The data for "The ZTF Source Classification Project: III. A Catalog of Variable Sources"
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2020
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| author | Healy, Brian F. Coughlin, Michael W. Mahabal, Ashish A. Jegou du Laz, Theophile Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne A. van Roestel, Jan Szkody, Paula Zielske, LeighAnna Guiga, Mohammed Hassan, Muhammad Yusuf Hughes, Jill L. Nir, Guy Parikh, Saagar Park, Sungmin Purohit, Palak Rebbapragada, Umaa Reed, Draco Warshofsky, Daniel Wold, Avery Bloom, Joshua S. Masci, Frank J. Riddle, Reed Smith, Roger |
| author_facet | Healy, Brian F. Coughlin, Michael W. Mahabal, Ashish A. Jegou du Laz, Theophile Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne A. van Roestel, Jan Szkody, Paula Zielske, LeighAnna Guiga, Mohammed Hassan, Muhammad Yusuf Hughes, Jill L. Nir, Guy Parikh, Saagar Park, Sungmin Purohit, Palak Rebbapragada, Umaa Reed, Draco Warshofsky, Daniel Wold, Avery Bloom, Joshua S. Masci, Frank J. Riddle, Reed Smith, Roger |
| contents | <p>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 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 1,648,948,910 light curves across 636 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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19135641 |
| institution | Zenodo |
| language | |
| publishDate | 2020 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The data for "The ZTF Source Classification Project: III. A Catalog of Variable Sources" Healy, Brian F. Coughlin, Michael W. Mahabal, Ashish A. Jegou du Laz, Theophile Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne A. van Roestel, Jan Szkody, Paula Zielske, LeighAnna Guiga, Mohammed Hassan, Muhammad Yusuf Hughes, Jill L. Nir, Guy Parikh, Saagar Park, Sungmin Purohit, Palak Rebbapragada, Umaa Reed, Draco Warshofsky, Daniel Wold, Avery Bloom, Joshua S. Masci, Frank J. Riddle, Reed Smith, Roger <p>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 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 1,648,948,910 light curves across 636 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.</p> |
| title | The data for "The ZTF Source Classification Project: III. A Catalog of Variable Sources" |
| url | https://doi.org/10.5281/zenodo.19135641 |