摘要：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 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 the u, g, r, and i bands, respectively. We classified all point-like nonmoving 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, 3485 sources were classified as variables, of which 1321 were classified as periodic. Among the periodic classes, we discovered with high confidence one δ Scuti, 39 eclipsing binaries, 48 rotational variables, and 90 RR Lyrae, and for the nonperiodic classes, we discovered one cataclysmic variable, 630 QSOs, and one supernova candidate. The first data release can be accessed in the project archive of HiTS (http://astro.cmm.uchile.cl/HiTS/).
关键词：catalogs;methods: data analysis;stars: variables: general;surveys;techniques: photometric
