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

TL;DR

The High Cadence Transient Survey (HiTS) systematically catalogs and classifies millions of transient objects using advanced data science techniques, achieving high accuracy in identifying variable and periodic sources from large-scale, high-cadence observations.

Contribution

This work introduces a comprehensive data science pipeline for large-scale transient classification, including a hierarchical Random Forest model with high accuracy, applied to the first HiTS data release.

Findings

Cataloged ~15 million object detections and ~2.5 million classified light-curves.

Achieved ~97% accuracy in classifying variable sources.

Discovered various periodic and non-periodic transient objects, including eclipsing binaries, RR-Lyrae, QSOs, and supernova candidates.

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, $3\,485$ 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.