DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves

Melton, Elizabeth J.; Feigelson, E. D.; Montalto, M.; Caceres, Gabriel; Rosenswie, Andrew W.; Abelson, Cullen S.

doi:10.48550/arxiv.2302.06700

Cited by 2 publications

(6 citation statements)

References 53 publications

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“…Formal statistical time series diagnostics, such as the Shapiro-Wilk and Ljung-Box tests, can determine if the light curves deviate from Gaussian white noise. This is a significant problem: 36% of light curves from TESS Full Frame Images (FFIs) show statistically significant short-memory stochastic variability after spline detrending (Melton et al 2023b, their Figure 5).…”

Section: Difficulties With Detecting Small Transiting Planetsmentioning

confidence: 99%

“…ARIMA detrending for transiting planet detection was introduced by Caceres et al (2019a) and found to be effective in reducing unwanted light curve variations in most Kepler and TESS light curves (Caceres et al 2019b;Melton et al 2023b). The general utility of ARMA-type modeling for astronomical time-domain studies is discussed by Feigelson et al (2018).…”

Section: Difficulties With Detecting Small Transiting Planetsmentioning

confidence: 99%

“…11 While these coefficients may be larger than realistically present in many detrended TESS light curves, the simulations are designed to reveal clear differences between periodogram performance for white and correlated noise. For reference, ARIMA models have been applied to large samples of Kepler and TESS light curves by Caceres et al (2019b) and Melton et al (2023b), respectively, based on the methodology described by Caceres et al (2019a) and Melton et al (2023a).…”

Section: Construction and Analysis Of Simulated Light Curvesmentioning

confidence: 99%

“…Inaccuracies in TCF depth estimation may have several causes: (a) incorporation of some transit signal into the ARMA fit can reduce the estimated depth; (b) over-differencing by ARIMA can produce an anticorrelation at lag = 1 and increase the estimated depth; and (c) inaccurate registration of the cadence with respect to the transit ingress and egress can reduce the double-spike signal and estimated depth. See Figure 7 in Melton et al (2023a) and also see Melton et al (2023b) for more discussion. These problems with TCF depth estimation were noted by Caceres et al (2019b) and Melton et al (2023b) in their Kepler and TESS applications, which required improvement during the vetting phase of analysis.…”

Section: Supplemental Periodogram Analysesmentioning

confidence: 99%

“…See Figure 7 in Melton et al (2023a) and also see Melton et al (2023b) for more discussion. These problems with TCF depth estimation were noted by Caceres et al (2019b) and Melton et al (2023b) in their Kepler and TESS applications, which required improvement during the vetting phase of analysis. Difficulties with transit depth estimation also arise in BLS transit fitting, as discussed by Kovács et al (2002) and Ofir (2014).…”

Section: Supplemental Periodogram Analysesmentioning

confidence: 99%

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A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

Gondhalekar,

Feigelson,

Caceres

et al. 2023

ApJL

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The sensitivities of two periodograms are compared for weak signal planet detection in transit surveys: the widely used Box Least Squares (BLS) algorithm following light curve detrending and the Transit Comb Filter (TCF) algorithm following autoregressive ARIMA modeling. Small depth transits are injected into light curves with different simulated noise characteristics. Two measures of spectral peak significance are examined: the periodogram signal-to-noise ratio (S/N) and a false alarm probability (FAP) based on the generalized extreme value distribution. The relative performance of the BLS and TCF algorithms for small planet detection is examined for a range of light curve characteristics, including orbital period, transit duration, depth, number of transits, and type of noise. We find that the TCF periodogram applied to ARIMA fit residuals with the S/N detection metric is preferred when short-memory autocorrelation is present in the detrended light curve and even when the light curve noise had white Gaussian noise. BLS is more sensitive to small planets only under limited circumstances with the FAP metric. BLS periodogram characteristics are inferior when autocorrelated noise is present due to heteroscedastic noise and false period detection. Application of these methods to TESS light curves with known small exoplanets confirms our simulation results. The study ends with a decision tree that advises transit survey scientists on procedures to detect small planets most efficiently. The use of ARIMA detrending and TCF periodograms can significantly improve the sensitivity of any transit survey with regularly spaced cadence.

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Section: Difficulties With Detecting Small Transiting Planetsmentioning

confidence: 99%

Section: Difficulties With Detecting Small Transiting Planetsmentioning

confidence: 99%

Section: Construction and Analysis Of Simulated Light Curvesmentioning

confidence: 99%

Section: Supplemental Periodogram Analysesmentioning

confidence: 99%

Section: Supplemental Periodogram Analysesmentioning

confidence: 99%

See 3 more Smart Citations

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

Gondhalekar,

Feigelson,

Caceres

et al. 2023

ApJL

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The sherlock pipeline: new exoplanet candidates in the WASP-16, HAT-P-27, HAT-P-26, and TOI-2411 systems

Dévora-Pajares,

Pozuelos,

Thuillier

et al. 2024

Monthly Notices of the Royal Astronomical Society

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The launches of NASA Kepler and TESS missions have significantly enhanced the interest in the exoplanet field during the last 15 years, providing a vast amount of public data that is being exploited by the community thanks to the continuous development of new analysis tools. However, using these tools is not straightforward, and users must dive into different codes, input-output formats, and methodologies, hindering an efficient and robust exploration of the available data. We present the SHERLOCK pipeline, an end-to-end public software that allows the users to easily explore observations from space-based missions such as TESS or Kepler to recover known planets and candidates issued by the official pipelines and search for new planetary candidates that remained unnoticed. The pipeline incorporates all the steps to search for transit-like features, vet potential candidates, provide statistical validation, conduct a Bayesian fitting, and compute observational windows from ground-based observatories. Its performance is tested against a catalog of known and confirmed planets from the TESS mission, trying to recover the official TESS Objects of Interest (TOIs), explore the existence of companions that have been missed, and release them as new planetary candidates. SHERLOCK demonstrated an excellent performance, recovering 98% of the TOIs and confirmed planets in our test sample and finding new candidates. Specifically, we release four new planetary candidates around the systems WASP-16 (with P∼10.46 d and R∼2.20 R⊕), HAT-P-27 (with P∼1.20 d and R∼4.33 R⊕), HAT-P-26 (with P∼6.59 d and R∼1.97 R⊕), and TOI-2411 (with P∼18.75 d and R∼2.88 R⊕).

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DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves

Cited by 2 publications

References 53 publications

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

The sherlock pipeline: new exoplanet candidates in the WASP-16, HAT-P-27, HAT-P-26, and TOI-2411 systems

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