Darwin–Fowler method

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In statistical mechanics, the Darwin–Fowler method is used for deriving the distribution functions with mean probability. It was developed by Charles Galton Darwin and Ralph H. Fowler in 1922-1923.[1][2]

Distribution functions are used in statistical physics to estimate the mean number of particles occupying an energy level (hence also called occupation numbers). These distributions are mostly derived as those numbers for which the system under consideration is in its state of maximum probability. But one really requires average numbers. These average numbers can be obtained by the Darwin–Fowler method. Of course, for systems in the thermodynamic limit (large number of particles), as in statistical mechanics, the results are the same as with maximization.

Darwin–Fowler methodEdit

In most texts on statistical mechanics the statistical distribution functions   in Maxwell–Boltzmann statistics, Bose–Einstein statistics, Fermi–Dirac statistics) are derived by determining those for which the system is in its state of maximum probability. But one really requires those with average or mean probability, although – of course – the results are usually the same for systems with a huge number of elements, as is the case in statistical mechanics. The method for deriving the distribution functions with mean probability has been developed by C. G. Darwin and Fowler[2] and is therefore known as the Darwin–Fowler method. This method is the most reliable general procedure for deriving statistical distribution functions. Since the method employs a selector variable (a factor introduced for each element to permit a counting procedure) the method is also known as the Darwin–Fowler method of selector variables. Note that a distribution function is not the same as the probability – cf. Maxwell–Boltzmann distribution, Bose–Einstein distribution, Fermi–Dirac distribution. Also note that the distribution function   which is a measure of the fraction of those states which are actually occupied by elements, is given by   or  , where   is the degeneracy of energy level   of energy   and   is the number of elements occupying this level (e.g. in Fermi-Dirac statistics 0 or 1). Total energy   and total number of elements   are then given by   and  .

The Darwin–Fowler method has been treated in the texts of E. Schrödinger,[3] Fowler[4] and Fowler and E.A. Guggenheim,[5] of K. Huang,[6] and of H.J.W. Müller–Kirsten.[7] The method is also discussed and used for the derivation of Bose–Einstein condensation in the book of R.B. Dingle [de].[8]

Classical statisticsEdit

For   independent elements with   on level with energy   and   for a canonical system in a heat bath with temperature   we set


The average over all arrangements is the mean occupation number


Insert a selector variable   by setting


In classical statistics the   elements are (a) distinguishable and can be arranged with packets of   elements on level   whose number is


so that in this case


Allowing for (b) the degeneracy   of level   this expression becomes


The selector variable   allows to pick out the coefficient of   which is  . Thus


and hence


This result which agrees with the most probable value obtained by maximization does not involve a single approximation and is therefore exact, and thus demonstrates the power of this Darwin-Fowler method.

Quantum statisticsEdit

We have as above


where   is the number of elements in energy level  . Since in quantum statistics elements are indistinguishable no preliminary calculation of the number of ways of dividing elements into packets   is required. Therefore the sum   refers only to the sum over possible values of  .

In the case of Fermi-Dirac statistics we have


per state. There are   states for energy level  . Hence we have


In the case of Bose-Einstein statistics we have


By the same procedure as before we obtain in the present case






Summarizing both cases and recalling the definition of  , we have that   is the coefficient of   in


where the upper signs apply to Fermi-Dirac statistics, and the lower signs to Bose-Einstein statistics.

Next we have to evaluate the coefficient of   in   In the case of a function   which can be expanded as


the coefficient of   is, with the help of the residue theorem of Cauchy,


We note that similarly the coefficient   in the above can be obtained as




Differentiating one obtains




One now evaluates the first and second derivatives of   at the stationary point   at which  . This method of evaluation of   around the saddle point  is known as the method of steepest descent. One then obtains


We have   and hence


(the +1 being negligible since   is large). We shall see in a moment that this last relation is simply the formula


We obtain the mean occupation number   by evaluating


This expression gives the mean number of elements of the total of   in the volume   which occupy at temperature   the 1-particle level   with degeneracy   (see e.g. a priori probability). For the relation to be reliable one should check that higher order contributions are initially decreasing in magnitude so that the expansion around the saddle point does indeed yield an asymptotic expansion.

Further readingEdit

  • Mehra, Jagdish; Schrödinger, Erwin; Rechenberg, Helmut (2000-12-28). The Historical Development of Quantum Theory. Springer Science & Business Media. ISBN 9780387951805.


  1. ^ "Darwin-Fowler method". Encyclopedia of Mathematics. Retrieved 2018-09-27.
  2. ^ a b C.G. Darwin and R.H. Fowler, Phil. Mag. 44(1922) 450–479, 823–842.
  3. ^ E. Schrödinger, Statistical Thermodynamics, Cambridge University Press (1952).
  4. ^ R.H. Fowler, Statistical Mechanics, Cambridge University Press (1952).
  5. ^ R.H. Fowler and E. Guggenheim, Statistical Thermodynamics, Cambridge University Press (1960).
  6. ^ K. Huang, Statistical Mechanics, Wiley (1963).
  7. ^ H.J.W. Müller–Kirsten, Basics of Statistical Physics, 2nd ed., World Scientific (2013), ISBN 978-981-4449-53-3.
  8. ^ R. B. Dingle, Asymptotic Expansions: Their Derivation and Interpretation, Academic Press (1973); pp. 267–271.