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A graph showing variation of quantum efficiency with wavelength of a CCD chip from Wide Field and Planetary Camera 2, formerly installed on the Hubble Space Telescope.. The term quantum efficiency (QE) may apply to incident photon to converted electron (IPCE) ratio [1] of a photosensitive device, or it may refer to the TMR effect of a magnetic tunnel junction.
For example, in the case of beam deflection problems it is wise to use a deformed shape that is analytically similar to the expected solution. A quartic may fit most of the easy problems of simply linked beams even if the order of the deformed solution may be lower.
The DQE is generally expressed in terms of Fourier-based spatial frequencies as: [10] = = ()where u is the spatial frequency variable in cycles per millimeter, q is the density of incident x-ray quanta in quanta per square millimeter, G is the system gain relating q to the output signal for a linear and offset-corrected detector, T(u) is the system modulation transfer function, and W(u) is the ...
Given two (usually independent) random variables X and Y, the distribution of the random variable Z that is formed as the ratio Z = X/Y is a ratio distribution. An example is the Cauchy distribution (also called the normal ratio distribution), which comes about as the ratio of two normally distributed variables with zero mean.
To calculate the probability of any interactive process between electrons and photons, it is a matter of first noting, with Feynman diagrams, all the possible ways in which the process can be constructed from the three basic elements. Each diagram involves some calculation involving definite rules to find the associated probability amplitude.
As usual for monotonic relationships, the likelihood ratio's monotonicity comes in handy in statistics, particularly when using maximum-likelihood estimation. Also, distribution families with MLR have a number of well-behaved stochastic properties, such as first-order stochastic dominance and increasing hazard ratios. Unfortunately, as is also ...
The finite volume method (FVM) is a method for representing and evaluating partial differential equations in the form of algebraic equations. [1] In the finite volume method, volume integrals in a partial differential equation that contain a divergence term are converted to surface integrals, using the divergence theorem.
These quartiles are used to calculate the interquartile range, which helps to describe the spread of the data, and determine whether or not any data points are outliers. In order for these statistics to exist, the observations must be from a univariate variable that can be measured on an ordinal, interval or ratio scale .