相关论文: Astrophysically robust systematics removal using v…

Light curves produced by wide-field exoplanet transit surveys such as CoRoT, Kepler, and TESS are affected by sensor-wide systematic noise which is correlated both spatiotemporally and with other instrumental parameters such as photometric…

Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic…

We present ARC2 (Astrophysically Robust Correction 2), an open-source Python-based systematics-correction pipeline to correct for the Kepler prime mission long cadence light curves. The ARC2 pipeline identifies and corrects any isolated…

The Kepler spacecraft has collected data of high photometric precision and cadence almost continuously since operations began on 2009 May 2. Primarily designed to detect planetary transits and asteroseismological signals from solar-like…

With the unprecedented photometric precision of the Kepler Spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities,…

Instrumental data are affected by systematic effects that dominate the errors and can be relevant when searching for small signals. This is the case of the K2 mission, a follow up of the Kepler mission, that, after a failure on two reaction…

We present K2SC (K2 Systematics Correction), a Python pipeline to model instrumental systematics and astrophysical variability in light curves from the K2 mission. K2SC uses Gaussian process regression to model position-dependent…

The recently approved NASA K2 mission has the potential to multiply by an order of magnitude the number of short-period transiting planets found by Kepler around bright and low-mass stars, and to revolutionise our understanding of stellar…

We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program of the mission. The methods, originally…

We present the results of an automated variability analysis of the Kepler public data measured in the first quarter (Q1) of the mission. In total, about 150 000 light curves have been analysed to detect stellar variability, and to identify…

Abstract abridged. Eclipsing binary systems provide the opportunity to measure the fundamental parameters of their component stars in a stellar-model-independent way. This makes them ideal candidates for testing and calibrating theories of…

Light curves feature many kinds of variability, including instrumental systematics, intrinsic stellar variability such as pulsations, and flux changes caused by transiting exoplanets or eclipsing binary stars. Detrending is a key…

We propose a new information theoretic metric for finding periodicities in stellar light curves. Light curves are astronomical time series of brightness over time, and are characterized as being noisy and unevenly sampled. The proposed…

Time-series photometry and spectroscopy of transiting exoplanets allow us to study their atmospheres. Unfortunately, the required precision to extract atmospheric information surpasses the design specifications of most general purpose…

We propose a new framework to predict stellar properties from light curves. We analyze the light-curve data from the Kepler space mission and develop a novel tool for deriving the stellar rotation periods for main-sequence stars. Using this…

This paper presents detailed consideration of methodologies to calibrate differential light curves for accurate physical starspot modeling. We use the Sun and starspot models as a testbed to highlight some factors in this calibration that…

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…

Thanks to missions like Kepler and TESS, we now have access to tens of thousands of high precision, fast cadence, and long baseline stellar photometric observations. In principle, these light curves encode a vast amount of information about…

Research into light curves from stars (temporal variation of brightness) has completely changed how exoplanets are discovered or characterised. This study including star light curves from the Kepler dataset as a way to discover exoplanets…

Recent developments in computational power and machine learning techniques motivate their use in many different astrophysical research areas. Consequently, many machine learning models have been trained to classify exoplanet transit signals…