Related papers: A Machine Learning Technique to Identify Transit S…
In the first three years of operation the Kepler mission found 3,697 planet candidates from a set of 18,406 transit-like features detected on over 200,000 distinct stars. Vetting candidate signals manually by inspecting light curves and…
We present the results of a search for potential transit signals in four years of photometry data acquired by the Kepler Mission. The targets of the search include 111,800 stars which were observed for the entire interval and 85,522 stars…
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…
We present results of the final Kepler Data Processing Pipeline search for transiting planet signals in the full 17-quarter primary mission data set. The search includes a total of 198,709 stellar targets, of which 112,046 were observed in…
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit…
In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify.…
Never before has the detection and characterization of exoplanets via transit photometry been as promising and feasible as it is now, due to the increasing breadth and sensitivity of time domain optical surveys. Past works have made use of…
We present and discuss five candidate exoplanetary systems identified with the Kepler spacecraft. These five systems show transits from multiple exoplanet candidates. Should these objects prove to be planetary in nature, then these five…
Detection of a planetary ring of exoplanets remains as one of the most attractive but challenging goals in the field. We present a methodology of a systematic search for exoplanetary rings via transit photometry of long-period planets. The…
We present the results of a search for potential transit signals in the full 17-quarter data set collected during Kepler's primary mission that ended on May 11, 2013, due to the on-board failure of a second reaction wheel needed to maintain…
The kepler and TESS missions have generated over 100,000 potential transit signals that must be processed in order to create a catalog of planet candidates. During the last few years, there has been a growing interest in using machine…
The Transiting Exoplanet Survey Satellite (TESS) is surveying a large fraction of the sky, generating a vast database of photometric time series data that requires thorough analysis to identify exoplanetary transit signals. Automated…
The Kepler Mission is monitoring the brightness of ~150,000 stars searching for evidence of planetary transits. As part of the "Hunt for Exomoons with Kepler" (HEK) project, we report a planetary system with two confirmed planets and one…
We present the results of a search for potential transit signals in the first three years of photometry data acquired by the Kepler Mission. The targets of the search include 112,321 targets which were observed over the full interval and an…
We present the results of a search for potential transit signals in the first three quarters of photometry data acquired by the Kepler Mission. The targets of the search include 151,722 stars which were observed over the full interval and…
New transiting planet candidates are identified in sixteen months (May 2009 - September 2010) of data from the Kepler spacecraft. Nearly five thousand periodic transit-like signals are vetted against astrophysical and instrumental false…
Context. Transit detection algorithms are mathematical tools used for detecting planets in the photometric data of transit surveys. In this work we study their application to space-based surveys. Aims: Space missions are exploring the…
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…
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…
Photometric surveys such as Kepler have the precision to identify exoplanet and eclipsing binary candidates from only a single transit. K2, with its 75d campaign duration, is ideally suited to detect significant numbers of single-eclipsing…