Finding binaries from phase modulation of pulsating stars with Kepler – VI. Orbits for 10 new binaries with mischaracterized primaries

Murphy, Simon J.; Barbara, Nicholas H.; Hey, Daniel R.; Bedding, Timothy R.; Fulcher, Ben D.

doi:10.1093/mnras/staa562

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…There are relatively few stars in the Kepler sample that are hotter than this range, while pulsations on the cooler side are dominated by a single class (solar-like oscillations) that have already been extensively classified studied (see Jackiewicz 2021 for a recent review). We note that we have previously used our method to identify samples of 𝛿 Scuti stars (Murphy et al 2020) and 𝛾 Doradus stars (Li et al 2020). Finally, we compare our classification of Kepler stars to labels assigned by a less interpretable, performance-focused classifier in Audenaert et al (2021), where we find similar results.…”

Section: Introductionmentioning

confidence: 55%

“…The catalogue is intended as a useful tool in searching for candidate variable stars of interest. We note that this catalogue and our broader methodology have already proven useful in identifying new 𝛾 Doradus stars (Li et al 2019) and 𝛿 Scuti stars (Murphy et al 2020) in the Kepler field. We provide suggestions for searching our catalogue in Section 4.3.2.…”

Section: Classifying Unlabelled Starsmentioning

confidence: 88%

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Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning

Barbara,

Bedding,

Fulcher

et al. 2022

Preprint

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With the availability of large-scale surveys like Kepler and TESS, there is a pressing need for automated methods to classify light curves according to known classes of variable stars. We introduce a new algorithm for classifying light curves that compares 7000 time-series features to find those which most effectively classify a given set of light curves. We apply our method to Kepler light curves for stars with effective temperatures in the range 6500-10,000 K. We show that the sample can be meaningfully represented in an interpretable five-dimensional feature space that separates seven major classes of light curves (𝛿 Scuti stars, 𝛾 Doradus stars, RR Lyrae stars, rotational variables, contact eclipsing binaries, detached eclipsing binaries, and non-variables). We achieve a balanced classification accuracy of 82% on an independent test set of Kepler stars using a Gaussian mixture model classifier. We use our method to classify 12,000 Kepler light curves from Quarter 9 and provide a catalogue of the results. We further outline a confidence heuristic based on probability density with which to search our catalogue, and extract candidate lists of correctly-classified variable stars.

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Section: Introductionmentioning

confidence: 55%

Section: Classifying Unlabelled Starsmentioning

confidence: 88%

Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning

Barbara,

Bedding,

Fulcher

et al. 2022

Preprint

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“…type δ Scuti stars, has been highly successful in identifying binary systems (Murphy et al 2018(Murphy et al , 2020. The sensitivity of pulsation timing is proportional to the oscillation frequency, and, as a result, only one planet has successfully been detected using pulsation timing with δ Sct stars (Murphy et al 2016).…”

Section: Phase Of Oscillation Modesmentioning

confidence: 99%

“…In previous papers, the time-delay has been modeled for binary systems where the orbital period was not known a priori (Murphy et al 2020;Hey et al 2020). In those cases, the orbital period was obtained by either subdividing the light curve into equal segments or by 'brute-forcing' a model over a grid of orbital periods to observe where the model likelihood peaks.…”

Section: Phase Of Oscillation Modesmentioning

confidence: 99%

A search for transits among the δ Scuti variables in Kepler

Hey,

Montet,

Pope

et al. 2021

Preprint

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We search for transits around all known pulsating δ Sct variables (6500 K < T eff < 10 000 K) in the long-cadence Kepler data after subtracting the pulsation signal through an automated routine. To achieve this, we devise a simple and computationally inexpensive method for distinguishing between low-frequency pulsations and transits in light curves. We find 3 new candidate transit events that were previously hidden behind the pulsations, but caution that they are likely to be false positive events. We also examined the Kepler Objects of Interest catalog and identify 13 additional host stars which show δ Sct pulsations. For each star in our sample, we use the non-detection of pulsation timing variations for a planet that is known to be transiting a δ Sct variable to obtain both an upper limit on the mass of the planet and the expected radial velocity semi-amplitude of the host star. Simple injection tests of our pipeline imply 100% recovery for planets of 0.5 R Jup or greater. Extrapolating our number of Kepler δ Sct stars, we expect 12 detectable planets above 0.5 R Jup in TESS. Our sample contains some of the hottest known transiting planets around evolved stars, and is the first complete sample of transits around δ Sct variables. We make available our code and pulsation-subtracted light curves to facilitate further analysis.

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“…The full power of the PT technique, however, revealed itself when the nearly uninterrupted 4 yr baseline of Kepler photometry was exploited. Some works (Shibahashi & Kurtz 2012;Murphy et al 2014;Shibahashi et al 2015;Murphy et al 2018Murphy et al , 2020, although they focused on the detection and characterization of stellar-mass companions, demonstrated that under favorable assumptions, even LTTE signals from planetary-mass companions ( 13 M jup ) are in principle detectable through space-based photometry (Murphy et al 2014). Murphy et al (2016) presented the PT detection of a massive planetary candidate for the first time (11.8 ± 0.7 M jup , P = 840 ± 20 d).…”

Section: Introductionmentioning

confidence: 99%

TESS search for substellar companions through pulsation timing of δ Scuti stars

Vaulato

Nascimbeni

Piotto

2022

A&A

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Early-type main-sequence pulsating stars such as δ Scuti variables are one of the least explored class of targets in the search for exoplanets. Pulsation timing (PT) is an alternative technique to the most effective search methods. It exploits the light-travel-time effect (LTTE) to infer the presence of additional massive bodies around a pulsating star by measuring a periodic phase modulation of its signal. PT has been extremely successful in discovering and characterizing stellar binaries when it was applied to high-precision light curves over long temporal baselines, such as those delivered by the Kepler mission. In favorable conditions, the sensitivity of PT can reach the planetary-mass regime; one such candidate has already been claimed. The advent of TESS, with its nearly full-sky coverage and the availability of full-frame images, opens a great opportunity to expand this field of research. In this work, we present a pilot study aimed to understand the potential of PT as applied to TESS data, which are considerably different from Kepler data in terms of photometric noise, sampling cadence, and temporal baseline. We focused on the most favorable class of δ Scuti, that is, those showing strong pulsations and very simple frequency spectra. After the development of a customized pipeline, we were able to detect candidate companions for two targets in the (sub-)stellar mass regime: Chang 134 (43 ± 5 M jup , P 82 d) and V393 Car ( 100 M jup , P 700 d). Our results also highlight the limiting factors of this technique and the importance of an accurate absolute time calibration for future missions such as PLATO.

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Finding binaries from phase modulation of pulsating stars with Kepler – VI. Orbits for 10 new binaries with mischaracterized primaries

Cited by 9 publications

References 37 publications

Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning

Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning

A search for transits among the δ Scuti variables in Kepler

TESS search for substellar companions through pulsation timing of δ Scuti stars

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