The Transiting Exoplanet Survey Satellite (TESS), launched successfully on 18th of April, 2018, will observe nearly the full sky and will provide time-series imaging data in ~27-day-long campaigns. TESS is equipped with 4 cameras; each has a field-of-view of 24x24 degrees. During the first two years of the primary mission, one of these cameras, Camera #1, is going to observe fields centered at an ecliptic latitude of 18 degrees. While the ecliptic plane itself is not covered during the primary mission, the characteristic scale height of the main asteroid belt and Kuiper belt implies that a significant amount of small solar system bodies will cross the field-of-view of this camera. Based on the comparison of the expected amount of information of TESS and Kepler/K2, we can compute the cumulative etendues of the two optical setups. This comparison results in roughly comparable optical etendues, however the net etendue is significantly larger in the case of TESS since all of the imaging data provided by the 30-minute cadence frames are downlinked rather than the pre-selected stamps of Kepler/K2. In addition, many principles of the data acquisition and optical setup are clearly different, including the level of confusing background sources, full-frame integration and cadence, the field-of-view centroid with respect to the apparent position of the Sun, as well as the differences in the duration of the campaigns. As one would expect, TESS will yield time-series photometry and hence rotational properties for only brighter objects, but in terms of spatial and phase space coverage, this sample will be more homogeneous and more complete. Here we review the main analogues and differences between the Kepler/K2 mission and the TESS mission, focusing on scientific implications and possible yields related to our Solar System.
Deep Learning Classification in Asteroseismology Using an Improved Neural Network: Results on 15000 Kepler Red Giants and Applications to K2 and TESS Data
Towards Instrument-Agnostic Exoplanet Candidate Prioritization
Atholton High School（阿索尔顿高中）；Astrophysics Science Division, NASA Goddard Space Flight Center（NASA戈达德太空飞行中心天体物理学科学部）；Center for Space Science and Technology, University of Maryland Baltimore County（马里兰大学巴尔的摩县分校空间科学与技术中心）；Center for Research and Exploration in Space Science and Technology, NASA/GSFC（NASA/GSFC空间科学与工程探索研究中心）；SETI Institute（SETI研究所）；Department of Computer Sciences, University of Maryland Baltimore County（马里兰大学巴尔的摩县分校计算机科学系）；Department of Astronomy, University of Maryland College Park（马里兰大学帕克分校天文学系）
ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier
MIT Kavli Institute for Astrophysics & Space Research, Massachusetts Institute of Technology（麻省理工学院卡弗里天体物理与空间研究所）；Institute of Astronomy, KU Leuven（荷语鲁汶大学天文研究所）；Astronomical Institute of Czech Academy of Sciences（捷克科学院天文研究所）；Department of Physics, National University of Singapore（新加坡国立大学物理系）
Power-2 limb-darkening coefficients for the $uvby$, $UBVRIJHK$, SDSS $ugriz$, Gaia, Kepler, TESS, and CHEOPS photometric systems II. PHOENIX spherically symmetric stellar atmosphere models
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。
