Benchmark tests for Markov Chain Monte Carlo fitting of exoplanet eclipse observations

Justin Rogers, Mercedes López-Morales, Dániel Apai, Elisabeth Adams

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Ground-based observations of exoplanet eclipses provide important clues to the planets' atmospheric physics, yet systematics in light curve analyses are not fully understood. It is unknown if measurements suggesting near-infrared flux densities brighter than models predict are real, or artifacts of the analysis processes. We created a large suite of model light curves, using both synthetic and real noise, and tested the common process of light curve modeling and parameter optimization with a Markov Chain Monte Carlo algorithm. With synthetic white noise models, we find that input eclipse signals are generally recovered within 10% accuracy for eclipse depths greater than the noise amplitude, and to smaller depths for higher sampling rates and longer baselines. Red noise models see greater discrepancies between input and measured eclipse signals, often biased in one direction. Finally, we find that in real data, systematic biases result even with a complex model to account for trends, and significant false eclipse signals may appear in a non-Gaussian distribution. To quantify the bias and validate an eclipse measurement, we compare both the planet-hosting star and several of its neighbors to a separately chosen control sample of field stars. Re-examining the Rogers et al. Ks-band measurement of CoRoT-1b finds an eclipse 3190-440+370 ppm deep centered at φme = 0.50418-0.00203+0.00197. Finally, we provide and recommend the use of selected data sets we generated as a benchmark test for eclipse modeling and analysis routines, and propose criteria to verify eclipse detections.

Original languageEnglish (US)
Article number64
JournalAstrophysical Journal
Volume767
Issue number1
DOIs
StatePublished - Apr 10 2013

Keywords

  • binaries: eclipsing
  • methods: analytical
  • methods: statistical
  • planetary systems
  • stars: individual (CoRoT-1)
  • techniques: photometric

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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