Replicar los datos para: The high cadence transient survey (Hits): Source, light-curve and classification catalogs
The High Cadence Transient Survey (HiTS) aims to discover and study transient objects with characteristic timescales between hours and days, such as pulsating, eclipsing and exploding stars. This survey represents a unique laboratory to explore large etendue observations from cadences of about 0.1 days and to test new computational tools for the analysis of large data. This work follows a fully Data Science approach: from the raw data to the analysis and classification of variable sources. We compile a catalog of ~15 million object detections and a catalog of ~2.5 million light-curves classified by variability. The typical depth of the survey is 24.2, 24.3, 24.1 and 23.8 in u, g, r, and i bands, respectively. We classified all point-like non-moving sources by first extracting features from their light--curves and then applying a Random Forest classifier. For the classification, we used a training set constructed using a combination of cross-matched catalogs, visual inspection, transfer/active learning, and data augmentation. The classification model consists of several Random Forest classifiers organized in a hierarchical scheme. The classifier accuracy estimated on a test set is approximately 97%. In the unlabeled data, 3,485 sources were classified as variables, of which 1,321 were classified as periodic. Among the periodic classes we discovered with high confidence, 1 δ scuti, 39 eclipsing binaries, 48 rotational variables, and 90 RR-Lyrae. For the non-periodic classes we discovered 1 cataclysmic variables, 630 QSO, and 1 supernova candidate.
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Repositorio de datos de investigación de la Universidad de Chile
2019
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| Subjects: | Astronomy and Astrophysics, Light-curve catalog, Variable stars, |
| Online Access: | https://doi.org/10.34691/FK2/9SGXZG |
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| Summary: | The High Cadence Transient Survey (HiTS) aims to discover and study transient objects with characteristic timescales between hours and days, such as pulsating, eclipsing and exploding stars. This survey represents a unique laboratory to explore large etendue observations from cadences of about 0.1 days and to test new computational tools for the analysis of large data. This work follows a fully Data Science approach: from the raw data to the analysis and classification of variable sources. We compile a catalog of ~15 million object detections and a catalog of ~2.5 million light-curves classified by variability. The typical depth of the survey is 24.2, 24.3, 24.1 and 23.8 in u, g, r, and i bands, respectively. We classified all point-like non-moving sources by first extracting features from their light--curves and then applying a Random Forest classifier. For the classification, we used a training set constructed using a combination of cross-matched catalogs, visual inspection, transfer/active learning, and data augmentation. The classification model consists of several Random Forest classifiers organized in a hierarchical scheme. The classifier accuracy estimated on a test set is approximately 97%. In the unlabeled data, 3,485 sources were classified as variables, of which 1,321 were classified as periodic. Among the periodic classes we discovered with high confidence, 1 δ scuti, 39 eclipsing binaries, 48 rotational variables, and 90 RR-Lyrae. For the non-periodic classes we discovered 1 cataclysmic variables, 630 QSO, and 1 supernova candidate. |
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