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：现有系外行星大多通过验证技术发现，而非依靠互补观测确认。这类技术会生成一个分数，通常代表在给定与凌星信号相关的某类信息（以x表示）时，该信号为系外行星的概率（y(x)=系外行星）。除Rowe等人（2014）利用重数信息生成概率分数的验证技术外，现有验证方法均忽略了重数增强信息。本研究提出一个基于以下前提的框架：给定现有凌星信号筛选器（分类器），利用重数信息提升其性能。我们将此框架应用于多个现有分类器，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），以论证该框架能有效提升给定分类器的性能。我们进而将所提重数增强框架应用于ExoMiner V1.2（该版本修正了原始ExoMiner分类器（Valizadegan等人，2022）的某些缺陷），并依据开普勒星表验证了69颗位于含多个KOI系统中的新系外行星。