Direct imaging of exoplanets requires to separate the background noise from the exoplanet signals. Statistical methods have been recently proposed to avoid subtracting any signal of interest as opposed to initial self-subtracting methods based on Angular Differential Imaging (ADI). However, unless conservative thresholds are chosen to claim for a detection, such approaches tend to produce a list of candidates that include many false positives. Choosing high, conservative, thresholds leads to miss the faintest planets. We extend a statistical framework with a logistic regression to filter the list of candidates. Features with physical/optical meaning (in two wavelengths) are used, leading to a very fast and pragmatic approach. The overall method requires a simple edge detection (image processing) and clustering algorithm to work with sub-images. To estimate its efficiency, we apply our approach to targets observed with the ESO/SPHERE high contrast imager, that were previously used as tests for blind surveys. Experimental results with injected signals show that either the number of false detections is considerably reduced or faint exoplanets that would otherwise not be detected can be sometimes found. Typically, on the blind tests performed, we are now able to detect around 50% more of the injected planets with an SNR below 5, and with a very low number of additional candidates.
ExoDNN: Boosting exoplanet detection with artificial intelligence. Application to Gaia Data Release 3
ATG Science & Engineering for the European Space Agency (ESA)（欧洲航天局工程与技术科学部）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Centro de Astrobiología (CAB, CSIC-INTA)（天体生物学中心（CSIC-INTA））；ISDEFE；European Space Agency (ESA), European Space Astronomy Centre (ESAC)（欧洲航天局欧洲空间天文中心）；European Space Agency (ESA), European Space Research and Technology Centre (ESTEC)（欧洲航天局欧洲空间研究与技术中心）
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）
Department of Physics, University of Warwick（沃里克大学物理系）；Centre for Exoplanets and Habitability, University of Warwick（沃里克大学系外行星与宜居性中心）；Instituto de Ciencias Físicas (CONICET / ECyT-UNSAM)（物理科学研究所（CONICET / ECyT-UNSAM））；Gerencia de Tecnología de la información y de las Comunicaciones (GTIC), Subgerencia Vinculación y Desarrollo de Nuevas Tecnologías de la Información, DTE-CNEA. Centro Atómico Constituyentes（信息与通信技术管理局（GTIC），技术联络与发展新技术子部门，DTE-CNEA原子构成中心）；Instituto Tecnológico de Buenos Aires (ITBA)（布宜诺斯艾利斯技术研究所）
