The ZTF Source Classification Project. III. A Catalog of Variable Sources
The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of...
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| Language: | English |
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2024-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/ad33c6 |
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| author | Brian F. Healy Michael W. Coughlin Ashish A. Mahabal Theophile Jegou du Laz Andrew Drake Matthew J. Graham Lynne A. Hillenbrand Jan van Roestel Paula Szkody LeighAnna Zielske Mohammed Guiga Muhammad Yusuf Hassan Jill L. Hughes Guy Nir Saagar Parikh Sungmin Park Palak Purohit Umaa Rebbapragada Draco Reed Daniel Warshofsky Avery Wold Joshua S. Bloom Frank J. Masci Reed Riddle Roger Smith |
| author_facet | Brian F. Healy Michael W. Coughlin Ashish A. Mahabal Theophile Jegou du Laz Andrew Drake Matthew J. Graham Lynne A. Hillenbrand Jan van Roestel Paula Szkody LeighAnna Zielske Mohammed Guiga Muhammad Yusuf Hassan Jill L. Hughes Guy Nir Saagar Parikh Sungmin Park Palak Purohit Umaa Rebbapragada Draco Reed Daniel Warshofsky Avery Wold Joshua S. Bloom Frank J. Masci Reed Riddle Roger Smith |
| author_sort | Brian F. Healy |
| collection | DOAJ |
| description | The classification of variable objects provides insight into a wide variety of
astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky
Transient Facility (ZTF) provides time-series observations that record the
variability of more than a billion sources. The scale of these data necessitates
automated approaches to make a thorough analysis. Building on previous work,
this paper reports the results of the ZTF Source Classification Project
( SCoPe ), which trains neural network and XGBoost
(XGB) machine-learning (ML) algorithms to perform dichotomous classification of
variable ZTF sources using a manually constructed training set containing
170,632 light curves. We find that several classifiers achieve high precision
and recall scores, suggesting the reliability of their predictions for
209,991,147 light curves across 77 ZTF fields. We also identify the most
important features for XGB classification and compare the performance of the two
ML algorithms, finding a pattern of higher precision among XGB classifiers. The
resulting classification catalog is available to the public, and the software
developed for SCoPe is open source and adaptable to
future time-domain surveys. |
| format | Article |
| id | doaj-art-85b51fc73bd94cdcac9a4847abf37697 |
| institution | OA Journals |
| issn | 0067-0049 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-85b51fc73bd94cdcac9a4847abf376972025-08-20T01:55:12ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492024-01-0127211410.3847/1538-4365/ad33c6The ZTF Source Classification Project. III. A Catalog of Variable SourcesBrian F. Healy0https://orcid.org/0000-0002-7718-7884Michael W. Coughlin1https://orcid.org/0000-0002-8262-2924Ashish A. Mahabal2https://orcid.org/0000-0003-2242-0244Theophile Jegou du Laz3https://orcid.org/0009-0003-6181-4526Andrew Drake4https://orcid.org/0000-0003-0228-6594Matthew J. Graham5https://orcid.org/0000-0002-3168-0139Lynne A. Hillenbrand6Jan van Roestel7https://orcid.org/0000-0002-2626-2872Paula Szkody8https://orcid.org/0000-0003-4373-7777LeighAnna Zielske9Mohammed Guiga10Muhammad Yusuf Hassan11Jill L. Hughes12Guy Nir13https://orcid.org/0000-0002-7501-5579Saagar Parikh14Sungmin Park15Palak Purohit16Umaa Rebbapragada17https://orcid.org/0000-0002-2560-3495Draco Reed18Daniel Warshofsky19Avery Wold20https://orcid.org/0000-0002-9998-6732Joshua S. Bloom21https://orcid.org/0000-0002-7777-216XFrank J. Masci22https://orcid.org/0000-0002-8532-9395Reed Riddle23https://orcid.org/0000-0002-0387-370XRoger Smith24https://orcid.org/0000-0001-7062-9726School of Physics and Astronomy, University of Minnesota , Minneapolis, MN 55455, USASchool of Physics and Astronomy, University of Minnesota , Minneapolis, MN 55455, USADivision of Physics , Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA; Center for Data Driven Discovery , California Institute of Technology, Pasadena, CA 