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 Curé

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The 4 yr 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. 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, and 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 data set; 1004 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 of 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.

Original languageEnglish (US)
Article number58
JournalAstronomical Journal
Volume158
Issue number2
DOIs
StatePublished - Jan 1 2019

Fingerprint

Kepler mission
planets
planet
transit
light curve
stars
time series analysis
false alarms
classifiers
dip
brightness
recovery
methodology
filter
filters
orbitals
radii
output

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

Caceres, G. A., Feigelson, E. D., Babu, G. J., Bahamonde, N., Christen, A., Bertin, K., ... Curé, M. (2019). Autoregressive Planet Search: Application to the Kepler Mission. Astronomical Journal, 158(2), [58]. https://doi.org/10.3847/1538-3881/ab26ba
Caceres, Gabriel A. ; Feigelson, Eric D. ; Babu, G. Jogesh ; Bahamonde, Natalia ; Christen, Alejandra ; Bertin, Karine ; Meza, Cristian ; Curé, Michel. / Autoregressive Planet Search : Application to the Kepler Mission. In: Astronomical Journal. 2019 ; Vol. 158, No. 2.
@article{f6ec9e49d70f4050855e007d708cc9e4,
title = "Autoregressive Planet Search: Application to the Kepler Mission",
abstract = "The 4 yr 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. 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, and 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 data set; 1004 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 of 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.",
author = "Caceres, {Gabriel A.} and Feigelson, {Eric D.} and Babu, {G. Jogesh} and Natalia Bahamonde and Alejandra Christen and Karine Bertin and Cristian Meza and Michel Cur{\'e}",
year = "2019",
month = "1",
day = "1",
doi = "10.3847/1538-3881/ab26ba",
language = "English (US)",
volume = "158",
journal = "Astronomical Journal",
issn = "0004-6256",
publisher = "IOP Publishing Ltd.",
number = "2",

}

Caceres, GA, Feigelson, ED, Babu, GJ, Bahamonde, N, Christen, A, Bertin, K, Meza, C & Curé, M 2019, 'Autoregressive Planet Search: Application to the Kepler Mission', Astronomical Journal, vol. 158, no. 2, 58. https://doi.org/10.3847/1538-3881/ab26ba

Autoregressive Planet Search : Application to the Kepler Mission. / Caceres, Gabriel A.; Feigelson, Eric D.; Babu, G. Jogesh; Bahamonde, Natalia; Christen, Alejandra; Bertin, Karine; Meza, Cristian; Curé, Michel.

In: Astronomical Journal, Vol. 158, No. 2, 58, 01.01.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Autoregressive Planet Search

T2 - Application to the Kepler Mission

AU - Caceres, Gabriel A.

AU - Feigelson, Eric D.

AU - Babu, G. Jogesh

AU - Bahamonde, Natalia

AU - Christen, Alejandra

AU - Bertin, Karine

AU - Meza, Cristian

AU - Curé, Michel

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The 4 yr 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. 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, and 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 data set; 1004 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 of 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.

AB - The 4 yr 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. 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, and 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 data set; 1004 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 of 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.

UR - http://www.scopus.com/inward/record.url?scp=85072025530&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85072025530&partnerID=8YFLogxK

U2 - 10.3847/1538-3881/ab26ba

DO - 10.3847/1538-3881/ab26ba

M3 - Article

AN - SCOPUS:85072025530

VL - 158

JO - Astronomical Journal

JF - Astronomical Journal

SN - 0004-6256

IS - 2

M1 - 58

ER -