Year: 2019
Type: article
Abstract: The detection of periodic signals from transiting exoplanets is often impeded by extraneous aperiodic photometric variability, either intrinsic to the star or arising from the measurement process. Frequently, these variations are autocorrelated wherein later flux values are correlated with previous ones. In this work, we present the methodology of the autoregessive planet search (ARPS) project, which uses the autoregressive integrated moving average (ARIMA) and related statistical models that treat a wide variety of stochastic processes, as well as nonstationarity, to improve detection of new planetary transits. Provided a time series is evenly spaced or can be placed on an evenly spaced grid with missing values, these low-dimensional parametric models can prove very effective. We introduce a planet search algorithm to detect periodic transits in the residuals after the application of ARIMA models. Our matched-filter algorithm, the transit comb filter (TCF), is closely related to the traditional box-fitting least-squares and provides an analogous periodogram. Finally, if a previously identified or simulated sample of planets is available, selected scalar features from different stages of the analysis—the original light curves, ARIMA fits, TCF periodograms, and folded light curves—can be collectively used with a multivariate classifier to identify promising candidates while efficiently rejecting false alarms. We use Random Forests for this task, in conjunction with receiver operating characteristic curves, to define discovery criteria for new, high-fidelity planetary candidates. The ARPS methodology can be applied to both evenly spaced satellite light curves and densely cadenced ground-based photometric surveys.
Source: The Astronomical Journal
Authors: Gabriel A. Caceres, Eric D. Feigelson, G. Jogesh Babu, Natalia Bahamonde, Alejandra Christen, Karine Bertin, Cristian Meza, Michel Curé
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Awards: Advanced Statistics in the Search for Planets, NASA'S KEPLER MISSION IS THE SOURCE OF MORE EXOPLANETS THAN ANY OTHER INSTRUMENT BUT THE DISCOVERY DEPENDS ON COMPLEX STATISTICAL ANALYSIS PROCEDURES EMBEDDED IN THE KEPLER PIPELINE. A PARTICULAR CHALLENGE IS MITIGATING IRREGULAR STELLAR VARIABILITY WITHOUT LOSS OF SENSITIVITY TO FAINT PERIODIC PLANETARY TRANSITS. THIS PROPOSAL PRESENTS A TWO-STAGE ALTERNATIVE ANALYSIS PROCEDURE. FIRST PARAMETRIC AUTOREGRESSIVE ARFIMA MODELS COMMONLY USED IN ECONOMETRICS REMOVE MOST OF THE STELLAR VARIATIONS. SECOND A NOVEL MATCHED FILTER IS USED TO CREATE A PERIODOGRAM FROM WHICH TRANSIT-LIKE PERIODICITIES ARE IDENTIFIED. THIS ANALYSIS PROCEDURE THE KEPLER AUTOREGRESSIVE PLANET SEARCH (KARPS) IS ING MOST OF THE KEPLER OBJECTS OF INTEREST AND IS EXPECTED TO IDENTIFY ADDITIONAL PLANETARY CANDIDATES. THE PROPOSED RESEARCH WILL COMPLETE APPLICATION OF THE KARPS METHODOLOGY TO THE PRIME KEPLER MISSION LIGHT CURVES OF 200 000: STARS AND COMPARE THE RESULTS WITH KEPLER OBJECTS OF INTEREST OBTAINED WITH THE KEPLER PIPELINE. WE WILL THEN CONDUCT A VARIETY OF ASTRONOMICAL STUDIES BASED ON THE KARPS RESULTS. IMPORTANT SUBSAMPLES WILL BE EXTRACTED INCLUDING HABITABLE ZONE PLANETS HOT SUPER-EARTHS GRAZING-TRANSIT HOT JUPITERS AND MULTI-PLANET SYSTEMS. GROUND-BASED SPECTROSCOPY OF POORLY STUDIED CANDIDATES WILL BE PERFORMED TO BETTER CHARACTERIZE THE HOST STARS. STUDIES OF STELLAR VARIABILITY WILL THEN BE PURSUED BASED ON KARPS ANALYSIS. THE AUTOCORRELATION FUNCTION AND NONSTATIONARITY MEASURES WILL BE USED TO IDENTIFY SPOTTED STARS AT DIFFERENT STAGES OF AUTOREGRESSIVE MODELING. PERIODIC VARIABLES WITH FOLDED LIGHT CURVES INCONSISTENT WITH PLANETARY TRANSITS WILL BE IDENTIFIED THEY MAY BE ECLIPSING OR MUTUALLY-ILLUMINATING BINARY STAR SYSTEMS. CLASSIFICATION OF STELLAR VARIABLES WITH KARPS-DERIVED STATISTICAL PROPERTIES WILL BE ATTEMPTED. KARPS PROCEDURES WILL THEN BE APPLIED TO ARCHIVED K2 DATA TO IDENTIFY PLANETARY TRANSITS AND CHARACTERIZE STELLAR VARIABILITY.
OpenAlex ID: W2908886259