91125, USADivision of Physics , Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USADivision of Physics , Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USADivision of Physics , Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USADepartment of Astronomy, California Institute of Technology , Pasadena, CA 91125, USAAnton Pannekoek Institute for Astronomy, University of Amsterdam , 1090 GE, Amsterdam, The NetherlandsDepartment of Astronomy, University of Washington , Seattle, WA 98195, USASchool of Physics and Astronomy, University of Minnesota , Minneapolis, MN 55455, USA; Department of Physics, Pacific Lutheran University , Tacoma, WA 98447, USADepartment of Computer Science and Engineering, University of Minnesota , Minneapolis, MN 55455, USADivision of Physics , Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA; Department of Electrical Engineering, Indian Institute of Technology Gandhinagar , Gujarat 382055, IndiaSchool of Physics and Astronomy, University of Minnesota , Minneapolis, MN 55455, USADepartment of Astronomy, University of California , Berkeley, CA 94720, USA; Lawrence Berkeley National Laboratory , 1 Cyclotron Road, MS 50B-4206, Berkeley, CA 94720, USADepartment of Electrical and Computer Engineering, Carnegie Mellon University , Pittsburgh, PA 15213, USA; Computer Vision, Imaging and Graphics Lab, Indian Institute of Technology Gandhinagar , Gujarat 382055, IndiaSchool of Statistics, University of Minnesota , Minneapolis, MN 55455, USADepartment of Electrical Engineering, Indian Institute of Technology Gandhinagar , Gujarat 382055, India; Division of Engineering and Applied Science , California Institute of Technology, Pasadena, CA 91125, USAJet Propulsion Laboratory, California Institute of Technology , Pasadena, CA 91125, USASchool of Physics and Astronomy, University of Minnesota , Minneapolis, MN 55455, USASchool of Physics and Astronomy, University of Minnesota , Minneapolis, MN 55455, USAIPAC, California Institute of Technology , 1200 E. California Blvd., Pasadena, CA 91125, USADepartment of Astronomy, University of California , Berkeley, CA 94720, USA; Lawrence Berkeley National Laboratory , 1 Cyclotron Road, MS 50B-4206, Berkeley, CA 94720, USAIPAC, California Institute of Technology , 1200 E. California Blvd., Pasadena, CA 91125, USACaltech Optical Observatories , California Institute of Technology, Pasadena, CA 91125, USACaltech Optical Observatories , California Institute of Technology, Pasadena, CA 91125, USAThe classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project ( SCoPe ), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open source and adaptable to future time-domain surveys.https://doi.org/10.3847/1538-4365/ad33c6SurveysVariable starsCatalogsClassificationNeural networksAlgorithms |
| spellingShingle | Brian F. Healy Michael W. Coughlin Ashish A. Mahabal Theophile Jegou du Laz Andrew Drake Matthew J. Graham Lynne A. Hillenbrand Jan van Roestel Paula Szkody LeighAnna Zielske Mohammed Guiga Muhammad Yusuf Hassan Jill L. Hughes Guy Nir Saagar Parikh Sungmin Park Palak Purohit Umaa Rebbapragada Draco Reed Daniel Warshofsky Avery Wold Joshua S. Bloom Frank J. Masci Reed Riddle Roger Smith The ZTF Source Classification Project. III. A Catalog of Variable Sources The Astrophysical Journal Supplement Series Surveys Variable stars Catalogs Classification Neural networks Algorithms |
| title | The ZTF Source Classification Project. III. A Catalog of Variable Sources |
| title_full | The ZTF Source Classification Project. III. A Catalog of Variable Sources |
| title_fullStr | The ZTF Source Classification Project. III. A Catalog of Variable Sources |
| title_full_unstemmed | The ZTF Source Classification Project. III. A Catalog of Variable Sources |
| title_short | The ZTF Source Classification Project. III. A Catalog of Variable Sources |
| title_sort | ztf source classification project iii a catalog of variable sources |
| topic | Surveys Variable stars Catalogs Classification Neural networks Algorithms |
| url | https://doi.org/10.3847/1538-4365/ad33c6 |
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