The High Cadence Transient Survey (HITS): Compilation and characterization of light-curve catalogs
| Autor: | Juan-Carlos Maureira, Pablo Huijse, Mario Hamuy, Thomas de Jaeger, F. Forster, G. Cabrera-Vives, Lluís Galbany, Pavlos Protopapas, Jaime San Martín, Paulina Lira, Giuliano Pignata, G. E. Medina, Santiago González-Gaitán, Jorge Martínez-Palomera, Ricardo R. Muñoz |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Physics
010504 meteorology & atmospheric sciences photometric [Techniques] FOS: Physical sciences Astronomy and Astrophysics Surveys Light curve 01 natural sciences variables: general [Stars] Characterization (materials science) Space and Planetary Science 0103 physical sciences Transit (astronomy) Catalogs data analysis [Methods] Cadence Astrophysics - Instrumentation and Methods for Astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) 010303 astronomy & astrophysics 0105 earth and related environmental sciences Remote sensing |
| Popis: | 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 \textit{Data Science} approach: from the raw data to the analysis and classification of variable sources. We compile a catalog of ${\sim}15$ million object detections and a catalog of ${\sim}2.5$ 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 $\delta$-scutti, 39 eclipsing binaries, 48 rotational variables and 90 RR-Lyrae and for the non-periodic classes we discovered 1 cataclysmic variables, 630 QSO, and 1 supernova candidates. The first data release can be accessed in the project archive of HiTS. Comment: 22 pages including 10 figures and 9 tables. Accepted for publication in AJ. For associated files, see http://astro.cmm.uchile.cl/HiTS/ |
| Databáze: | OpenAIRE |
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