A machine learned classifier for RR Lyrae in the VVV survey

Abstract

Variable stars of RR Lyrae type are a prime tool to obtain distances to old stellar populations in the Milky Way, and one of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the Galactic Bulge. Due to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae,and particularly those of the more easily recognized type ab (i.e., fundamental-mode pulsators), from the 10^6-10^7 variables expected in the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a K_s-band VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier, which were the choice of features, training set, selection of aperture and family of classifiers. We find that the AdaBoost family of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaBoost algorithm that achieves a harmonic mean between false positives and false negatives of ~7% for typical VVV light curve sets. This performance is estimated using cross-validation and through the comparison to two independent datasets that were classified by human experts.

arXiv comment11 pages, 7 figures. Astronomy & Astrophysics, in press

References in corpus (20)

• The generalised Lomb-Scargle periodogram. A new formalism for the floating-mean and Keplerian periodograms
• VISTA Variables in the Via Lactea (VVV): The public ESO near-IR variability survey of the Milky Way
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Automated supervised classification of variable stars I. Methodology
• Reddening and metallicity maps of the Milky Way bulge from VVV and 2MASS II. The complete high resolution extinction map and implications for Bulge studies
• Random forest automated supervised classification of Hipparcos periodic variable stars
• K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4
• A comparison of period finding algorithms
• Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey
• A Package for the Automated Classification of Periodic Variable Stars
• Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification
• Variable stars in the VVV globular clusters. I. 2MASS-GC02 and Terzan10
• The automated classification of astronomical lightcurves using Kohonen self-organising maps
• Mapping the outer bulge with RRab stars from the VVV Survey
• The VVV Templates Project. Towards an Automated Classification of VVV Light-Curves. I. Building a database of stellar variability in the near-infrared
• Bulge RR Lyrae stars in the VVV tile $\text{b201}$
• Discovery of a Pair of Classical Cepheids in an Invisible Cluster Beyond the Galactic Bulge
• Stellar Variability in the VVV survey
• The EB Factory Project I. A Fast, Neural Net Based, General Purpose Light Curve Classifier Optimized for Eclipsing Binaries
• Stellar Variability in the VVV Survey: An Update

Cited by in corpus (26)

• Machine-Learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: the PS1 Sample
• Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
• Separation of pulsar signals from noise with supervised machine learning algorithms
• On the use of field RR Lyrae as Galactic probes: I. The Oosterhoff dichotomy based on fundamental variables
• Machine learning search for variable stars
• Scalable End-to-end Recurrent Neural Network for Variable star classification
• A comprehensive statistical study on gamma-ray bursts
• The VVV Survey RR Lyrae Population in the Galactic Centre Region
• A Data-driven Study of RR Lyrae Near-IR Light Curves: Principal Component Analysis, Robust Fits, and Metallicity Estimates
• A Near-infrared RR Lyrae census along the southern Galactic plane: the Milky Way's stellar fossil brought to light
• Unsupervised Classification of Variable Stars
• Into the Darkness: Classical and Type II Cepheids in the Zona Galactica Incognita
• Kinematics of RR Lyrae stars in the Galactic bulge with OGLE-IV and Gaia DR2
• Establishing the Galactic Centre Distance Using VVV Bulge RR Lyrae Variables
• An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves
• Discrete-time autoregressive model for unequally spaced time-series observations
• An Automated tool to detect variable sources in the Vista Variables in the V\'ıa Láctea Survey. The VVV Variables (V${}^4$) catalog of tiles d001 and d002
• Near-Infrared Search for Fundamental-mode RR Lyrae Stars Toward the Inner Bulge by Deep Learning
• Novel bivariate autoregressive model for predicting and forecasting irregularly observed time series
• Deep Transfer Learning for Classification of Variable Sources
• Semi-Supervised Classification and Clustering Analysis for Variable Stars
• VVV catalog of ab-type RR Lyrae in the inner Galactic bulge
• Automatic Catalog of RRLyrae from $\sim$ 14 million VVV Light Curves: How far can we go with traditional machine-learning?
• Informative Bayesian model selection for RR Lyrae star classifiers
• Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift
• Pulsating stars in $\omega$ Centauri: Near-IR properties and period-luminosity relations