{"id":"https://openalex.org/W7134036058","doi":"https://doi.org/10.48550/arxiv.2603.04516","title":"Augmenting representations with scientific papers","display_name":"Augmenting representations with scientific papers","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7134036058","doi":"https://doi.org/10.48550/arxiv.2603.04516"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.04516","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039313308","display_name":"Nicol\u00f2 Oreste Pinciroli Vago","orcid":"https://orcid.org/0000-0001-7906-4987"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vago, Nicol\u00f2 Oreste Pinciroli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128272877","display_name":"Rocco Di Tella","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Tella, Rocco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Cuesta-L\u00e1zaro, Carolina","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cuesta-L\u00e1zaro, Carolina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128239341","display_name":"Michael J. Smith","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smith, Michael J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020090078","display_name":"Cecilia Garraffo","orcid":"https://orcid.org/0000-0002-8791-6286"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garraffo, Cecilia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128275135","display_name":"Rafael Mart\u00ednez-Galarza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mart\u00ednez-Galarza, Rafael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06030000001192093,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06030000001192093,"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/T10028","display_name":"Topic Modeling","score":0.0551999993622303,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.050700001418590546,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6046000123023987},{"id":"https://openalex.org/keywords/scientific-reasoning","display_name":"Scientific reasoning","score":0.47780001163482666},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.4456000030040741},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43290001153945923},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.41830000281333923},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4163999855518341}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6513000130653381},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6046000123023987},{"id":"https://openalex.org/C2992562121","wikidata":"https://www.wikidata.org/wiki/Q3817808","display_name":"Scientific reasoning","level":2,"score":0.47780001163482666},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.4456000030040741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4431000053882599},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43290001153945923},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.41830000281333923},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.39169999957084656},{"id":"https://openalex.org/C2781083858","wikidata":"https://www.wikidata.org/wiki/Q17327049","display_name":"Scientific literature","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3140000104904175},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.29980000853538513},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.2637999951839447}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.04516","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.04516","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04516","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.04516","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6566371917724609}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Astronomers":[0],"have":[1],"acquired":[2],"vast":[3],"repositories":[4],"of":[5,17,53,105,109,136,152],"multimodal":[6,55,171],"data,":[7,131],"including":[8,180],"images,":[9],"spectra,":[10,90],"and":[11,69,129,160,186],"time":[12],"series,":[13],"complemented":[14],"by":[15,140],"decades":[16],"literature":[18,207],"that":[19,81,92,149],"analyzes":[20],"astrophysical":[21],"sources.":[22,114],"Still,":[23],"these":[24,97],"data":[25,204],"sources":[26],"are":[27],"rarely":[28],"systematically":[29],"integrated.":[30],"This":[31],"work":[32],"introduces":[33],"a":[34,67,78,83,93,150,181,187],"contrastive":[35,79],"learning":[36],"framework":[37,193],"designed":[38],"to":[39,197],"align":[40],"X-ray":[41],"spectra":[42],"with":[43,205],"domain":[44],"knowledge":[45],"extracted":[46],"from":[47,89],"scientific":[48,64,199],"literature,":[49],"facilitating":[50],"the":[51,107,116,134,170],"development":[52],"shared":[54,118,161],"representations.":[56],"Establishing":[57],"this":[58,192],"connection":[59],"is":[60,99,208],"inherently":[61],"complex,":[62],"as":[63],"texts":[65,88],"encompass":[66],"broader":[68],"more":[70],"diverse":[71],"physical":[72,138],"context":[73],"than":[74],"spectra.":[75],"We":[76],"propose":[77],"pipeline":[80],"achieves":[82],"20%":[84],"Recall@1%":[85],"when":[86],"retrieving":[87],"proving":[91],"meaningful":[94],"alignment":[95],"between":[96],"modalities":[98],"not":[100],"only":[101],"possible":[102],"but":[103],"capable":[104],"accelerating":[106],"interpretation":[108],"rare":[110],"or":[111],"poorly":[112],"understood":[113],"Furthermore,":[115],"resulting":[117],"latent":[119,172],"space":[120,173],"effectively":[121],"encodes":[122],"physically":[123],"significant":[124],"information.":[125],"By":[126],"fusing":[127],"spectral":[128,144],"textual":[130],"we":[132],"improve":[133],"estimation":[135],"20":[137],"variables":[139],"16-18%":[141],"over":[142],"unimodal":[143,159],"baselines.":[145],"Our":[146],"results":[147],"indicate":[148],"Mixture":[151],"Experts":[153],"(MoE)":[154],"strategy,":[155],"which":[156],"leverages":[157],"both":[158],"representations,":[162],"yields":[163],"superior":[164],"performance.":[165],"Finally,":[166],"outlier":[167],"analysis":[168],"within":[169],"identifies":[174],"high-priority":[175],"targets":[176],"for":[177],"follow-up":[178],"investigation,":[179],"candidate":[182],"pulsating":[183],"ULX":[184],"(PULX)":[185],"gravitational":[188],"lens":[189],"system.":[190],"Importantly,":[191],"can":[194],"be":[195],"extended":[196],"other":[198],"domains":[200],"where":[201],"aligning":[202],"observational":[203],"existing":[206],"possible.":[209]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-07T00:00:00"}