Publication

Effects of noise in different approaches for the statistics of polariton condensates

Paolo Schwendimann, Antonio Quattropani, Davide Sarchi
2008
Journal Articles

Abstract

The second-order correlation function of the polariton condensate is calculated within two different approaches. Both approaches qualitatively reproduce the deviations from the full coherence of the second-order correlation function that have been observed in the experiments. However, a quantitative difference in the magnitude of the predicted deviations is found. This difference originates in the modeling of the polariton dynamics in the two approaches.

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https://infoscience.epfl.ch/entities/publication/f32ec860-9665-43d7-bc90-81b34a63b128

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Paolo Schwendimann, Antonio Quattropani, Davide Sarchi
2008
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/f32ec860-9665-43d7-bc90-81b34a63b128

About this result

Related concepts (11)

Least absolute deviations

Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L1 norm of such values. It is analogous to the least squares technique, except that it is based on absolute values instead of squared values.

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The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. The RMSD represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences. These deviations are called residuals when the calculations are performed over the data sample that was used for estimation and are called errors (or prediction errors) when computed out-of-sample.

Reduced chi-squared statistic

In statistics, the reduced chi-square statistic is used extensively in goodness of fit testing. It is also known as mean squared weighted deviation (MSWD) in isotopic dating and variance of unit weight in the context of weighted least squares. Its square root is called regression standard error, standard error of the regression, or standard error of the equation (see ) It is defined as chi-square per degree of freedom: where the chi-squared is a weighted sum of squared deviations: with inputs: variance , observations O, and calculated data C.

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