Multiplicity Boost Of Transit Signal Classifiers: Validation of 69 New Exoplanets Using The Multiplicity Boost of ExoMiner
arXiv:2305.02470 · doi:10.3847/1538-3881/acd344
Abstract
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.
arXiv commentThe paper is accepted for publication in the Astronomical Journal in April 27th, 2023
References in corpus (30)
- Gaia Early Data Release 3: The astrometric solution
- Kepler Input Catalog: Photometric Calibration and Stellar Classification
- Architecture of Kepler's Multi-transiting Systems: II. New investigations with twice as many candidates
- Validation of Kepler's Multiple Planet Candidates. III: Light Curve Analysis & Announcement of Hundreds of New Multi-planet Systems
- Revised Stellar Properties of Kepler Targets for the Quarter 1-16 Transit Detection Run
- False positive probabilties for all Kepler Objects of Interest: 1284 newly validated planets and 428 likely false positives
- Planetary Candidates Observed by Kepler. VIII. A Fully Automated Catalog With Measured Completeness and Reliability Based on Data Release 25
- Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90
- Revised Radii of Kepler Stars and Planets Using Gaia Data Release 2
- Kepler Data Validation I -- Architecture, Diagnostic Tests, and Data Products for Vetting Transiting Planet Candidates
- Almost All of Kepler's Multiple Planet Candidates are Planets
- The Derivation, Properties and Value of Kepler's Combined Differential Photometric Precision
- An Efficient Automated Validation Procedure for Exoplanet Transit Candidates
- Planetary Candidates Observed by Kepler. VII. The First Fully Uniform Catalog Based on The Entire 48 Month Dataset (Q1-Q17 DR24)
- Validation of Kepler's Multiple Planet Candidates. II: Refined Statistical Framework and Descriptions of Systems of Special Interest
- A First Comparison of Kepler Planet Candidates in Single and Multiple Systems
- Identification of Background False Positives from Kepler Data
- Automatic Classification of Kepler Planetary Transit Candidates
- Detection of Potential Transit Signals in 17 Quarters of Kepler Data: Results of the Final Kepler Mission Transiting Planet Search (DR25)
- Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning
- Diffuser-Assisted Infrared Transit Photometry for Four Dynamically Interacting \textit{Kepler} Systems
- ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier that Validates 301 New Exoplanets
- SOPHIE velocimetry of Kepler transit candidates. VIII. KOI-205 b: a brown-dwarf companion to a K-type dwarf
- SOPHIE velocimetry of Kepler transit candidates. XIII. KOI-189 B and KOI-686 B: two very low-mass stars in long-period orbits
- Transit Shapes and Self Organising Maps as a Tool for Ranking Planetary Candidates: Application to Kepler and K2
- Exoplanet Validation with Machine Learning: 50 new validated Kepler planets
- Hidden Worlds: Dynamical Architecture Predictions of Undetected Planets in Multi-planet Systems and Applications to TESS Systems
- Following up the Kepler field: Masses of Targets for transit timing and atmospheric characterization
- Revisiting the Architecture of the KOI-89 System
- Revisiting Kepler Transiting Systems: Unvetting Planets and Constraining Relationships among Harmonics in Phase Curves
Cited by in corpus (7)
- TESS Investigation -- Demographics of Young Exoplanets (TI-DYE) II: a second giant planet in the 17-Myr system HIP 67522
- Architecture Classification for Extrasolar Planetary Systems
- exoMMR: a New Python Package to Confirm and Characterize Mean Motion Resonances
- A joint effort to discover and characterize two resonant mini Neptunes around TOI-1803 with TESS, HARPS-N and CHEOPS
- DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates
- An Independent Search for Small Long-period Planets in Kepler Data I: Detection Pipeline
- Simulation of the spin evolution of some selected exoplanets and inferences on their climate