Most existing exoplanets are discovered using validation techniques rather than being confirmed by complementary observations. These techniques generate a score that is typically the probability of the transit signal being an exoplanet (y(x)=exoplanet) given some information related to that signal (represented by x). Except for the validation technique in Rowe et al. (2014) that uses multiplicity information to generate these probability scores, the existing validation techniques ignore the multiplicity boost information. In this work, we introduce a framework with the following premise: given an existing transit signal vetter (classifier), improve its performance using multiplicity information. We apply this framework to several existing classifiers, which include vespa (Morton et al. 2016), Robovetter (Coughlin et al. 2017), AstroNet (Shallue & Vanderburg 2018), ExoNet (Ansdel et al. 2018), GPC and RFC (Armstrong et al. 2020), and ExoMiner (Valizadegan et al. 2022), to support our claim that this framework is able to improve the performance of a given classifier. We then use the proposed multiplicity boost framework for ExoMiner V1.2, which addresses some of the shortcomings of the original ExoMiner classifier (Valizadegan et al. 2022), and validate 69 new exoplanets for systems with multiple KOIs from the Kepler catalog.

翻译：现有大部分系外行星通过验证技术发现，而非经由补充观测确认。这些技术根据信号相关信息（以x表示）生成一个分数，通常代表该凌星信号是系外行星的概率（y(x)=系外行星）。除Rowe等人（2014）利用多重性信息生成此类概率分数的验证技术外，现有验证方法均忽略多重性增强信息。本研究提出一个基于以下前提的框架：给定现有凌星信号审核器（分类器），利用多重性信息提升其性能。我们将该框架应用于多个现分类器，包括vespa（Morton等人，2016）、Robovetter（Coughlin等人，2017）、AstroNet（Shallue & Vanderburg，2018）、ExoNet（Ansdel等人，2018）、GPC与RFC（Armstrong等人，2020）以及ExoMiner（Valizadegan等人，2022），以证明该框架能够提升给定分类器的性能。随后，我们将提出的多重性增强框架应用于ExoMiner V1.2（该版本解决了原始ExoMiner分类器的部分缺陷，Valizadegan等人，2022），并从开普勒星表中对具有多个KOI的系统验证了69颗新系外行星。