Bi-Gaussian fitting of skewed peaks

Buys, T. S.; Clerk, K. De

doi:10.1021/ac60315a005

Cited by 68 publications

(30 citation statements)

References 1 publication

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“…+32 novae per year, where the uncertainties are 1σ errors of an assumed bi-Gaussian distribution (Buys & De Clerk 1972). Since the local novae (m ∼ < 2) are predominately disk novae, an increase in the bulge-to-disk ratio has the effect of shifting the peak of the probability distribution to somewhat higher nova rates.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

The Galactic Nova Rate Revisited

Shafter

2017

ApJ

101

104

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Despite its fundamental importance, a reliable estimate of the Galactic nova rate has remained elusive. Here, the overall Galactic nova rate is estimated by extrapolating the observed rate for novae reaching m ≤ 2 to include the entire Galaxy using a two component disk plus bulge model for the distribution of stars in the Milky Way. The present analysis improves on previous work by considering important corrections for incompleteness in the observed rate of bright novae and by employing a Monte Carlo analysis to better estimate the uncertainty in the derived nova rates. Several models are considered to account for differences in the assumed properties of bulge and disk nova populations and in the absolute magnitude distribution. The simplest models, which assume uniform properties between bulge and disk novae, predict Galactic nova rates of ∼50 to in excess of 100 per year, depending on the assumed incompleteness at bright magnitudes. Models where the disk novae are assumed to be more luminous than bulge novae are explored, and predict nova rates up to 30% lower, in the range of ∼35 to ∼75 per year. An average of the most plausible models yields a rate of 50 +31 −23 yr −1 , which is arguably the best estimate currently available for the nova rate in the Galaxy. Virtually all models produce rates that represent significant increases over recent estimates, and bring the Galactic nova rate into better agreement with that expected based on comparison with the latest results from extragalactic surveys.

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Section: Monte Carlo Simulationsmentioning

confidence: 99%

The Galactic Nova Rate Revisited

Shafter

2017

ApJ

101

104

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“…Empirical models to characterize peaks have also been suggested in the literature. Some of these empirical models are bi‐Gaussian model , models of Torres Lapasio , Li , Chesler and Cram , Lan and Jorgenson , and Nikitas , Frazer‐Suzuki model , Gram‐Charlier series , Edgeworth‐Cramer series , and Pap‐Papai model . Although these various functions and empirical models may provide some improvement over Gaussian model in peak characterization for some of the unusually shaped peaks, these cannot be all‐encompassing for accurately characterizing the vast number of possible chromatographic peak shapes that result from various physical and chemical processes taking place inside the column.…”

Section: Introductionmentioning

confidence: 99%

The utility of statistical moments in chromatography using trapezoidal and Simpson's rules of peak integration

Misra

Wahab

Patel

et al. 2019

J of Separation Science

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Modern chromatographic data acquisition softwares often behave as black boxes where the researchers have little control over the raw data processing. One of the significant interests of separation scientists is to extract physico-chemical information from chromatographic experiments and peak parameters. In addition, column developers need the total peak shape analysis to characterize the flow profile in chromatographic beds. Statistical moments offer a robust approach for providing detailed information for peaks in terms of area, its center of gravity, variance, resolution, and its skew without assuming any peak model or shape. Despite their utility and theoretical significance, statistical moments are rarely incorporated as they often provide underestimated or overestimated results because of inappropriate choice of the integration method and selection of integration limits. The Gaussian model is universally used in most chromatography softwares to assess efficiency, resolution, and peak position. Herein we present a user-friendly, and accessible approach for calculating the zeroth, first, second, and third moments through more accurate numerical integration techniques (Trapezoidal and Simpson's rule) which provide an accurate estimate of peak parameters as compared to rectangular integration. An Excel template is also provided which can calculate the four moments in three steps with or without baseline correction.

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“…The LIF spectra is recorded as a multi-dimensional matrix of intensity and spectral parameters. Spectroscopy peaks are frequently observed to be asymmetric in which cases the peak maximum loses its significance and the calculation of peak area is also complicate [12]. The bi-Gaussian peak function was used for LIF spectral deconvolution to retrieve asymmetric approximations of the basic spectral components in this study.…”

Section: Lif Spectra Deconvolutionmentioning

confidence: 99%

Lidar monitoring of organic matter in the Pearl River Estuary

Chen

Pan

et al. 2014

SPIE Proceedings

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A dual-wavelength lidar fluorosensor system for fast diagnosis of chromophoric dissolved matter (CDOM) in water in the Pearl River estuary was discussed. The laser-induced fluorescence (LIF) system used two lasers as excitation sources with wavelength at 355nm and 532 nm, and a hyperspectral CCD spectrometer was used to record the fluorescence signal. The overlapping fluorescence spectra of water Raman scattering and CDOM were separated with fitting biGaussian of the least squares method. High correlation was observed between concentration of CDOM and fluorescence normalized to water Raman scattering. The in situ results demonstrated rapid characterization of dissolved organic matter can be done by the LIF technique.

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Bi-Gaussian fitting of skewed peaks

Cited by 68 publications

References 1 publication

The Galactic Nova Rate Revisited

The Galactic Nova Rate Revisited

The utility of statistical moments in chromatography using trapezoidal and Simpson's rules of peak integration

Lidar monitoring of organic matter in the Pearl River Estuary

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