Open AccessDetection and Removal of Periodic Noise in Kepler/K2 Photometry with Principal Component Analysis
Author(s) -
Riley Clarke,
Federica Bianco,
John E. Gizis
Publication year - 2021
Publication title -
research notes of the aas
Language(s) - English
Resource type - Journals
ISSN - 2515-5172
DOI - 10.3847/2515-5172/ac179b
Subject(s) - principal component analysis , kepler , photometry (optics) , noise (video) , fourier transform , fourier analysis , signal (programming language) , fourier series , pattern recognition (psychology) , artificial intelligence , computer science , mathematics , computer vision , mathematical analysis , stars , image (mathematics) , programming language
We present a novel method for detrending systematic noise from time series data using Principal Component Analysis (PCA) in Fast Fourier Transforms. This method is demonstrated on time series data obtained from the inaugural campaign of the Kepler K2 mission, as well as three objects of interest from Campaign 4. Unlike previous detrending techniques that utilize PCA, this method performs the detrending in Fourier space rather than temporal space. The advantage of performing the analysis in frequency space is that the technique is sensitive purely to the periodicity of the unwanted signal and not to its morphological characteristics. This method could improve measurements of low signal-to-noise photometric features by reducing systematics. We also discuss challenges and limitations associated with this technique.