{"id":"https://openalex.org/W7152112720","doi":"https://doi.org/10.48550/arxiv.2604.05227","title":"Active Measurement of Two-Point Correlations","display_name":"Active Measurement of Two-Point Correlations","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7152112720","doi":"https://doi.org/10.48550/arxiv.2604.05227"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05227","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05227","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05227","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123336337","display_name":"Max Hamilton","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamilton, Max","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061155671","display_name":"Daniel Sheldon","orcid":"https://orcid.org/0000-0002-4257-2432"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheldon, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052551454","display_name":"Subhransu Maji","orcid":"https://orcid.org/0000-0002-3869-9334"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maji, Subhransu","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":null,"has_fulltext":false,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.13510000705718994,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.13510000705718994,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10026","display_name":"Galaxies: Formation, Evolution, Phenomena","score":0.11680000275373459,"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/T14163","display_name":"Astronomical Observations and Instrumentation","score":0.09160000085830688,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5870000123977661},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5759999752044678},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4593999981880188},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4120999872684479},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.40230000019073486},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.38580000400543213},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.3650999963283539},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3587000072002411}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5870000123977661},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5759999752044678},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4729999899864197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4713999927043915},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4494999945163727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4327000081539154},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3447999954223633},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.28760001063346863},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05227","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05227","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05227","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05227","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":null,"license_id":null,"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":{"Two-point":[0],"correlation":[1],"functions":[2],"(2PCF)":[3],"are":[4],"widely":[5],"used":[6],"to":[7,32,74,98,128],"characterize":[8],"how":[9],"points":[10,108],"cluster":[11],"in":[12,167],"space.":[13],"In":[14],"this":[15],"work,":[16],"we":[17],"study":[18],"the":[19,23,48,105],"problem":[20],"of":[21,29,38,50,60,72,88,90,119,164],"measuring":[22],"2PCF":[24,49,89],"over":[25],"a":[26,33,36,57,64,82,95,146],"large":[27],"set":[28],"points,":[30],"restricted":[31],"subset":[34,59],"satisfying":[35],"property":[37],"interest.":[39],"An":[40],"example":[41],"comes":[42],"from":[43],"astronomy,":[44],"where":[45],"scientists":[46],"measure":[47],"star":[51],"clusters,":[52],"which":[53,77],"make":[54],"up":[55],"only":[56],"tiny":[58],"possible":[61],"sources":[62,73],"within":[63],"galaxy.":[65],"This":[66],"task":[67],"typically":[68],"requires":[69],"careful":[70],"labeling":[71],"construct":[75],"catalogs,":[76],"is":[78],"time-consuming.":[79],"We":[80,144],"present":[81],"human-in-the-loop":[83],"framework":[84],"for":[85,109],"efficient":[86],"estimation":[87],"target":[91],"sources.":[92],"By":[93],"leveraging":[94],"pre-trained":[96],"classifier":[97],"guide":[99],"sampling,":[100],"our":[101,133],"approach":[102],"adaptively":[103],"selects":[104],"most":[106],"informative":[107],"human":[110],"annotation.":[111],"After":[112],"each":[113],"annotation,":[114],"it":[115],"produces":[116],"unbiased":[117,148],"estimates":[118],"pair":[120],"counts":[121],"across":[122],"multiple":[123],"distance":[124],"bins":[125],"simultaneously.":[126],"Compared":[127],"simple":[129],"Monte":[130],"Carlo":[131],"approaches,":[132],"method":[134],"achieves":[135],"substantially":[136],"lower":[137],"variance":[138],"while":[139],"significantly":[140],"reducing":[141],"annotation":[142],"effort.":[143],"introduce":[145],"novel":[147],"estimator,":[149],"sampling":[150],"strategy,":[151],"and":[152,160],"confidence":[153],"interval":[154],"construction":[155],"that":[156],"together":[157],"enable":[158],"scalable":[159],"statistically":[161],"grounded":[162],"measurement":[163],"two-point":[165],"correlations":[166],"astronomy":[168],"datasets.":[169]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-09T00:00:00"}