تقرير
Machine Learning for the Zwicky Transient Facility
| العنوان: | Machine Learning for the Zwicky Transient Facility |
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| المؤلفون: | Ashish Mahabal, Umaa D Rebbapragada, Richard Walters, Frank J. Masci, Nadejda Blagorodnova, Jan van Roestel, Quan-Zhi Ye, Rahul Biswas, Kevin Burdge, Chan-Kao Chang, Dmitry A. Duev, V. Zach Golkhou, Adam A. Miller, Jakob Nordin, Charlotte Ward, Scott Adams, Eric C. Bellm, Doug Branton, Brian Bue, Chris Cannella, Andrew Connolly, Richard Dekany, Ulrich Feindt, Tiara Hung, Lucy Fortson, Sara Frederick, C. Fremling, Suvi Gezari, Matthew Graham, Steven L Groom, Mansi M. Kasliwal, Shrinivas Kulkarni, Thomas Kupfer, Hsing Wen Lin, Chris Lintott, Ragnhild Lunnan, John Parejko, Thomas A Prince, Reed Riddle, Ben Rusholme, Nicholas Saunders, Nima Sedaghat, David L. Shupe, Leo P. Singer, Maayane T. Soumagnac, Paula Szkody, Yutaro Tachibana, Kushal Tirumala, Sjoert van Velzen, Darryl Wright |
| المصدر: | Publications of the Astronomical Society of the Pacific. 131(997) |
| بيانات النشر: | United States: NASA Center for Aerospace Information (CASI), 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Computer Programming And Software |
| الوصف: | The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective. |
| نوع الوثيقة: | Report |
| اللغة: | English |
| تدمد: | 1538-3873 0004-6280 |
| DOI: | 10.1088/1538-3873/aaf3fa |
| URL الوصول: | https://ntrs.nasa.gov/citations/20210013637 |
| ملاحظات: | AST-1440341 80NM0018D0004P00002 J-090005 |
| رقم الأكسشن: | edsnas.20210013637 |
| قاعدة البيانات: | NASA Technical Reports |
Full Text Finder | تدمد: | 15383873 00046280 |
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| DOI: | 10.1088/1538-3873/aaf3fa |