Finding outlier light curves in catalogues of periodic variable stars

Protopapas, Pavlos; Giammarco, J.; Faccioli, L.; Struble, M. F.; Dave, R.; Alcock, Charles

doi:10.1111/j.1365-2966.2006.10327.x

Cited by 73 publications

(86 citation statements)

References 32 publications

(41 reference statements)

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“…The simplest universal phasing algorithm aligns a set of times series by setting the maximum (or minimum) of each time series to time t . Protopapas et al developed a more robust version of this method that accounts for noise (Protopapas et al 2006). Rather than phase each time series to its maximum, the method sorts the values of the time series and isolates the highest and lowest ten percent.…”

Section: The Challenge Of Unphased Datamentioning

confidence: 99%

“…Figure 6 shows an example of an Eclipsing Binary light-curve before and after folding. The data are pre-processed according to a technique developed by Protopapas et al (2006). We refer the reader to Protopapas et al (2006) for an in-depth discussion of each technique and its parameters.…”

Section: Light-curve Datamentioning

confidence: 99%

“…The data are pre-processed according to a technique developed by Protopapas et al (2006). We refer the reader to Protopapas et al (2006) for an in-depth discussion of each technique and its parameters. In brief, we conservatively pre-process the data using spike removal, smoothing, interpolation and normalization.…”

Section: Light-curve Datamentioning

confidence: 99%

“…On the ogle data set we compare PCAD's performance to a robust solution called PN-MEANS (Protopapas et al 2006). This is a method published in an astrophysics journal for light-curve data specifically.…”

Section: Benchmark and Approximationsmentioning

confidence: 99%

“…Time t is set to the mean value of the cluster containing the higher (or lower) values. For full details on how universal phasing is performed, please refer to Protopapas et al (2006).…”

Section: The Challenge Of Unphased Datamentioning

confidence: 99%

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Finding anomalous periodic time series

et al. 2008

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Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single continuous time series or a set of time series whose periods are aligned. Light-curve data precludes the use of these methods as the periods of any given pair of light-curves may be out of sync. One may use an existing anomaly detection method if, prior to similarity calculation, one performs the costly act of aligning two light-curves, an operation that scales poorly to massive data sets. This paper presents PCAD, an unsupervised anomaly detection method for large sets of unsynchronized periodic time-series data, that outputs a ranked list of both global and local anomalies. It calculates its anomaly score for each light-curve in relation to a set of centroids produced by a modified k-means clustering algorithm. Our method is able to scale to large data sets through the use of sampling. We validate our method on both light-curve data and other time series data sets. We demonstrate its effectiveness at finding known anomalies, and discuss the effect of sample size and number of centroids on our results. We compare our method to naive solutions and existing time series anomaly detection

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Section: The Challenge Of Unphased Datamentioning

confidence: 99%

Section: Light-curve Datamentioning

confidence: 99%

Section: Light-curve Datamentioning

confidence: 99%

Section: Benchmark and Approximationsmentioning

confidence: 99%

“…Time t is set to the mean value of the cluster containing the higher (or lower) values. For full details on how universal phasing is performed, please refer to Protopapas et al (2006).…”

Section: The Challenge Of Unphased Datamentioning

confidence: 99%

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Finding anomalous periodic time series

et al. 2008

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ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

Zou

Liu

et al. 2019

Softw Pract Exp

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The application of anomaly detection to data monitoring is a fundamental requirement of the public service systems of a smart city. Many detection methods have been proposed for identifying anomalous situations, including methods based on periodicity or biseries correlations. However, the detection results of these methods are not ideal. Thus, we present a new anomaly detection algorithm for time series based on the relative outlier distance (ROD) and biseries correlations. The proposed algorithm detects outliers based on the ROD and identifies abnormal points and change points based on biseries correlations. Experimental results show that our method achieves better recall and F1-measure scores than various time series-based techniques while maintaining a high level of precision.

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A survey on machine learning based light curve analysis for variable astronomical sources

Zhang³

et al. 2021

WIREs Data Min & Knowl

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The improvement of observation capabilities has expanded the scale of new data available for time domain astronomy research, and the accumulation of observational data continues to accelerate. However, traditional data analysis methods are difficult to fully tap the potential scientific value of all data.Therefore, in the current and future research on light curve analysis, it is inevitable to use artificial intelligence (AI) technology to assist in data analysis in order to obtain as many candidates as possible with scientific research goals. This survey reviews important developments in light curve analysis over the past years, summarizes the basic concepts in machine learning and their applications in light curve analysis and concludes perspectives and challenges for light curve analysis in the near future. The full exploration of light curves of variable celestial objects relies heavily on new techniques derived from promotion of machine learning and deep learning in the astronomical big data era. This article is categorized under:deep learning, light curve analysis, machine learning, variable 1 | INTRODUCTION Time domain astronomy (TDA) is promoted by new wide-field multi-epoch sky surveys, and it is a research area that studies characteristics of the target celestial object changing with time by the large data sets coming from sky survey (Vaughan, 2013), and it is a branch of astronomy that studies astronomical phenomena with time-varying characteristics. TDA requires a sufficient number of observations for the same target celestial object in a certain time scale, and fits the obtained time series data to form a light curve. Quantitatively analyze the changing laws of time series to obtain specific astroinformatics on the target celestial object. The variable astronomical phenomena originate from the movement of celestial objects or systems and their physical changes, such as supernovae, pulsars, exoplanets, active galactic nuclei, and gamma-ray bursts. The analysis and statistics of time-varying celestial objects are important means to study the formation and evolution of the universe, galaxies, and various celestial systems.Ce Yu and Kun Li authors are equally contributed to this study.

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Finding outlier light curves in catalogues of periodic variable stars

Cited by 73 publications

References 32 publications

Finding anomalous periodic time series

Finding anomalous periodic time series

ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

A survey on machine learning based light curve analysis for variable astronomical sources

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