New Insights into Time Series Analysis II -- No Correlated Observations

Abstract

Statistical parameters are used in finance, weather, industrial, science, among other vast number of different fields to draw conclusions. New more efficient selection methods are mandatory to analyses the huge amount of astronomical data. The standard and new data-mining parameters to analyses non-correlated data are used to set the best way to discriminate stochastic and non-stochastic variations. We introduce 16 modified statistical parameters covering different features of statistical distribution, like; average, dispersion, and shape parameters. Many of dispersion and shape parameters are unbound parameters, i.e. equations which do not require the calculation of the average. Moreover, the majority of them have lower error than previous ones that is mainly observed for distributions having few measurements. A set of non-correlated variability indices, sample size corrections, and a new noise model as well as tests of different apertures and cutoffs on the data (BAS approach) are introduced. The number of misselections is reduced by about 520% using a single waveband and 1200% combining all wavebands. On the other hand, the even mean also improves the correlated indices introduced in Paper 1 Ferreira Lopes & Cross (2016). The misselection rate is reduced by about 18% if the even mean is used instead of the mean to compute the correlated indices in the WFCAM database. Even statistics allows us to improve the effectiveness of both correlated and non-correlated indices. The correlated variability indices, proposed in the first paper of this series, are also improved if the even mean is used. The even parameters will also be useful for classifying light curves in the last step of this project. We consider that the first step of this project, where we set new techniques and methods that provide a huge improve on the efficiency of selection of variable stars, is now complete.

20 pages, 8 figures, paper accepted to A&A

References in corpus (17)

• The UKIRT Infrared Deep Sky Survey (UKIDSS)
• The WFCAM Science Archive
• The UKIRT Wide Field Camera ZYJHK Photometric System: Calibration from 2MASS
• The VISTA Science Archive
• Automated supervised classification of variable stars I. Methodology
• Comparative performance of selected variability detection techniques in photometric time series
• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
• Stellar Cycles from Photometric Data: CoRoT Stars
• The rotational behavior of Kepler Stars with Planets
• Image-Subtraction Photometry of Variable Stars in the Globular Clusters NGC 6388 and NGC 6441
• Detection and Removal of Artifacts in Astronomical Images
• The University of New South Wales Extrasolar Planet Search: a catalogue of variable stars from fields observed 2004--2007
• The ACS Nearby Galaxy Survey Treasury III: Cepheids in the Outer Disk of M81
• New Insights into Time Series Analysis - I - Correlated observations
• The variability behavior of CoRoT M-giant Stars
• A study of variable stars in the open cluster NGC 1582 and its surrounding field
• Exploring the Variable Sky with the Sloan Digital Sky Survey

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• Robust Identification of Active Galactic Nuclei through HST Optical Variability in GOODS-S: Comparison with the X-ray and mid-IR Selected Samples
• 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
• Understanding of the properties of neural network approaches for transient light curve approximations
• Semi-Supervised Classification and Clustering Analysis for Variable Stars
• 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