相关论文: Automated identification of transiting exoplanet c…

The Transiting Exoplanet Survey Satellite (TESS) is a NASA-sponsored Explorer mission that will perform a wide-field survey for planets that transit bright host stars. Here, we predict the properties of the transiting planets that TESS will…

NASA's Kepler Space Telescope was designed to determine the frequency of Earth-sized planets orbiting Sun-like stars, but these planets are on the very edge of the mission's detection sensitivity. Accurately determining the occurrence rate…

The ESA Ariel mission has been adopted for launch in 2029 and will conduct a survey of around one thousand exoplanetary atmospheres during its primary mission life. By providing homogeneous datasets, with a high SNR and wide wavelength…

We report first results from the CHinese Exoplanet Searching Program from Antarctica (CHESPA)---a wide-field high-resolution photometric survey for transiting exoplanets carried out using telescopes of the AST3 (Antarctic Survey Telescopes…

The high number of planet discoveries made in the last years provides a good sample for statistical analysis, leading to some clues on the distributions of planet parameters, like masses and periods, at least in close proximity to the host…

In this work, six convolutional neural networks (CNNs) have been trained based on %different feature images and arrays from the database including 15,638 superflare candidates on solar-type stars, which are collected from the three-years…

The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of millions of stars with time spans ranging from $\sim$ 27 days to about 1 year of continuous observations. This vast amount of data contains a wealth of information…

A machine learning technique with two-dimension convolutional neural network is proposed for detecting exoplanet transits. To test this new method, five different types of deep learning models with or without folding are constructed and…

The recently launched NASA Transiting Exoplanet Survey Satellite (TESS) mission is going to collect lightcurves for a few hundred million of stars and we expect to increase the number of pulsating stars to analyze compared to the few…

The advent of high-contrast imaging instruments combined with medium-resolution spectrographs allows spectral and temporal dimensions to be combined with spatial dimensions to detect and potentially characterize exoplanets with higher…

The Transiting Exoplanet Survey Satellite (TESS) will be conducting a nearly all-sky photometric survey over two years, with a core mission goal to discover small transiting exoplanets orbiting nearby bright stars. It will obtain 30-minute…

The Transiting Exoplanet Survey Satellite (TESS) is currently concluding its 2-year primary science mission searching 85% of the sky for transiting exoplanets. TESS has already discovered well over one thousand TESS objects of interest…

The rapid expansion of exoplanet survey missions such as Kepler, TESS, and the upcoming PLATO mission has generated massive light-curve datasets that challenge traditional vetting pipelines. We introduce a hybrid deep-learning framework…

Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well proven object…

The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in Full-Frame Image mode with a time resolution of 200 seconds to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of…

We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for…

Space-based photometric surveys have discovered large numbers of planets transiting other stars, but these observe in a single band-pass and yield only the planet radius, orbital period, and transit duration. Information on the masses,…

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric…

This paper discusses the transit model fitting and multiple-planet search algorithms and performance of the Kepler Science Data Processing Pipeline, developed by the Kepler Science Operations Center (SOC). Threshold Crossing Events (TCEs),…

With the development of ever-improving telescopes capable of observing exoplanet atmospheres in greater detail and number, there is a growing demand for enhanced 3D climate models to support and help interpret observational data from space…