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Title: OF 'COCKTAIL PARTIES' AND EXOPLANETS

Abstract

The characterization of ever smaller and fainter extrasolar planets requires an intricate understanding of one's data and the analysis techniques used. Correcting the raw data at the 10{sup -4} level of accuracy in flux is one of the central challenges. This can be difficult for instruments that do not feature a calibration plan for such high precision measurements. Here, it is not always obvious how to de-correlate the data using auxiliary information of the instrument and it becomes paramount to know how well one can disentangle instrument systematics from one's data, given nothing but the data themselves. We propose a non-parametric machine learning algorithm, based on the concept of independent component analysis, to de-convolve the systematic noise and all non-Gaussian signals from the desired astrophysical signal. Such a 'blind' signal de-mixing is commonly known as the 'Cocktail Party problem' in signal processing. Given multiple simultaneous observations of the same exoplanetary eclipse, as in the case of spectrophotometry, we show that we can often disentangle systematic noise from the original light-curve signal without the use of any complementary information of the instrument. In this paper, we explore these signal extraction techniques using simulated data and two data sets observed with themore » Hubble Space Telescope NICMOS instrument. Another important application is the de-correlation of the exoplanetary signal from time-correlated stellar variability. Using data obtained by the Kepler mission we show that the desired signal can be de-convolved from the stellar noise using a single time series spanning several eclipse events. Such non-parametric techniques can provide important confirmations of the existent parametric corrections reported in the literature, and their associated results. Additionally they can substantially improve the precision exoplanetary light-curve analysis in the future.« less

Authors:
 [1]
  1. University College London, Gower Street, WC1E 6BT (United Kingdom)
Publication Date:
OSTI Identifier:
22016337
Resource Type:
Journal Article
Journal Name:
Astrophysical Journal
Additional Journal Information:
Journal Volume: 747; Journal Issue: 1; Other Information: Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0004-637X
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; ASTROPHYSICS; CALIBRATION; COMPUTERIZED SIMULATION; CORRECTIONS; DATA ANALYSIS; ECLIPSE; PLANETS; SPECTROPHOTOMETRY; TELESCOPES

Citation Formats

Waldmann, I. P., E-mail: ingo@star.ucl.ac.uk. OF 'COCKTAIL PARTIES' AND EXOPLANETS. United States: N. p., 2012. Web. doi:10.1088/0004-637X/747/1/12.
Waldmann, I. P., E-mail: ingo@star.ucl.ac.uk. OF 'COCKTAIL PARTIES' AND EXOPLANETS. United States. https://doi.org/10.1088/0004-637X/747/1/12
Waldmann, I. P., E-mail: ingo@star.ucl.ac.uk. 2012. "OF 'COCKTAIL PARTIES' AND EXOPLANETS". United States. https://doi.org/10.1088/0004-637X/747/1/12.
@article{osti_22016337,
title = {OF 'COCKTAIL PARTIES' AND EXOPLANETS},
author = {Waldmann, I. P., E-mail: ingo@star.ucl.ac.uk},
abstractNote = {The characterization of ever smaller and fainter extrasolar planets requires an intricate understanding of one's data and the analysis techniques used. Correcting the raw data at the 10{sup -4} level of accuracy in flux is one of the central challenges. This can be difficult for instruments that do not feature a calibration plan for such high precision measurements. Here, it is not always obvious how to de-correlate the data using auxiliary information of the instrument and it becomes paramount to know how well one can disentangle instrument systematics from one's data, given nothing but the data themselves. We propose a non-parametric machine learning algorithm, based on the concept of independent component analysis, to de-convolve the systematic noise and all non-Gaussian signals from the desired astrophysical signal. Such a 'blind' signal de-mixing is commonly known as the 'Cocktail Party problem' in signal processing. Given multiple simultaneous observations of the same exoplanetary eclipse, as in the case of spectrophotometry, we show that we can often disentangle systematic noise from the original light-curve signal without the use of any complementary information of the instrument. In this paper, we explore these signal extraction techniques using simulated data and two data sets observed with the Hubble Space Telescope NICMOS instrument. Another important application is the de-correlation of the exoplanetary signal from time-correlated stellar variability. Using data obtained by the Kepler mission we show that the desired signal can be de-convolved from the stellar noise using a single time series spanning several eclipse events. Such non-parametric techniques can provide important confirmations of the existent parametric corrections reported in the literature, and their associated results. Additionally they can substantially improve the precision exoplanetary light-curve analysis in the future.},
doi = {10.1088/0004-637X/747/1/12},
url = {https://www.osti.gov/biblio/22016337}, journal = {Astrophysical Journal},
issn = {0004-637X},
number = 1,
volume = 747,
place = {United States},
year = {Thu Mar 01 00:00:00 EST 2012},
month = {Thu Mar 01 00:00:00 EST 2012}
}