2003
DOI: 10.1046/j.1365-8711.2003.06633.x
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Cross-correlation and maximum-likelihood analysis: a new approach to combining cross-correlation functions

Abstract: This paper presents a new approach to combining cross‐correlation functions. The combination is based on a maximum‐likelihood approach and uses a non‐linear combination scheme. It can be effective for radial velocity analysis of multi‐order spectra, or for analysis of multiple exposures of the same object. Simulations are presented to show the potential of the suggested combination scheme. The technique has already been used to detect a very faint companion of HD 41004.

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Cited by 149 publications
(169 citation statements)
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“…According to the Appendix in Zucker et al (1995), the error estimate of the component RVs derived from multi-dimension cross-correlation techniques can be obtained from a one dimensional (1D) error analysis carried out on each dimension. In this version of the program, we therefore applied a relation deduced by Zucker (2003) from the maximum-likelihood theory that provides reliable estimates of the uncertainty as long as the errors are dominated by random processes.…”
Section: Hd 93146 Amentioning
confidence: 99%
“…According to the Appendix in Zucker et al (1995), the error estimate of the component RVs derived from multi-dimension cross-correlation techniques can be obtained from a one dimensional (1D) error analysis carried out on each dimension. In this version of the program, we therefore applied a relation deduced by Zucker (2003) from the maximum-likelihood theory that provides reliable estimates of the uncertainty as long as the errors are dominated by random processes.…”
Section: Hd 93146 Amentioning
confidence: 99%
“…We refer to it hereafter as the standard method. Its last significant reformulation is from Zucker (2003), who derives it from a maximum likelihood argument that enables him to provide a consistent estimate for the actual random error in a given measurement. He also shows how to combine the information from different orders of a spectrum without merging them.…”
Section: Historical Backgroundmentioning
confidence: 99%
“…the orders of an echelle spectrogram, different CCDs (as in the case of Gaia) or disjoint wavelength regions (e.g. to eliminate interstellar or atmospheric spectral features); afterwards these C-functions can be averaged (Zucker 2003) or, for those of Sect. 2.3.3, summed.…”
Section: Observed Fluxmentioning
confidence: 99%
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“…Indeed, the cross-correlation function is calculated between the position spectrum and the derivative of the intensity spectrum. The mathematical evaluation of the cross-correlation errors has been discussed by Zucker (2003) who proposes using a likelihood estimator. This approach to estimating the errors was studied in our simulations.…”
Section: Cross-correlation Extractionmentioning
confidence: 99%