New Insights into Time Series Analysis - I - Correlated observations

Abstract

The first step when investigating time varying data is the detection of any reliable changes in star brightness. This step is crucial to decreasing the processing time by reducing the number of sources processed in later, slower steps. Variability indices and their combinations have been used to identify variability patterns and to select non-stochastic variations, but the separation of true variables is hindered because of wavelength-correlated systematics of instrumental and atmospheric origin, or due to possible data reduction anomalies. The main aim is to review the current inventory of correlation variability indices and measure the efficiency for selecting non-stochastic variations in photometric data. The WFCAM Science Archive (WSA) were used to test the different indices. We improve the panchromatic variability indices and introduce a new set of variability indices for preselecting variable star candidates. Using the WFCAMCAL Variable Star Catalogue (WVSC1) we delimit the efficiency of each variability index. Moreover we test new insights about these indices to improve the efficiency of detection of time-series data dominated by correlated variations. We propose five new variability indices which display a high efficiency for the detection of variable stars. We determine the best way to select variable stars using these and the current tool inventory. In addition, we propose an universal analytical expression to select likely variables using the fraction-of-fluctuations on these indices (f_fluc). The f_fluc can be used as an universal way to analyse photometric data since it displays a only weak dependency with the instrument properties. The variability indices computed in this new approach allow us to reduce misclassification and these will be implemented in an automatic classifier which will be addressed in a forthcoming paper in this series.

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Cited by in corpus (18)

• Comparative performance of selected variability detection techniques in photometric time series
• Machine learning search for variable stars
• Robust Identification of Active Galactic Nuclei through HST Optical Variability in GOODS-S: Comparison with the X-ray and mid-IR Selected Samples
• New Insights into Time Series Analysis II -- No Correlated Observations
• New type II Cepheids from VVV data towards the Galactic center
• The VVV Infrared Variability Catalog (VIVA-I)
• The VVV survey: Long-period variable stars I. Photometric catalog of ten VVV/OGLE tiles
• The VMC Survey - XX. Identification of new Cepheids in the Small Magellanic Cloud
• Long-term Stellar Variability in the Galactic Centre Region
• The Hubble Catalog of Variables (HCV)
• New Insights into Time Series Analysis III - Setting constraints on period analysis
• VVV survey near-infrared colour catalogue of known variable stars
• Automated classification of eclipsing binary systems in the VVV Survey
• Unveiling short period binaries in the inner VVV bulge
• Variability search in M 31 using Principal Component Analysis and the Hubble Source Catalog
• Recovering variable stars in large surveys: EA${}_{\mathrm{up}}$ Algol-type class in the Catalina Survey
• New Insights into Time Series Analysis IV: Panchromatic and Flux Independent Period Finding Methods
• Recovering Signals in CoRoT Mission (RSCoRoT): I. Short Period Variable Stars