New insights into time series analysis

Lopes, C. E. Ferreira; Cross, N. J. G.

doi:10.1051/0004-6361/201526733

Cited by 19 publications

(16 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…α cor are values related to Eq. (16) of Ferreira Lopes & Cross (2016). compared to those found in Table 2 of paper I. This reduction is related with to the sources that have few correlations.…”

mentioning

confidence: 69%

“…Current techniques of data processing can be improved considerably. For instance, the flux independent index that we proposed in a previous paper reduces the mis-selection of variable sources by about 250% (Ferreira Lopes & Cross 2016). A reliable selection of astronomical databases allows us to put forward faster A&A 604, A121 (2017) scientific results such as those enclosed in many current surveys (e.g.…”

Section: Introductionmentioning

confidence: 92%

“…Moreover, Ferreira Lopes et al (2015a) andCross (2016) performed a comprehensive stellar variability analysis of the WFCAMCAL08B characterizing the photometric data and identifying 319 stars (WVSC1), of which 275 are classified as periodic variable stars and 44 objects as suspected variables or apparent aperiodic variables. In this paper we analyze the same sample from (Ferreira Lopes et al 2015a;and Ferreira Lopes & Cross 2016). First, we selected all sources classified as a star or probable star with at least 10 unflagged epochs in any of the five filters.…”

Section: Real Datamentioning

confidence: 99%

See 2 more Smart Citations

New insights into time series analysis

Lopes

Cross

2017

A&A

Self Cite

View full text Add to dashboard Cite

Context. Statistical parameters are used to draw conclusions in a vast number of fields such as finance, weather, industrial, and science. These parameters are also used to identify variability patterns on photometric data to select non-stochastic variations that are indicative of astrophysical effects. New, more efficient, selection methods are mandatory to analyze the huge amount of astronomical data. Aims. We seek to improve the current methods used to select non-stochastic variations on non-correlated data. Methods. We used standard and new data-mining parameters to analyze non-correlated data to find the best way to discriminate between stochastic and non-stochastic variations. A new approach that includes a modified Strateva function was performed to select non-stochastic variations. Monte Carlo simulations and public time-domain data were used to estimate its accuracy and performance. Results. We introduce 16 modified statistical parameters covering different features of statistical distribution such as average, dispersion, and shape parameters. Many dispersion and shape parameters are unbound parameters, i.e. equations that do not require the calculation of average. Unbound parameters are computed with single loop and hence decreasing running time. Moreover, the majority of these parameters have lower errors than previous parameters, which is mainly observed for distributions with few measurements. A set of non-correlated variability indices, sample size corrections, and a new noise model along with tests of different apertures and cut-offs on the data (BAS approach) are introduced. The number of mis-selections are 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 I. The mis-selection 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. Conclusions. The selection of non-stochastic variations is improved by non-correlated indices. The even-averages provide a better estimation of mean and median for almost all statistical distributions analyzed. The correlated variability indices, which are 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 improvement on the efficiency of selection of variable stars, is now complete. Many of these techniques may be useful for a large number of fields. Next, we will commence a new step of this project regarding the analysis of period search methods.

show abstract

mentioning

confidence: 69%

Section: Introductionmentioning

confidence: 92%

Section: Real Datamentioning

confidence: 99%

See 1 more Smart Citation

New insights into time series analysis

Lopes

Cross

2017

A&A

Self Cite

View full text Add to dashboard Cite

show abstract

“…Setting correct inputs using either method to select variable stars or to perform frequency finding searches is mandatory to get accurate outputs. The variability indices used to select variable stars candidates were studied deeply in the first two papers of this series, Ferreira Lopes & Cross (2016. These studies enabled us to provide the optimal constraints on noise models and establish welldefined criteria to settle the best approach to discriminate variable stars from noise as well as to affirm that the selection of a reliable sample is unfeasible using variability indices.…”

Section: Resultsmentioning

confidence: 99%

“…4.3 in Ferreira Lopes & Cross 2016), produces a substantially smaller number of time series which have to be further analysed. However, the resulting selection is still three or more times larger than just the well-defined signals, according to Ferreira Lopes & Cross (2016. This means that the set of preliminary selection criteria is unable to produce samples comprised only of variable stars, and so, it would be desirable that the following steps of signal searching methods would provide reliable identifications and accurate estimates of periods (frequencies) and amplitudes, even in cases where the preliminary analysis failed to give a confident indicator that the signal was truly variable and not just a noisy time series.…”

Section: Introductionmentioning

confidence: 99%

New Insights into Time Series Analysis III: Setting constraints on period analysis

Lopes

Cross

Jablonski

2018

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

E-science of photometric data requires automatic procedures and a precise recognition of periodic patterns to perform science as well as possible on large data. Analytical equations that enable us to set the best constraints to properly reduce processing time and hence optimize signal searches play a crucial role in this matter. These are increasingly important because the production of unbiased samples from variability indices and statistical parameters has not been achievable so far. We discuss the constraints used in periodic signals detection methods as well as the uncertainties in the estimation of periods and amplitudes. The frequency resolution necessary to investigate a time series is assessed with a new approach that estimates the necessary sampling resolution from shifts on the phase diagrams for successive frequency grid points.We demonstrate the underlying meaning of the oversampling factor. We reassess the frequency resolutions required to find the variability periods of EA stars and use the new resolutions to analyse a small sample of EA up Catalina stars, i.e. EA stars previously classified as having insufficient number of observations at the eclipses. As a result, the variability periods of four EA up stars were determined. Moreover, we have a new approach to estimate the amplitude and period variations. From these estimations information about the intrinsic variations of the sources are obtained. For a complete characterization of the light curve signal the period uncertainty and period variation must be determined. Constraints on periodic signal searches were analysed and delimited.

show abstract

VaST: A variability search toolkit

Sokolovsky

Lebedev

2018

Astronomy and Computing

View full text Add to dashboard Cite

Variability Search Toolkit (VaST) is a software package designed to find variable objects in a series of sky images. It can be run from a script or interactively using its graphical interface. VaST relies on source list matching as opposed to image subtraction. SExtractor is used to generate source lists and perform aperture or PSF-fitting photometry (with PSFEx). Variability indices that characterize scatter and smoothness of a lightcurve are computed for all objects. Candidate variables are identified as objects having high variability index values compared to other objects of similar brightness. The two distinguishing features of VaST are its ability to perform accurate aperture photometry of images obtained with non-linear detectors and handle complex image distortions. The software has been successfully applied to images obtained with telescopes ranging from 0.08 to 2.5 m in diameter equipped with a variety of detectors including CCD, CMOS, MIC and photographic plates. About 1800 variable stars have been discovered with VaST. It is used as a transient detection engine in the New Milky Way (NMW) nova patrol. The code is written in C and can be easily compiled on the majority of UNIX-like systems. VaST is free software available at http://scan.sai.msu.ru/vast/

show abstract

New insights into time series analysis

Cited by 19 publications

References 58 publications

New insights into time series analysis

New insights into time series analysis

New Insights into Time Series Analysis III: Setting constraints on period analysis

VaST: A variability search toolkit

Contact Info

Product

Resources

About