A Paradigm Shift in Exoplanet False Positive Identification with HIP-44302

TL;DR

This paper presents a new comprehensive approach combining imaging, photometry, transits, and modeling to accurately identify false positives in exoplanet detection, exemplified by the reclassification of HIP-44302 as a triple-star system.

Contribution

It introduces a novel paradigm integrating multiple data sources and modeling techniques within EXOFASTv2 to definitively distinguish false positives from genuine exoplanets.

Findings

HIP-44302 is a false positive triple-star system.

The system consists of a binary orbiting a single star at 0.2946'' separation.

The new approach effectively rules out planetary scenarios for complex systems.

Abstract

Through detailed modeling of all three stars, we show that HIP-44302 is a false positive triple-star system. While the Transiting Exoplanet Survey Satellite initially designated the object as a planetary candidate, observing a 10-day transit and secondary eclipse in Sectors 8 and 35, we definitively exclude this scenario, finding instead that the transit comes from eclipsing stellar companions with $P = 9.599214$ days and $a = 0.1415$ AU. This binary orbits a single star at a wide separation of $\rho = 0.2946''$, determined through high-resolution AO and speckle imaging and corresponding to a $\sim$297 AU, $\sim$1886-year orbit at a distance of 1010 pc. Using transit data and photometry, we use EXOFASTv2 to fit the transit light curve, spectral energy distribution, and MIST evolutionary models of the three stars. We find that the isolated star and the larger binary star are massive A stars, with the single star having evolved off the main sequence, while the smaller companion is a G star. We also found an unusually low rotational velocity of 2.3 km/s from the single A star, observed from MINERVA and TRES RVs, implying a pole-on orientation. The triple-star architecture derived from multiple data sources made this target a complex system that required new capabilities within EXOFASTv2 to properly model. Our successful modeling demonstrates a new paradigm for false positive identification and classification that incorporates imaging, photometry, transits, and global modeling to definitively rule out false positives that may otherwise pollute candidate catalogs without extensive follow-up observations.