In quantum statistics, Bose– Einstein Einstein statistics (or more colloquially B–E statistics) is one of two possible ways in which a collection of non-interacting indistinguishable particles may occupy a set of available discrete energy states, at thermodynamic equilibrium. The aggregation of particles in the same state, which is a characteristic of particles obeying Bose– Einstein Einstein statistics, accounts for the cohesive streaming of laser light and the frictionless creeping of superfluid helium. The theory of this behaviour was developed (1924–25) by Satyendra Nath Bose, who recognized that a collection of identical and indistinguishable particles can be distributed in this way. The idea was later adopted and extended by Albert Einstein Einstein in collaboration with Bose. The Bose– Einstein Einstein statistics apply only to those particles not limited to single occupancy of the same state—that is, particles that do not obey the Pauli exclusion principle Pauli exclusion principle restrictions. Such particles have integer values of spin and are named bosons, after the statistics that correctly describe their behaviour. There must also be no significant interaction between the particles.Contents1 Concept 2 History 3 Two derivations of the Bose– Einstein Einstein distribution3.1 Derivation from the grand canonical ensemble 3.2 Derivation in the canonical approach4 Interdisciplinary applications 5 See also 6 Notes 7 ReferencesConcept At low temperatures, bosons behave differently from fermions (which obey the Fermi–Dirac statistics) in a way that an unlimited number of them can "condense" into the same energy state. This apparently unusual property also gives rise to the special state of matter – the Bose– Einstein Einstein condensate. Fermi–Dirac and Bose–Einstein statistics apply when quantum effects are important and the particles are "indistinguishable". Quantum effects appear if the concentration of particles satisfies N V ≥ n q , displaystyle frac N V geq n_ q , where N is the number of particles, V is the volume, and nq is the quantum concentration, for which the interparticle distance is equal to the thermal de Broglie wavelength, so that the wavefunctions of the particles are barely overlapping. Fermi–Dirac statistics Fermi–Dirac statistics apply to fermions (particles that obey the Pauli exclusion principle), and Bose– Einstein Einstein statistics apply to bosons. As the quantum concentration depends on temperature, most systems at high temperatures obey the classical (Maxwell–Boltzmann) limit, unless they also have a very high density, as for a white dwarf. Both Fermi–Dirac and Bose– Einstein Einstein become Maxwell–Boltzmann statistics Maxwell–Boltzmann statistics at high temperature or at low concentration. B–E statistics was introduced for photons in 1924 by Bose and generalized to atoms by Einstein Einstein in 1924–25. The expected number of particles in an energy state i for B–E statistics is n i ( ε i ) = g i e ( ε i − μ ) / k T − 1 , displaystyle n_ i (varepsilon _ i )= frac g_ i e^ (varepsilon _ i -mu )/kT -1 , with εi > μ and where ni is the number of particles in state i, gi is the degeneracy of energy level i, εi is the energy of the i-th state, μ is the chemical potential, k is the Boltzmann constant, and T is absolute temperature. For comparison, the average number of fermions with energy ϵ i displaystyle epsilon _ i given by Fermi–Dirac particle-energy distribution has a similar form: n ¯ i ( ϵ i ) = g i e ( ϵ i − μ ) / k T + 1 . displaystyle bar n _ i (epsilon _ i )= frac g_ i e^ (epsilon _ i -mu )/kT +1 . k T ≫ ϵ i − μ displaystyle kTgg epsilon _ i -mu , namely n i = g i k T ε i − μ . displaystyle n_ i = frac g_ i kT varepsilon _ i -mu . History While presenting a lecture at the University of Dhaka University of Dhaka (in what was then British India British India and now Bangladesh) on the theory of radiation and the ultraviolet catastrophe, Satyendra Nath Bose Satyendra Nath Bose intended to show his students that the contemporary theory was inadequate, because it predicted results not in accordance with experimental results. During this lecture, Bose committed an error in applying the theory, which unexpectedly gave a prediction that agreed with the experiment. The error was a simple mistake—similar to arguing