Dataset of artefacts for machine learning applications in astronomy
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Sreejith, Sreevarsha Pruzhinskaya, Maria V. Volnova, Alina A. Krushinsky, Vadim V. Malanchev, Konstantin L. Ishida, Emille E. O. Lavrukhina, Anastasia D. Semenikhin, Timofey A. Gangler, Emmanuel Kornilov, Matwey V. Korolev, Vladimir S. |
| author_facet | Sreejith, Sreevarsha Pruzhinskaya, Maria V. Volnova, Alina A. Krushinsky, Vadim V. Malanchev, Konstantin L. Ishida, Emille E. O. Lavrukhina, Anastasia D. Semenikhin, Timofey A. Gangler, Emmanuel Kornilov, Matwey V. Korolev, Vladimir S. |
| contents | Accurate photometry in astronomical surveys is challenged by image artefacts, which affect measurements and degrade data quality. Due to the large amount of available data, this task is increasingly handled using machine learning algorithms, which often require a labelled training set to learn data patterns. We present an expert-labelled dataset of 1127 artefacts with 1213 labels from 26 fields in ZTF DR3, along with a complementary set of nominal objects. The artefact dataset was compiled using the active anomaly detection algorithm PineForest, developed by the SNAD team. These datasets can serve as valuable resources for real-bogus classification, catalogue cleaning, anomaly detection, and educational purposes. Both artefacts and nominal images are provided in FITS format in two sizes (28 x 28 and 63 x 63 pixels). The datasets are publicly available for further scientific applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dataset of artefacts for machine learning applications in astronomy Sreejith, Sreevarsha Pruzhinskaya, Maria V. Volnova, Alina A. Krushinsky, Vadim V. Malanchev, Konstantin L. Ishida, Emille E. O. Lavrukhina, Anastasia D. Semenikhin, Timofey A. Gangler, Emmanuel Kornilov, Matwey V. Korolev, Vladimir S. Instrumentation and Methods for Astrophysics Accurate photometry in astronomical surveys is challenged by image artefacts, which affect measurements and degrade data quality. Due to the large amount of available data, this task is increasingly handled using machine learning algorithms, which often require a labelled training set to learn data patterns. We present an expert-labelled dataset of 1127 artefacts with 1213 labels from 26 fields in ZTF DR3, along with a complementary set of nominal objects. The artefact dataset was compiled using the active anomaly detection algorithm PineForest, developed by the SNAD team. These datasets can serve as valuable resources for real-bogus classification, catalogue cleaning, anomaly detection, and educational purposes. Both artefacts and nominal images are provided in FITS format in two sizes (28 x 28 and 63 x 63 pixels). The datasets are publicly available for further scientific applications. |
| title | Dataset of artefacts for machine learning applications in astronomy |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2504.08053 |