A Matched Filter Method for Ground-Based Sub-Noise Detection of Terrestrial Extrasolar Planets in Eclipsing Binaries: Application to CM Draconis

Jenkins, Jon M.; Doyle, Laurance R.; Cullers, D. K.

doi:10.1006/icar.1996.0018

Cited by 95 publications

(75 citation statements)

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“…Several transit detection algorithms were proposed in the recent literature: Bayesian algorithms (Doyle et al 2000;Defaÿ et al 2001;Aigrain & Favata 2002), matched filters (Jenkins et al 1996), box-shaped transit finder (Aigrain & Irwin 2004), and the Box-fitting Least Squares (BLS) method (Kovács et al 2002). A theoretical comparison of these methods was proposed (Tingley 2003), which concluded that "no detector is clearly superior for all transit signal energies", but an optimized BLS algorithm still performs slightly better for shallower transits.…”

Section: Introductionmentioning

confidence: 99%

Comparative blind test of five planetary transit detection algorithms on realistic synthetic light curves

Moutou¹,

Pont

Barge³

et al. 2005

A&A

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Abstract. Because photometric surveys of exoplanet transits are very promising sources of future discoveries, many algorithms are being developed to detect transit signals in stellar light curves. This paper compares such algorithms for the next generation of space-based transit detection surveys like CoRoT, Kepler, and Eddington. Five independent analyses of a thousand synthetic light curves are presented. The light curves were produced with an end-to-end instrument simulator and include stellar microvariability and a varied sample of stellar and planetary transits diluted within a much larger set of light curves. The results show that different algorithms perform quite differently, with varying degrees of success in detecting real transits and avoiding false positives. We also find that the detection algorithm alone does not make all the difference, as the way the light curves are filtered and detrended beforehand also has a strong impact on the detection limit and on the false alarm rate. The microvariability of sun-like stars is a limiting factor only in extreme cases, when the fluctuation amplitudes are large and the star is faint. In the majority of cases it does not prevent detection of planetary transits. The most sensitive analysis is performed with periodic box-shaped detection filters. False positives are method-dependent, which should allow reduction of their detection rate in real surveys. Background eclipsing binaries are wrongly identified as planetary transits in most cases, a result which confirms that contamination by background stars is the main limiting factor. With parameters simulating the CoRoT mission, our detection test indicates that the smallest detectable planet radius is on the order of 2 Earth radii for a 10-day orbital period planet around a K0 dwarf.

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Section: Introductionmentioning

confidence: 99%

Comparative blind test of five planetary transit detection algorithms on realistic synthetic light curves

Moutou¹,

Pont

Barge³

et al. 2005

A&A

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“…Jenkins et al 1996), and serendipitous approaches that comb photometric surveys gathered for other purposes which contain ∼10 observations and endeavor to identify candidates based on a single observation during an eclipse/transit (e.g. Ford et al 2008).…”

Section: Methodsmentioning

confidence: 99%

“…A numerical method -such as the BLS (Kovács et al 2002) -is used to assess how well each individual test light curve matches the observed light curve, with the best match designated the most statistically likely set of transit parameters. The number of possible test transits is reduced by using the properties of the transits (a periodic event that can be well-approximated by a constant decrease in flux for a fixed duration) and by taking small enough steps sizes (particularly in period) that the correlation coefficient between adjacent test light curves is 0.5 (Jenkins et al 1996) -or some similar criterion.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, it will assumedly recover 100% of transits in the data, although this comes at a high price computationally. That is the main downside of this approach: the number of test light curves necessary to cover parameter space increases linearly with the time baseline (Jenkins et al 1996). Given Gaia's long time baseline and considerable number of targets, this approach becomes computationally ponderous in the extreme.…”

Section: Methodsmentioning

confidence: 99%

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Searching for transits in data with long time baselines and poor sampling

Tingley

2011

A&A

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Aims. The standard method of searching parameter space for transits is ill-suited to data sets with long time baselines and poor temporal coverage, such as that anticipated from Gaia. In this paper, we present an alternative method for identifying transit candidates is such data, one focusing on finding periodicity in high S/N outliers. Methods. We describe a technique for testing a small number of flux measurements for periodicity and consistency with an origin in a transit with a constant change in flux and test their performance with Monte Carlo simulations. To complement this, we also include a description of a statistical method to analyze the distribution of these measurements to determine if they are normally distributed around a constant, reduce flux consistent with a planetary transits. Results. Large numbers of light curves can be quickly scanned for transit signatures with minimal loss in effectiveness for data sets with long time baselines and poor temporal coverage, where one observation per transit is the norm by testing for periodicity and analyzing their distribution. Conclusions. If the noise characteristics of the data set and the intrinsic noise of the individual stars are understood, this method focusing on statistical outliers is nearly equivalent to the standard method of scanning parameter space and significantly faster, if the signal noise, the individual transits are sampled no more than once and a periodicity test is employed. Moreover, the test for a transit origin can eliminate additional false positives.

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“…exoplanet transits or eclipsing binaries in general, Cepheids, RR Lyrae stars, novae), template fitting (e.g. Jenkins, Doyle & Cullers 1996, Macri et al 1999, Prša et al 2011, Sesar et al 2013, Angeloni et al 2014, Hoffmann et al 2016) with various trial periods/flare development time-scales can be performed. Simple cuts on lightcurve parameters (Henze, Meusinger & Pietsch 2008, Graczyk & Eyer 2010 as well as advanced machine learning techniques (Feeney et al 2005) can be used to select lightcurves of a known shape from a large photometric data set.…”

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confidence: 99%

Comparative performance of selected variability detection techniques in photometric time series data

Sokolovsky

Gavras

Karampelas

et al. 2016

Mon. Not. R. Astron. Soc.

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Photometric measurements are prone to systematic errors presenting a challenge to lowamplitude variability detection. In search for a general-purpose variability detection technique able to recover a broad range of variability types including currently unknown ones, we test 18 statistical characteristics quantifying scatter and/or correlation between brightness measurements. We compare their performance in identifying variable objects in seven time series data sets obtained with telescopes ranging in size from a telephoto lens to 1 m-class and probing variability on time-scales from minutes to decades. The test data sets together include lightcurves of 127539 objects, among them 1251 variable stars of various types and represent a range of observing conditions often found in ground-based variability surveys. The real data are complemented by simulations. We propose a combination of two indices that together recover a broad range of variability types from photometric data characterized by a wide variety of sampling patterns, photometric accuracies and percentages of outlier measurements. The first index is the interquartile range (IQR) of magnitude measurements, sensitive to variability irrespective of a time-scale and resistant to outliers. It can be complemented by the ratio of the lightcurve variance to the mean square successive difference, 1/η, which is efficient in detecting variability on time-scales longer than the typical time interval between observations. Variable objects have larger 1/η and/or IQR values than non-variable objects of similar brightness. Another approach to variability detection is to combine many variability indices using principal component analysis. We present 124 previously unknown variable stars found in the test data.

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A Matched Filter Method for Ground-Based Sub-Noise Detection of Terrestrial Extrasolar Planets in Eclipsing Binaries: Application to CM Draconis

Cited by 95 publications

References 0 publications

Comparative blind test of five planetary transit detection algorithms on realistic synthetic light curves

Comparative blind test of five planetary transit detection algorithms on realistic synthetic light curves

Searching for transits in data with long time baselines and poor sampling

Comparative performance of selected variability detection techniques in photometric time series data

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