650","display_name":"Narodowym Centrum Nauki","ror":"https://ror.org/03ha2q922"},{"id":"https://openalex.org/F4320328499","display_name":"Narodowa Agencja Wymiany Akademickiej","ror":"https://ror.org/02jf81j23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224108686.pdf","grobid_xml":"https://content.openalex.org/works/W7224108686.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7138824196","https://openalex.org/W4386499824","https://openalex.org/W4405848558","https://openalex.org/W2898386958","https://openalex.org/W4415950384","https://openalex.org/W6949969811","https://openalex.org/W2222757508","https://openalex.org/W2963481988","https://openalex.org/W2266823686","https://openalex.org/W2964112146"],"abstract_inverted_index":{"Global":[0],"analyses":[1],"of":[2,23,83,107,162,192],"nuclear":[3,123],"parton":[4],"distribution":[5,150],"functions":[6],"(nPDFs)":[7],"have":[8],"traditionally":[9],"used":[10],"the":[11,24,72,84,96,102,131,143,148,160,163,196],"Hessian":[12],"method":[13],"for":[14,61,184],"uncertainty":[15],"estimation,":[16],"relying":[17],"on":[18,113,130],"a":[19,58,118,126,181,188],"local":[20],"quadratic":[21],"approximation":[22,29,165],"chi-squared":[25],"function.":[26],"This":[27,55],"Gaussian":[28,164],"can":[30],"be":[31],"inadequate":[32],"when":[33],"experimental":[34],"constraints":[35],"are":[36,41],"limited":[37],"and":[38,91,141,195],"parameter":[39,193,198],"correlations":[40],"strong,":[42],"as":[43,180],"it":[44],"cannot":[45],"fully":[46],"capture":[47],"asymmetric":[48],"uncertainties,":[49],"flat":[50],"directions,":[51],"or":[52,171],"multiple":[53],"minima.":[54],"thesis":[56],"develops":[57],"Bayesian":[59,177],"framework":[60,183],"nPDF":[62,185],"determination":[63,106],"using":[64],"Markov":[65],"Chain":[66],"Monte":[67],"Carlo":[68],"(MCMC)":[69],"techniques":[70],"within":[71],"nCTEQ":[73],"analysis":[74,100,120],"framework.":[75],"An":[76],"adaptive":[77],"Metropolis-Hastings":[78],"algorithm":[79],"enables":[80],"direct":[81],"sampling":[82],"posterior":[85],"distribution,":[86],"revealing":[87],"pronounced":[88],"non-Gaussian":[89],"behavior":[90],"secondary":[92],"minima,":[93],"particularly":[94],"in":[95,166],"valence":[97],"distributions.":[98],"The":[99],"presents":[101],"first":[103],"global":[104],"single-nucleus":[105],"lead":[108,114,145],"(Pb)":[109],"PDFs":[110],"based":[111],"exclusively":[112],"data,":[115],"complemented":[116],"by":[117],"multi-nuclei":[119],"incorporating":[121],"different":[122],"targets":[124],"through":[125],"standard":[127],"analytic":[128],"dependence":[129],"mass":[132],"number.":[133],"Including":[134],"lighter":[135],"nuclei":[136],"significantly":[137],"constrains":[138],"quark":[139],"distributions":[140],"changes":[142],"extracted":[144],"PDFs,":[146],"while":[147],"gluon":[149],"remains":[151],"comparatively":[152],"stable.":[153],"A":[154],"comparison":[155],"with":[156,168],"Hessian-based":[157],"results":[158],"highlights":[159],"limitations":[161],"regions":[167],"strong":[169],"nonlinearity":[170],"multimodality.":[172],"Overall,":[173],"this":[174],"work":[175],"establishes":[176],"MCMC":[178],"inference":[179],"flexible":[182],"determination,":[186],"providing":[187],"more":[189],"comprehensive":[190],"characterization":[191],"uncertainties":[194],"underlying":[197],"space.":[199]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2026-10-29T00:00:00"}
