Related papers: Identifying Exoplanets with Deep Learning. V. Impr…

With manual searching processes, the rate at which scientists and astronomers discover exoplanets is slow because of inefficiencies that require an extensive time of laborious inspections. In fact, as of now there have been about only 5,000…

The Transiting Exoplanet Survey Satellite (TESS) has a goal of detecting small planets orbiting stars bright enough for mass determination via ground-based radial velocity observations. Here we present estimates of how many exoplanets the…

NASA's Kepler Space Telescope has been instrumental in the task of finding the presence of exoplanets in our galaxy. This search has been supported by computational data analysis to identify exoplanets from the signals received by the…

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…

Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove trends…

The Transiting Exoplanet Survey Satellite (TESS) is continuing its second extended mission after 55 sectors of observations. TESS publishes full-frame images (FFI) at a cadence of 1800, 600, or 200 seconds, allowing light curves to be…

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…

We present high-precision light curves of several M- and K-type, active detached eclipsing binaries (DEBs), recorded with 2-minute cadence by the Transiting Exoplanet Survey Satellite (TESS). Analysis of these curves, combined with new and…

The Transiting Exoplanet Survey Satellite (TESS) will perform a two-year survey of nearly the entire sky, with the main goal of detecting exoplanets smaller than Neptune around bright and nearby stars. There do not appear to be any…

We present RAVEN, a newly developed vetting and validation pipeline for TESS exoplanet candidates. The pipeline employs a Bayesian framework to derive the posterior probability of a candidate being a planet against a set of False Positive…

We present a systematic phase curve analysis of known transiting systems observed by the Transiting Exoplanet Survey Satellite (TESS) during year one of the primary mission. Using theoretical predictions for the amplitude of the planetary…

We present 2,241 exoplanet candidates identified with data from the Transiting Exoplanet Survey Satellite (TESS) during its two-year prime mission. We list these candidates in the TESS Objects of Interest (TOI) Catalog, which includes both…

Continuous data releases throughout the TESS primary mission will provide unique opportunities for the exoplanet community at large to contribute to maximizing TESS's scientific return via the discovery and validation of transiting planets.…

The legacy of NASA's K2 mission has provided hundreds of transiting exoplanets that can be revisited by new and future facilities for further characterization, with a particular focus on studying the atmospheres of these systems. However,…

Data from the Transiting Exoplanet Survey Satellite (TESS) has produced of order one million light curves at cadences of 120 s and especially 1800 s for every ~27-day observing sector during its two-year nominal mission. These data…

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…

Context. As the number of detected transiting exoplanet candidates continues to grow, the need for robust and scalable automated tools to prioritize or validate them has become increasingly critical. Among the most promising solutions, deep…

The upcoming TESS mission is expected to find thousands of transiting planets around bright stars, yet for three-quarters of the fields observed the temporal coverage will limit discoveries to planets with orbital periods below 13.7 days.…

We examine the ability of the Transiting Exoplanet Survey Satellite (TESS) to detect and improve our understanding of planetary systems in the Kepler field. By modeling the expected transits of all confirmed and candidate planets detected…

Current space-based missions, such as the Transiting Exoplanet Survey Satellite (TESS), provide a large database of light curves that must be analysed efficiently and systematically. In recent years, deep learning (DL) methods, particularly…