that flipping two fair coins will produce two heads one-third of the time—that would appear obviously wrong to anyone with a basic understanding of statistics (remarkably, this error resembled the famous blunder by d'Alembert known from his "Croix ou Pile" article). However, the results it predicted agreed with experiment, and Bose realized it might not be a mistake after all. For the first time, he took the position that the Maxwell–Boltzmann distribution would not be true for all microscopic particles at all scales. Thus, he studied the probability of finding particles in various states in phase space, where each state is a little patch having volume h3, and the position and momentum of the particles are not kept particularly separate but are considered as one variable. Bose adapted this lecture into a short article called "Planck's Law and the Hypothesis of Light Quanta" and submitted it to the Philosophical Magazine. However, the referee's report was negative, and the paper was rejected. Undaunted, he sent the manuscript to Albert Einstein Einstein requesting publication in the Zeitschrift für Physik. Einstein Einstein immediately agreed, personally translated the article from English into German (Bose had earlier translated Einstein's article on the theory of General Relativity from German to English), and saw to it that it was published. Bose's theory achieved respect when Einstein sent his own paper in support of Bose's to Zeitschrift für Physik, asking that they be published together. This was done in 1924. The reason Bose produced accurate results was that since photons are indistinguishable from each other, one cannot treat any two photons having equal energy as being two distinct identifiable photons. By analogy, if in an alternate universe coins were to behave like photons and other bosons, the probability of producing two heads would indeed be one-third, and so is the probability of getting a head and a tail which equals one-half for the conventional (classical, distinguishable) coins. Bose's "error" leads to what is now called Bose– Einstein Einstein statistics. Bose and Einstein Einstein extended the idea to atoms and this led to the prediction of the existence of phenomena which became known as Bose– Einstein Einstein condensate, a dense collection of bosons (which are particles with integer spin, named after Bose), which was demonstrated to exist by experiment in 1995. Two derivations of the Bose– Einstein Einstein distribution Derivation from the grand canonical ensemble The Bose– Einstein Einstein distribution, which applies only to a quantum system of non-interacting bosons, is easily derived from the grand canonical ensemble. In this ensemble, the system is able to exchange energy and exchange particles with a reservoir (temperature T and chemical potential µ fixed by the reservoir). Due to the non-interacting quality, each available single-particle level (with energy level ϵ) forms a separate thermodynamic system in contact with the reservoir. In other words, each single-particle level is a separate, tiny grand canonical ensemble. With bosons there is no limit on the number of particles N in the level, but due to indistinguishability each possible N corresponds to only one microstate (with energy Nϵ). The resulting partition function for that single-particle level therefore forms a geometric series: Z = ∑ N = 0 ∞ exp ⁡ ( N ( μ − ϵ ) / k B T ) = ∑ N = 0 ∞ [ exp ⁡ ( ( μ − ϵ ) / k B T ) ] N = 1 1 − exp ⁡ ( ( μ − ϵ ) / k B T ) displaystyle begin aligned mathcal Z &=sum _ N=0 ^ infty exp(N(mu -epsilon )/k_ B T)=sum _ N=0 ^ infty [exp((mu -epsilon )/k_ B T)]^ N \&= frac 1 1-exp((mu -epsilon )/k_ B T) end aligned and the average particle number for that single-particle substate is given by ⟨ N ⟩ = k B T 1 Z ( ∂ Z ∂ μ ) V , T = 1 exp ⁡ ( ( ϵ − μ ) / k B T ) − 1 displaystyle langle Nrangle =k_ B T frac 1 mathcal Z left( frac partial mathcal Z partial mu right)_ V,T = frac 1 exp((epsilon -mu )/k_ B T)-1 This result applies for each single-particle level and thus forms the Bose– Einstein Einstein distribution for the entire state of the system. The variance in particle number (due to thermal fluctuations) may also be derived: ⟨ ( Δ N ) 2 ⟩ = k B T ( d ⟨ N ⟩ d μ ) V , T = ⟨ N 2 ⟩ − ⟨ N ⟩ 2 displaystyle langle (Delta N)^ 2 rangle =k_ B Tleft( frac dlangle Nrangle dmu right)_ V,T =langle N^ 2 rangle -langle Nrangle ^ 2 This level of fluctuation is much larger than for distinguishable particles, which would instead show Poisson statistics Poisson statistics ( ⟨ ( Δ N ) 2 ⟩ = ⟨ N ⟩ 2 displaystyle langle (Delta N)^ 2 rangle =langle Nrangle ^ 2 ). This is because the probability distribution for the number of bosons in a given energy level is a geometric distribution, not a Poisson distribution. Derivation in the canonical approach It is also possible to derive approximate Bose– Einstein Einstein statistics in the canonical ensemble. These derivations are lengthy and only yield the above results in the asymptotic limit of a large number of particles. The reason is that the total number of bosons is fixed in the canonical ensemble. The Bose– Einstein Einstein distribution in this case can be derived as in most texts by maximization, but the mathematically best derivation is by the Darwin–Fowler method of mean values as emphasized by Dingle. See also Müller-Kirsten. The fluctuations of the ground state in the condensed region are however markedly different in the canonical and grand-canonical ensembles.DerivationSuppose we have a number of energy levels, labeled by index i displaystyle displaystyle i , each level having energy ε i displaystyle displaystyle varepsilon _ i and containing a total of n i displaystyle displaystyle n_ i particles. Suppose each level contains g i displaystyle displaystyle g_ i distinct sublevels, all of which have the same energy, and which are distinguishable. For example, two particles may have different momenta, in which case they are distinguishable from each other, yet they can still have the same energy. The value of g i displaystyle displaystyle g_ i associated with level i displaystyle displaystyle i is called the "degeneracy" of that energy level. Any number of bosons can occupy the same sublevel. Let w ( n , g ) displaystyle displaystyle w(n,g) be the number of ways of distributing n displaystyle displaystyle n particles among the g displaystyle displaystyle g sublevels of an energy level. There is only one way of distributing n displaystyle displaystyle n particles with one sublevel, therefore w ( n , 1 ) = 1 displaystyle displaystyle w(n,1)=1 . It is easy to see that there are ( n + 1 ) displaystyle displaystyle (n+1) ways of distributing n displaystyle displaystyle n particles in two sublevels which we will write as: w ( n , 2 ) = ( n + 1 ) ! n ! 1 ! . displaystyle w(n,2)= frac (n+1)! n!1! . With a little thought (see Notes below) it can be seen that the number of ways of distributing n displaystyle displaystyle n particles in three sublevels is w ( n , 3 ) = w ( n , 2 ) + w ( n − 1 , 2 ) + ⋯ + w ( 1 , 2 ) + w ( 0 , 2 ) displaystyle w(n,3)=w(n,2)+w(n-1,2)+cdots +w(1,2)+w(0,2) so that w ( n , 3 ) = ∑ k = 0 n w ( n − k , 2 ) = ∑ k = 0 n ( n − k + 1 ) ! ( n − k ) ! 1 ! = ( n + 2 ) ! n ! 2 ! displaystyle w(n,3)=sum _ k=0 ^ n w(n-k,2)=sum _ k=0 ^ n frac (n-k+1)! (n-k)!1! = frac (n+2)! n!2! where we have used the following theorem involving binomial coefficients: ∑ k = 0 n ( k + a ) ! k ! a ! = ( n + a + 1 ) ! n ! ( a + 1 ) ! . displaystyle sum _ k=0 ^ n frac (k+a)! k!a! = frac (n+a+1)! n!(a+1)! . Continuing this process, we can see that w ( n , g ) displaystyle displaystyle w(n,g) is just a binomial coefficient (See Notes below) w ( n , g ) = ( n + g − 1 ) ! n ! ( g − 1 ) ! . displaystyle w(n,g)= frac (n+g-1)! n!(g-1)! . For example, the population numbers for two particles in three sublevels are 200, 110, 101, 020, 011, or 002 for a total of six which equals 4!/(2!2!). The number of ways that a set of occupation numbers n i displaystyle displaystyle n_ i can be realized is the product of the ways that each individual energy level can be populated: W = ∏ i w ( n i , g i ) = ∏ i ( n i + g i − 1 ) ! n i ! ( g i − 1 ) ! ≈ ∏ i ( n i + g i ) ! n i ! ( g i ) ! displaystyle W=prod _ i w(n_ i ,g_ i )=prod _ i frac (n_ i +g_ i -1)! n_ i !(g_ i -1)! approx prod _ i frac (n_ i +g_ i )! n_ i !