# Supervised Learning Detection of Sixty Non-Transiting Hot Jupiter Candidates

## TL;DR

This paper introduces a supervised learning algorithm to detect non-transiting hot Jupiters in Kepler data by analyzing optical phase curves, identifying 60 high-probability candidates for further confirmation and study.

## Contribution

The study develops a novel supervised learning method to find non-transiting hot Jupiters using phase curve data, expanding detection capabilities beyond transiting planets.

## Key findings

- Identified 60 high-probability non-transiting hot Jupiter candidates.
- Classified 142,630 Kepler stars for potential phase curve signals.
- Derived constraints on albedos and phase curve offsets for candidates.

## Abstract

The optical, full-phase photometric variations of a short-period planet provide a unique view of the planet's atmospheric composition and dynamics. The number of planets with optical phase curve detections, however, is currently too small to study them as an aggregate population, motivating an extension of the search to non-transiting planets. Here we present an algorithm for the detection of non-transiting, short-period giant planets in the Kepler field. The procedure uses the phase curves themselves as evidence for the planets' existence. We employ a supervised learning algorithm to recognize the salient time-dependent properties of synthetic phase curves; we then search for detections of signals that match these properties. After demonstrating the algorithm's capabilities, we classify 142,630 FGK Kepler stars without confirmed planets or KOIs and, for each one, assign a probability of a phase curve of a non-transiting planet being present. We identify 60 high-probability non-transiting hot Jupiter candidates. We also derive constraints on the candidates' albedos and offsets of the phase curve maxima. These targets are strong candidates for follow-up radial velocity confirmation and characterization. Once confirmed, the atmospheric information content in the phase curves may be studied in yet greater detail.