# AutoRegressive Planet Search: Application to the Kepler Mission

## TL;DR

This paper applies the AutoRegressive Planet Search (ARPS) methodology to Kepler data, successfully identifying known and new exoplanet candidates with high accuracy using a three-stage analysis pipeline.

## Contribution

The paper demonstrates the effectiveness of ARPS in analyzing Kepler light curves, combining ARIMA modeling, periodogram analysis, and machine learning for exoplanet detection.

## Key findings

- Recovered 76% of confirmed planets, 97% with additional constraints
- Identified 97 new planet candidates after vetting
- Most candidates have periods less than 10 days, including ultra-short period hot planets

## Abstract

The 4-year light curves of 156,717 stars observed with NASA's Kepler mission are analyzed using the AutoRegressive Planet Search (ARPS) methodology described by Caceres et al. (2019). The three stages of processing are: maximum likelihood ARIMA modeling of the light curves to reduce stellar brightness variations; constructing the Transit Comb Filter periodogram to identify transit-like periodic dips in the ARIMA residuals; Random Forest classification trained on Kepler Team confirmed planets using several dozen features from the analysis. Orbital periods between 0.2 and 100 days are examined. The result is a recovery of 76% of confirmed planets, 97% when period and transit depth constraints are added. The classifier is then applied to the full Kepler dataset; 1,004 previously noticed and 97 new stars have light curve criteria consistent with the confirmed planets, after subjective vetting removes clear False Alarms and False Positive cases. The 97 Kepler ARPS Candidate Transits mostly have periods $P<10$ days; many are UltraShort Period hot planets with radii $<1$% of the host star. Extensive tabular and graphical output from the ARPS time series analysis is provided to assist in other research relating to the Kepler sample.