AutoRegressive Planet Search: Application to the Kepler Mission
Gabriel A. Caceres, Eric D. Feigelson, G. Jogesh Babu, Natalia, Bahamonde, Alejandra Christen, Karine Bertin, Cristian Meza, Michel Cur\'e
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.
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…
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.