Autoregressive Planet Search: Feasibility Study for Irregular Time Series

Andrew M. Stuhr, Eric D. Feigelson, Gabriel A. Caceres, Joel D. Hartman

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Sensitive signal processing methods are needed to detect transiting planets from ground-based photometric surveys. Caceres et al. show that the autoregressive planet search (ARPS) method - a combination of autoregressive integrated moving average (ARIMA) parametric modeling, a new transit comb filter (TCF) periodogram, and machine learning classification - is effective when applied to evenly spaced light curves from space-based missions. We investigate here whether ARIMA and TCF will be effective for ground-based survey light curves that are often sparsely sampled with high noise levels from atmospheric and instrumental conditions. The ARPS procedure is applied to selected light curves with strong planetary signals from the Kepler mission that have been altered to simulate the conditions of ground-based exoplanet surveys. Typical irregular cadence patterns are used from the Hungarian-made Automated Telescope Network-South (HATSouth) survey. We also evaluate recovery of known planets from HATSouth. Simulations test transit signal recovery as a function of cadence pattern and duration, stellar magnitude, planet orbital period, and transit depth. Detection rates improve for shorter periods and deeper transits. The study predicts that the ARPS methodology will detect planets with ⪆0.1% transit depth and periods ≲40 days in HATSouth stars brighter than ∼15 mag. ARPS methodology is therefore promising for planet discovery from ground-based exoplanet surveys with sufficiently dense cadence patterns.

Original languageEnglish (US)
Article number59
JournalAstronomical Journal
Volume158
Issue number2
DOIs
StatePublished - Jan 1 2019

Fingerprint

feasibility study
planets
planet
time series
transit
light curve
telescopes
extrasolar planets
stellar magnitude
recovery
Kepler mission
methodology
filter
filters
machine learning
signal processing
meteorology
stars
orbitals

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

Stuhr, Andrew M. ; Feigelson, Eric D. ; Caceres, Gabriel A. ; Hartman, Joel D. / Autoregressive Planet Search : Feasibility Study for Irregular Time Series. In: Astronomical Journal. 2019 ; Vol. 158, No. 2.
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Autoregressive Planet Search : Feasibility Study for Irregular Time Series. / Stuhr, Andrew M.; Feigelson, Eric D.; Caceres, Gabriel A.; Hartman, Joel D.

In: Astronomical Journal, Vol. 158, No. 2, 59, 01.01.2019.

Research output: Contribution to journalArticle

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