An Automated tool to detect variable sources in the Vista Variables in the V\'{\i}a L\'actea Survey. The VVV Variables (V$^{4}$) catalog of tiles d001 and d002

TL;DR

This paper presents an automated tool for detecting and classifying variable sources in near-infrared VVV survey data, leading to the discovery of 200 variables and analysis of cluster candidates.

Contribution

The authors developed a novel automated method combining variability indices and period analysis to identify and classify variable sources in VVV survey data.

Findings

Discovered 200 variable sources in VVV tiles d001 and d002.

Identified 70 irregular and 130 periodic variables.

Analyzed nine open cluster candidates with variable star associations.

Abstract

Time-varying phenomena are one of the most substantial sources of astrophysical information and their study has led to many fundamental discoveries in modern astronomy. We have developed an automated tool to search and analyze variable sources in the near infrared $\rm K_{s}$-band, using the data from the Vista Variables in the V\'ia L\'actea (VVV) ESO Public Large Survey. This process relies on the characterization of variable sources using different variability indices, calculated from time series generated with Point Spread Function photometry of sources under analysis. In particular, we used two main indices: the total amplitude $\rm \Delta K_s$ and the eta index, $\eta$, to identify variable sources. Once variable objects are identified, periods are determined with Generalized Lomb-Scargle periodograms, and the Information Potential Metric. Variability classes are assigned according to a compromise between comparisons with VVV Templates and the period of the variability. The automated tool is applied on VVV tiles d001 and d002 and led to discovery of 200 variable sources. We detected 70 irregular variable sources and 130 periodic ones. In addition nine open cluster candidates projected in the region are analyzed, the infrared variable candidates found around these clusters are further scrutinized by cross-matching their locations against emission star candidates from VPHAS+ survey $\rm H_{\alpha}$ color cuts.