{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/5320"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/5320","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Hierarchical Classification of Variable Stars Using Neural Networks","abstract":"Variable stars play a prominent role in our study of the universe and are essential to estimating cosmological parameters. They are considered '‘standard candles’' due to their intrinsic variability, which allows their distances to be calculated. With the proliferation of large-scale sky surveys that generate over 20 Terabytes of light-curve observations every day, automated methods are necessary to reduce manual efforts when classifying variable stars. To automate such classification, astronomers have developed various machine learning algorithms. Existing algorithms exploit star properties but fail to use the hierarchical structure known to exist in a specific family of stars. We believe embedding hierarchical information of stars into a learning algorithm can lead to more robust and efficient machine learning models. The goal of this thesis is to explore various approaches that exploit the hierarchical structure of stars within a neural network architecture. Results show the conditions under which adding information of the intrinsic hierarchical structure helps increase generalization performance.","abstract_html":"Variable stars play a prominent role in our study of the universe and are essential to estimating cosmological parameters. They are considered '‘standard candles’' due to their intrinsic variability, which allows their distances to be calculated. With the proliferation of large-scale sky surveys that generate over 20 Terabytes of light-curve observations every day, automated methods are necessary to reduce manual efforts when classifying variable stars. To automate such classification, astronomers have developed various machine learning algorithms. Existing algorithms exploit star properties but fail to use the hierarchical structure known to exist in a specific family of stars. We believe embedding hierarchical information of stars into a learning algorithm can lead to more robust and efficient machine learning models. The goal of this thesis is to explore various approaches that exploit the hierarchical structure of stars within a neural network architecture. Results show the conditions under which adding information of the intrinsic hierarchical structure helps increase generalization performance.","abstract_has_math":false,"creators":["Sirigiri, Sai Kiran Varma 1992-"],"institution":"University of Houston","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Vilalta, Ricardo"],"committee_chairs":[],"committee_members":["Mahabal, Ashish","Chen, Guoning","Toti, Giulia"],"year":2019,"date_issued":"2019-08","date_published":"2019-08","updated_at":"2026-07-24T02:32:47Z","subjects":["Variable stars","Hierarchical Classification","Neural networks","Neurosciences","Deep neural networks","Astronomy","Machine learning","Deep learning"],"languages":["eng"],"rights":["The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s)."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/5320","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Vilalta, Ricardo"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Mahabal, Ashish","Chen, Guoning","Toti, Giulia"]},{"key":"dc:creator","label":"Author","values":["Sirigiri, Sai Kiran Varma 1992-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-11-08T02:16:55Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-08"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Variable stars","Hierarchical Classification","Neural networks","Neurosciences","Deep neural networks","Astronomy","Machine learning","Deep learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s)."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/5320"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Variable stars play a prominent role in our study of the universe and are essential to estimating cosmological parameters. They are considered '‘standard candles’' due to their intrinsic variability, which allows their distances to be calculated. With the proliferation of large-scale sky surveys that generate over 20 Terabytes of light-curve observations every day, automated methods are necessary to reduce manual efforts when classifying variable stars. To automate such classification, astronomers have developed various machine learning algorithms. Existing algorithms exploit star properties but fail to use the hierarchical structure known to exist in a specific family of stars. We believe embedding hierarchical information of stars into a learning algorithm can lead to more robust and efficient machine learning models. The goal of this thesis is to explore various approaches that exploit the hierarchical structure of stars within a neural network architecture. Results show the conditions under which adding information of the intrinsic hierarchical structure helps increase generalization performance."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Hierarchical Classification of Variable Stars Using Neural Networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Vilalta, Ricardo"],"dc:contributor.committeemember":["Mahabal, Ashish","Chen, Guoning","Toti, Giulia"],"dc:creator":["Sirigiri, Sai Kiran Varma 1992-"],"dc:date.accessioned":["2019-11-08T02:16:55Z"],"dc:date.issued":["2019-08"],"dc:description.abstract":["Variable stars play a prominent role in our study of the universe and are essential to estimating cosmological parameters. They are considered '‘standard candles’' due to their intrinsic variability, which allows their distances to be calculated. With the proliferation of large-scale sky surveys that generate over 20 Terabytes of light-curve observations every day, automated methods are necessary to reduce manual efforts when classifying variable stars. To automate such classification, astronomers have developed various machine learning algorithms. Existing algorithms exploit star properties but fail to use the hierarchical structure known to exist in a specific family of stars. We believe embedding hierarchical information of stars into a learning algorithm can lead to more robust and efficient machine learning models. The goal of this thesis is to explore various approaches that exploit the hierarchical structure of stars within a neural network architecture. 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Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s)."],"dc:subject":["Variable stars","Hierarchical Classification","Neural networks","Neurosciences","Deep neural networks","Astronomy","Machine learning","Deep learning"],"dc:title":["Hierarchical Classification of Variable Stars Using Neural Networks"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:47Z"}