The positional probability and true host star identification of TESS exoplanet candidates
The positional probability and true host star identification of TESS exoplanet candidates
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TESS系外行星候选者的位置概率和真实主星识别
DOI：
10.1093/mnras/stad3286
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发表时间：
2024
影响因子：
4.8
通讯作者：
Hadjigeorghiou A
中科院分区：
文献类型：
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作者：
Hadjigeorghiou A
我们提出了一种方法，以得出一个概率估计的真正来源的探测testransiting事件。我们的方法依赖于将观测到的目标恒星的光度质心偏移量与如果事件发生在目标上或任何gaiaii确定的附近源上将发生的偏移量模型进行比较。比较是按概率进行的，允许我们将观测到的和模拟的偏移量的不确定性纳入我们的结果。该方法是由tessscience处理操作中心（SPOC）管道生成的forTESSFull Frame Image光曲线开发的，但可以很容易地适应来自其他来源的光曲线。我们将该方法应用于3226个感兴趣的tessobjects of Interest (toi)，并使用了spoc释放的光曲线。该方法正确地识别出655颗已知系外行星中96.5%的宿主是最有可能的日食来源。对于142个已确认的近食双星（neb）和近候选行星（npc），在96.5%的情况下，发现近源是最有可能的。对于已知真实来源的40个neb和npc，其中38个被正确指定为最有可能的。最后，对于2365颗活跃的候选行星，该方法表明2072颗最有可能在目标上，293颗在附近的来源上。该方法构成了开发中的审查和验证管道的一部分，称为draven，并作为独立工具发布。
We present a method for deriving a probabilistic estimate of the true source of a detectedTESStransiting event. Our method relies on comparing the observed photometric centroid offset for the target star with models of the offset that would occur if the event was either on the target or any of theGaiaidentified nearby sources. The comparison is done probabilistically, allowing us to incorporate the uncertainties of the observed and modelled offsets in our result. The method was developed forTESSFull Frame Image light curves produced from theTESSScience Processing Operations Center (SPOC) pipeline, but could be easily adapted to light curves from other sources. We applied the method on 3226TESSObjects of Interest (TOIs), with a released light curve fromSPOC. The method correctly identified 96.5 per cent of 655 known exoplanet hosts as the most likely source of the eclipse. For 142 confirmed Nearby Eclipsing Binaries (NEBs) and Nearby Planet Candidates (NPCs), a nearby source was found to be the most likely in 96.5 per cent of the cases. For 40 NEBs and NPCs where the true source is known, it was correctly designated as the most likely in 38 of those. Finally, for 2365 active planet candidates, the method suggests that 2072 are most likely on-target and 293 on a nearby source. The method forms a part of an in-development vetting and validation pipeline, calledRAVEN, and is released as a standalone tool.