Publications of the Astronomical Society of the Pacific· 2012Q1

<i>Kepler</i>Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction

Jeffrey C. Smith, Martin C. Stumpe, Jeffrey E. Van Cleve, Jon M. Jenkins et al.

1,030citations
Q1SCImago
2012year

Short summary

A new Bayesian Maximum A Posteriori (MAP) approach effectively removes systematic errors from Kepler photometric data while preserving astrophysical signals, outperforming standard cotrending methods.

AI-generated from the title and abstract; the full text is not read.

TakeawaysIn the app

Key pointsIn the app

Ask the paperIn the app

The rest is in the Pofolia app

Takeaways, key points and questions to the paper; new summaries every day for your field. Free.

Get it on Google Play Download on the App Store

Field: Signal Processing

Signal ProcessingComputer Science