[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125794-en":3,"doc-seo-125794-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},125794,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Using Dark Energy Explorers and Machine Learning to Enhance the Hobby-Eberly Telescope Dark Energy Experiment","Analysis using a citizen science campaign improves cosmological measurements from the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). The work targets percent-level estimates of the Hubble expansion rate H(z) and the angular diameter distance DA(z) at z=2.4, constrained by Lyα emitter counts, noise-driven false positives, and contamination from [O ii] emitters. The paper presents Dark Energy Explorers, combining human visual classification with unsupervised machine learning to increase LAEs and reduce errors, and reports large-scale volunteer activity supporting improved DA(z) and H(z).","Copyright by  \nLindsay R. House 2023  \nThe Thesis Committee for Lindsay R. House Certifies that this is the approved version of the following Thesis:  \nUsing Dark Energy Explorers and Machine Learning to Enhance the Hobby–Eberly Telescope Dark Energy Experiment  \nAPPROVED BY  \nSUPERVISING COMMITTEE:  \nKarl Gebhardt, Supervisor  \nKeely Finkelstein, Co-Supervisor  \nUsing Dark Energy Explorers and Machine Learning to Enhance the Hobby–Eberly Telescope Dark Energy Experiment  \nby  \nLindsay R. House  \nThesis  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nMaster of Arts  \nThe University of Texas at Austin  \nAugust 2023  \nAcknowledgements  \nThe results of the Dark Energy Explorers would not be as robust and useful if not for the care and dedication of the volunteers. We are extremely grateful to their work. It is having a large impact and is motivational.  \nL.H. acknowledges support from NSF GRFP DGE 2137420 and NASA 21-CSSFP21-0009. K.G. acknowledges support from the NSF-2008793 and from NASA 21-CSSFP21-0009.  \nDark Energy Explorers is recognized as an official NASA Citizen Science partner. This publication uses data generated via the [www.zooniverse.org](www.zooniverse.org) platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation.  \nHETDEX is led by the University of Texas at Austin McDonald Observatory and Department of Astronomy with participation from the Ludwig-Maximilians-Universität München, Max-Planck-Institut für Extraterrestrische Physik (MPE), Leibniz-Institut für Astrophysik Potsdam (AIP), Texas A&M University, The Pennsylvania State University, Institut für Astrophysik Göttingen, The University of Oxford, Max-Planck-Institut für Astrophysik (MPA), The University of Tokyo, and Missouri University of Science and Technology. In addition to Institutional support, HETDEX is funded by the National Science Foundation (grant AST-0926815), the State of Texas, the US Air Force (AFRL FA9451-04-2-0355), and generous support from private individuals and foundations.  \nThe Hobby–Eberly Telescope (HET) is a joint project of the University of Texas at Austin, the Pennsylvania State University, Ludwig-Maximilians-Universität München, and Georg-August-Universität Göttingen. The HET is named in honor of its principal  \nbenefactors, William P. Hobby and Robert E. Eberly. The Institute for Gravitation and the Cosmos is supported by the Eberly College of Science and the Office of the Senior Vice President for Research at the Pennsylvania State University.  \nThe authors acknowledge the Texas Advanced Computing Center (TACC) 13 at The University of Texas at Austin for providing high performance computing, visualization, and storage resources that have contributed to the research results reported within this paper.  \nAbstract  \nUsing Dark Energy Explorers and Machine Learning to Enhance the Hobby–Eberly Telescope Dark Energy Experiment  \nLindsay R. House, M.A.  \nThe University of Texas at Austin, 2023  \nSupervisor: Karl Gebhardt, Keely Finkelstein  \nWe present analysis using a citizen science campaign to improve the cosmological measures from the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) . The goal of HETDEX is to measure the Hubble expansion rate, H(z), and angular diameter distance, DA(z), at z = 2.4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyα emitters (LAEs), the false positive rate due to noise, and the contamination due to [O ii] emitting galaxies. This paper presents the citizen science project, Dark Energy Explorers ([https://www.zooniverse.org/projects/erinmc/dark-energy-explorers](https://www.zooniverse.org/projects/erinmc/dark-energy-explorers)), with the goal of increasing the number of LAEs and decreasing the number of false positives due to noise an","cbCaiquNYAp8fjWD","https://ap.wps.com/l/cbCaiquNYAp8fjWD","pdf",715469,1,37,"English","en",105,"# List of Tables\n# List of Figures\n# Chapter 1 : Introduction\n# Chapter 2: Improving the Accuracy of the Distance Measures from HETDEX\n# Chapter 3: Developing Dark Energy Explorers\n## 3.1 Tutorial and Field Guide\n## 3.2 Interface and Data Inputs\n## 3.3 Education and Engagement","[{\"question\":\"What is the main scientific goal of HETDEX described in the document?\",\"answer\":\"HETDEX aims to measure the Hubble expansion rate H(z) and the angular diameter distance DA(z) at z=2.4 with percent-level accuracy.\"},{\"question\":\"How does Dark Energy Explorers help improve the HETDEX measurements?\",\"answer\":\"It increases the number of Lyα emitters and decreases false positives from noise and contamination from [O ii] galaxies by using citizen science classifications.\"},{\"question\":\"Why does the document emphasize combining human eye classification with machine learning?\",\"answer\":\"Human visual classification is described as the most accurate approach, and it is used in combination with unsupervised machine learning to support labeling and improved analysis.\"}]","Using Dark Energy Explorers and Machine Learning to Enhance the Hobby-Eberly Telescope Dark Energy Experiment | 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