The High Cadence Transient Survey (HITS): Compilation and characterization of light-curve catalogs

Abstract

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, $3485$ sources were classified as variables, of which $1321$ 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.

arXiv comment22 pages including 10 figures and 9 tables. Accepted for publication in AJ. For associated files, see http://astro.cmm.uchile.cl/HiTS/

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• Deep Learning for Image Sequence Classification of Astronomical Events
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• Introducing the Search for Intermediate-mass Black-hole In Nearby Galaxy (SIBLING) Survey
• A Low Incidence of Mid-Infrared Variability in Dwarf Galaxies
• Red Supergiant Candidates for Multimessenger Monitoring of the Next Galactic Supernova
• deepSIP: Linking Type Ia Supernova Spectra to Photometric Quantities with Deep Learning
• Asteroids' Size Distribution and Colors from HiTS
• The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves
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• An Information Theory Approach on Deciding Spectroscopic Follow Ups
• Uncertainty estimation for time series classification: Exploring predictive uncertainty in transformer-based models for variable stars