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 some of these methods to improve the conventional algorithm based approaches presently used in astrophysics to detect exoplanets. Using the time-series analysis library TSFresh to analyse light curves, we extracted 789 features from each curve, which capture the information about the characteristics of a light curve. We then used these features to train a gradient boosting classifier using the machine learning tool lightgbm. This approach was tested on simulated data, which showed that is more effective than the conventional box least squares fitting (BLS) method. We further found that our method produced comparable results to existing state-of-the-art deep learning models, while being much more computationally efficient and without needing folded and secondary views of the light curves. For Kepler data, the method is able to predict a planet with an AUC of 0.948, so that 94.8 per cent of the true planet signals are ranked higher than non-planet signals. The resulting recall is 0.96, so that 96 per cent of real planets are classified as planets. For the Transiting Exoplanet Survey Satellite (TESS) data, we found our method can classify light curves with an accuracy of 0.98, and is able to identify planets with a recall of 0.82 at a precision of 0.63.
Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods
Catholic University of America（美国天主教大学）；Tel Aviv University（特拉维夫大学）；ThetaRay（ThetaRay公司）
一种使用机器学习（ML）方法的新型人工智能（AI）技术结合了由ThetaRay，Inc.应用于NASA的凌日系外行星勘测卫星（TESS）数据集，以识别系外行星候选者。AI/ML ThetaRay系统最初使用开普勒系外行星数据进行训练，并在应用于TESS数据之前使用确认的系外行星进行验证。数据的现有和新特征基于各种观测参数，通过采用半监督和无监督机器学习技术构建并用于AI/ML分析。通过将ThetaRay系统应用于TESS任务产生的10，803个阈值交叉事件（TCEs）的光变曲线，该算法从Mikulski Archive for Space Telescopes获得，该算法产生了约50个目标以供进一步分析，我们通过进一步的人工审查发现了三个新的系外行星候选者。这项研究首次展示了将特定的基于AI/ML的方法成功应用于大型天体物理数据集，以快速自动分类TCE。
Machine-learning clustering of close-in exoplanet populations: links to pebble accretion
Center for Star and Planet Formation（星系与行星形成中心）；Globe Institute（全球研究所）；University of Copenhagen（哥本哈根大学）；Lund Observatory（隆德天文台）；Department of Physics（物理系）；Lund University（隆德大学）；Graduate Institute of Astronomy（天文研究所）；National Central University（国立中央大学）
Locating Hidden Exoplanets in ALMA Data Using Machine Learning
The University of Georgia（佐治亚大学）；University of California, San Francisco（加州大学旧金山分校）；Cardiovascular Research Institute（心血管研究所）；The University of Alabama（阿拉巴马大学）
Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method
BITS Pilani, K. K. Birla Goa Campus（印度博拉理工学院果阿分校）；CSIS and APPCAIR, BITS Pilani, K. K. Birla Goa Campus（印度博拉理工学院果阿分校CSIS与APPCAIR中心）；Indian Institute of Astrophysics（印度天体物理研究所）
Deep learning for exoplanet detection and characterization by direct imaging at high contrast
Département d'Informatique de l' \'E cole normale supérieure (ENS-PSL, CNRS, Inria)；Universite Claude Bernard Lyon 1, Centre de Recherche Astrophysique de Lyon UMR 5574, ENS de Lyon, CNRS, Villeurbanne, France；Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK；Courant Institute；Center for Data Science, New York University；LIRA, Observatoire de Paris, Université PSL, Sorbonne Université, Université Paris Cité, CNRS, Meudon, France
Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission
Department of Industrial Physics, Enugu State University of Science and Technology（恩古州科技大学工业物理系）；Department of Physics, Dennis Osadebay University（德尼斯·奥萨德贝大学物理系）
Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopy
Physics Department, University of Florida, Gainesville, FL 32611, USA（佛罗里达大学物理系）；Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA（阿拉巴马大学物理与天文学系）
Analyzing the Habitable Zones of Circumbinary Planets Using Machine Learning
Beijing Normal University（北京师范大学）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Max Planck Institute for Astronomy（马克斯·普朗克天文研究所）
FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning
Kapteyn Astronomical Institute, University of Groningen（格罗宁根大学卡普坦天文研究所）；Netherlands Space Research Institute (SRON)（荷兰空间研究所（SRON））；Centre for Exoplanet Science, University of Edinburgh（爱丁堡大学系外行星科学中心）；School of GeoSciences, University of Edinburgh（爱丁堡大学地球科学学院）
Vetting Ambiguous Planetary Candidates (APC) in TESS Data: Insights from LATTE Package and Detection of Nine Potential Exoplanet Candidates
Pakistan Space and Upper Atmosphere Research Commission (SUPARCO)（巴基斯坦空间与高层大气研究委员会）；Institute of Frontier and Interdisciplinary Science, Shandong University（山东大学前沿交叉科学研究院）
3D magneto-hydrodynamical simulations of stellar convective noise for improved exoplanet detection. I. Case of regularly sampled radial velocity observations
A Planetary Illusion's Funeral: Non-detection of a Gaia DR3 Exoplanet Candidate, and the Role of Intermediate-precision Radial Velocities in Gaia Exoplanet Follow-up
Centre for Astrophysics, University of Southern Queensland（南昆士兰大学天体物理中心）；Max Planck Institute for Astronomy（马克斯·普朗克天文学研究所）；Center for Astrophysics, Harvard & Smithsonian（哈佛-史密森尼天体物理中心）
Machine Learning for Exoplanet Detection in High-Contrast Spectroscopy: Revealing Exoplanets by Leveraging Hidden Molecular Signatures in Cross-Correlated Spectra with Convolutional Neural Networks
Institute for Particle Physics and Astrophysics, ETH Zürich（苏黎世联邦理工学院粒子物理与天体物理研究所）；Seminar für Statistik, ETH Zürich（苏黎世联邦理工学院统计研讨会）；Department of Astronomy, University of Michigan（密歇根大学天文系）；Département d’Astronomie, Université de Genève（日内瓦大学天文系）；STAR Institute, University of Liège（列日大学STAR研究所）
本文提出 MLCCS，用感知机和一维卷积神经网络分析互相关光谱中的隐藏分子信号，在 SINFONI K 波段模拟数据上显著提升暗弱系外行星探测灵敏度。
