[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128782-en":3,"doc-seo-128782-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128782,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Unsupervised Classification of Binary SMBH Candidates in Gaia DR3 - A Machine Learning Approach to Astrometric Jitter and Cluster-Based Candidate Identification","Presents an unsupervised machine learning analysis of astrometric variability in Gaia DR3 quasars to identify indirect signatures of unresolved binary supermassive black holes. A filtered sample of ~10,000 high-quality quasars is represented using RUWE, astrometric excess noise, parallax, color index, and G-band magnitude. Features are normalized and reduced with PCA and t-SNE, then clustered via K-Means and DBSCAN. One K-Means population shows higher excess noise and intermediate RUWE, consistent with centroid jitter from orbital motion. Candidates are compiled from the most deviant cluster and prioritized for VLBI, variability surveys, and future Gaia follow-up.","Unsupervised Classification of Binary SMBH Candidates in Gaia DR3: A Machine Learning Approach to Astrometric Jitter and Cluster-Based Candidate Identification  \nAnmay Raj*  \nIndependent Researcher, Bihar, India.  \nAbstract: We present an unsupervised machine learning analysis of astrometric variability in Gaia DR3 quasars, aimed at identifying indirect signatures of unresolved binary supermassive black holes (SMBHBs) . Using a filtered sample of ∼10,000 high-quality quasars, we extract key features including RUWE, astrometric excess noise, parallax, color index, and G-band magnitude. These features are normalized and reduced using Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to uncover low-dimensional structure. We apply both K-Means and DBSCAN clustering algorithms to the projected feature space. The K-Means algorithm identifies three distinct populations, with one cluster exhibiting statistically higher excess noise and intermediate RUWE values, suggestive of potential centroid jitter induced by binary SMBH orbital motion. The clustering results are further validated using silhouette scores and consistent spatial separability in t-SNE projections. A catalog of candidate high-jitter quasars is compiled from the most deviant cluster, comprising over 3500 sources. These candidates are promising targets for future multi-wavelength follow-up using VLBI, variability surveys, and higher-precision Gaia astrometry. Our work demonstrates that unsupervised learning techniques offera powerful, scalable alternative to classical threshold-based methods for probing the hidden binary SMBH population at cosmological distances. This study represents one of the first applications of machine learning to stochastic astrometric variability in extragalactic sources and provides a reproducible framework for future discovery in Gaia DR4 and LSST-era datasets.  \nTable of Contents  \n1. Introduction............................................................................................................................................ 1  \n2. Methodology ........................................................................................................................................... 2  \n3. Feature Engineering ................................................................................................................................ 3  \n4. Clustering Methods.................................................................................................................................. 4  \n5. Results and Interpretation........................................................................................................................ 6  \n6. Candidate Catalog Creation ...................................................................................................................... 8  \n7. Discussion .............................................................................................................................................. 9  \n8. Conclusion ............................................................................................................................................ 10  \n9. Data Availability .................................................................................................................................... 11  \n10. Acknowledgement .......................................................................................................................... 11  \n11. References ..................................................................................................................................... 11  \n12. Conflict of Interest .......................................................................................................................... 11  \n13. Funding ......................................................................................................................................... 11  \n14. Appendix .....................","cbCainLL5tSvdcWT","https://ap.wps.com/l/cbCainLL5tSvdcWT","pdf",730053,1,12,"English","en",105,"# Introduction\n## Motivation\n## Astrometric Jitter as a Binary SMBH Signature\n## Limitations of Traditional Methods\n# Methodology\n## Feature Engineering\n## Clustering Methods\n# Results and Interpretation\n# Candidate Catalog Creation\n# Discussion\n# Conclusion\n# Data Availability\n# Acknowledgement\n# References\n# Conflict of Interest\n# Funding\n# Appendix","[{\"question\":\"What indirect signature does the study use to search for unresolved binary SMBHs?\",\"answer\":\"It uses astrometric variability, specifically stochastic astrometric “jitter” in Gaia DR3 quasar positions, which can reflect unresolved orbital motion.\"},{\"question\":\"Which clustering and dimensionality reduction methods are applied?\",\"answer\":\"The approach reduces feature dimensionality using PCA and t-SNE, then applies K-Means and DBSCAN clustering to the projected feature space.\"},{\"question\":\"How are candidate high-jitter quasars selected and validated?\",\"answer\":\"Candidates are taken from the most deviant K-Means cluster showing elevated excess noise and intermediate RUWE; results are supported by silhouette scores and consistent separability in t-SNE projections.\"}]","Unsupervised Classification of Binary SMBH Candidates in Gaia DR3 - A Machine Learning Approach to Astrometric Jitter and Cluster-Based Candidate Identification | PDF",1786003373,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"unsupervised-classification-of-binary-smbh-candidates-in-gaia-dr3-a-machine-learning-approach-to-astrometric-jitter-and-cluster-based-candidate-identification","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/unsupervised-classification-of-binary-smbh-candidates-in-gaia-dr3-a-machine-learning-approach-to-astrometric-jitter-and-cluster-based-candidate-identification/128782/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What indirect signature does the study use to search for unresolved binary SMBHs?","Question",{"text":76,"@type":77},"It uses astrometric variability, specifically stochastic astrometric “jitter” in Gaia DR3 quasar positions, which can reflect unresolved orbital motion.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which clustering and dimensionality reduction methods are applied?",{"text":81,"@type":77},"The approach reduces feature dimensionality using PCA and t-SNE, then applies K-Means and DBSCAN clustering to the projected feature space.",{"name":83,"@type":74,"acceptedAnswer":84},"How are candidate high-jitter quasars selected and validated?",{"text":85,"@type":77},"Candidates are taken from the most deviant K-Means cluster showing elevated excess noise and intermediate RUWE; results are supported by silhouette scores and consistent separability in t-SNE projections.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]