NEMESIS：系外行星Transit对TESS FFI中邻近M矮星的巡天I
NEMESIS: Exoplanet Transit Survey of Nearby M-Dwarfs in TESS FFIs I
摘要：
在这项工作中，我们分析了位于100秒差距内的33，054颗M矮星，这些M矮星位于观测扇区1至5的凌日系外行星调查卫星（TESS）全帧图像（FFI）中。我们提出了一个新的管道称为NEMESIS，它被开发用于提取去趋势测光，并在TESS FFI中执行单扇区数据的过境搜索。由于许多M矮星是暗淡的，并且没有被TESS以2分钟的节奏观测到，FFI凌日调查可以通过使用30分钟的节奏数据来验证有多少行星被错过。在这项工作中，我们发现了183个阈值交叉事件，并提出了29个行星候选人的部门1至5，其中24个是新的检测。我们的样本包含轨道周期从1.25到6.84天，行星半径从1.26到5.31地球半径。加上我们新的行星候选者探测以及之前在扇区1至5中观测到的探测，我们计算出每颗恒星在[1，9]天之间的周期范围和[0.5，11]地球半径之间的行星半径范围内的综合出现率为2.49 +/- 1.58颗行星。我们预计附近的M-矮星的估计产量为122 +/- 11过境检测。我们的新候选者中有23个具有信噪比> 7，透射光谱比> 38和发射光谱比> 10。我们通过位于https://filtergraph.com/NEMESIS的Filtergraph数据可视化服务为我们的行星候选者提供所有数据产品。
英文摘要：
In this work, we present the analysis of 33,054 M-dwarf stars located within 100 parsecs in the Transiting Exoplanet Survey Satellite (TESS) Full Frame Images (FFIs) of the observed sectors 1 to 5. We present a new pipeline called NEMESIS which was developed to extract detrended photometry and perform transit searches of single sector data in TESS FFIs. As many M-dwarfs are faint and are not observed with a 2 minute cadence by TESS, FFI transit surveys can give an empirical validation of how many planets are missed by using the 30 minute cadence data. In this work, we detected 183 threshold crossing events and present 29 planet candidates for sectors 1 to 5, 24 of which are new detections. Our sample contains orbital periods ranging from 1.25 to 6.84 days and planetary radii from 1.26 to 5.31 Earth radii. With the addition of our new planet candidate detections along with previous detections observed in sectors 1 to 5, we calculate an integrated occurrence rate of 2.49 +/- 1.58 planets per star for the period range between [1,9] days and planet radius range between [0.5,11] Earth radii. We project an estimated yield of 122 +/- 11 transit detections of nearby M-dwarfs. 23 of our new candidates have Signal to Noise ratios > 7, Transmission Spectroscopy Metrics > 38 and Emission Spectroscopy Metrics > 10. We provide all of our data products for our planet candidates through the Filtergraph data visualization service located at https://filtergraph.com/NEMESIS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