(g_ i )! where the approximation assumes that n i ≫ 1 displaystyle n_ i gg 1 . Following the same procedure used in deriving the Maxwell–Boltzmann statistics, we wish to find the set of n i displaystyle displaystyle n_ i for which W is maximised, subject to the constraint that there be a fixed total number of particles, and a fixed total energy. The maxima of W displaystyle displaystyle W and ln ⁡ ( W ) displaystyle displaystyle ln(W) occur at the same value of n i displaystyle displaystyle n_ i and, since it is easier to accomplish mathematically, we will maximise the latter function instead. We constrain our solution using Lagrange multipliers Lagrange multipliers forming the function: f ( n i ) = ln ⁡ ( W ) + α ( N − ∑ n i ) + β ( E − ∑ n i ε i ) displaystyle f(n_ i )=ln(W)+alpha (N-sum n_ i )+beta (E-sum n_ i varepsilon _ i ) Using the n i ≫ 1 displaystyle n_ i gg 1 ( x ! ≈ x x e − x 2 π x ) displaystyle left(x!approx x^ x ,e^ -x , sqrt 2pi x right) gives f ( n i ) = ∑ i ( n i + g i ) ln ⁡ ( n i + g i ) − n i ln ⁡ ( n i ) + α ( N − ∑ n i ) + β ( E − ∑ n i ε i ) + K . displaystyle f(n_ i )=sum _ i (n_ i +g_ i )ln(n_ i +g_ i )-n_ i ln(n_ i )+alpha left(N-sum n_ i right)+beta left(E-sum n_ i varepsilon _ i right)+K. Where K is the sum of a number of terms which are not functions of the n i displaystyle n_ i . Taking the derivative with respect to n i displaystyle displaystyle n_ i , and setting the result to zero and solving for n i displaystyle displaystyle n_ i n i = g i e α + β ε i − 1 . displaystyle n_ i = frac g_ i e^ alpha +beta varepsilon _ i -1 . By a process similar to that outlined in the Maxwell–Boltzmann statistics article, it can be seen that: d ln ⁡ W = α d N + β d E displaystyle dln W=alpha ,dN+beta ,dE which, using Boltzmann's famous relationship S = k ln ⁡ W displaystyle S=k,ln W becomes a statement of the second law of thermodynamics at constant volume, and it follows that β = 1 k T displaystyle beta = frac 1 kT and α = − μ k T displaystyle alpha =- frac mu kT where S is the entropy, μ displaystyle mu is the chemical potential, k is Boltzmann's constant Boltzmann's constant and T is the temperature, so that finally: n i = g i e ( ε i − μ ) / k T − 1 . displaystyle n_ i = frac g_ i e^ (varepsilon _ i -mu )/kT -1 . Note that the above formula is sometimes written: n i = g i e ε i / k T / z − 1 , displaystyle n_ i = frac g_ i e^ varepsilon _ i /kT /z-1 , where z = exp ⁡ ( μ / k T ) displaystyle displaystyle z=exp(mu /kT) is the absolute activity, as noted by McQuarrie. Also note that when the particle numbers are not conserved, removing the conservation of particle numbers constraint is equivalent to setting α displaystyle alpha and therefore the chemical potential μ displaystyle mu to zero. This will be the case for photons and massive particles in mutual equilibrium and the resulting distribution will be the Planck distribution.NotesA much simpler way to think of Bose– Einstein Einstein distribution function is to consider that n particles are denoted by identical balls and g shells are marked by g-1 line partitions. It is clear that the permutations of these n balls and g − 1 partitions will give different ways of arranging bosons in different energy levels. Say, for 3 (= n) particles and 3 (= g) shells, therefore (g − 1) = 2, the arrangement might be ●●●, or ●●●, or ●●● , etc. Hence the number of distinct permutations of n + (g-1) objects which have n identical items and (g − 1) identical items will be: ( g − 1 + n ) ! ( g − 1 ) ! n ! displaystyle frac (g-1+n)! (g-1)!n! OR The purpose of these notes is to clarify some aspects of the derivation of the Bose– Einstein Einstein (B–E) distribution for beginners. The enumeration of cases (or ways) in the B–E distribution can be recast as follows. Consider a game of dice throwing in which there are n displaystyle displaystyle n dice, with each die taking values in the set 1 , … , g displaystyle displaystyle 1,dots ,g , for g ≥ 1 displaystyle ggeq 1 . The constraints of the game are that the value of a die i displaystyle displaystyle i , denoted by m i displaystyle displaystyle m_ i , has to be greater than or equal to the value of die ( i − 1 ) displaystyle displaystyle (i-1) , denoted by m i − 1 displaystyle displaystyle m_ i-1 , in the previous throw, i.e., m i ≥ m i − 1 displaystyle m_ i geq m_ i-1 . Thus a valid sequence of die throws can be described by an n-tuple ( m 1 , m 2 , … , m n ) displaystyle displaystyle (m_ 1 ,m_ 2 ,dots ,m_ n ) , such that m i ≥ m i − 1 displaystyle m_ i geq m_ i-1 . Let S ( n , g ) displaystyle displaystyle S(n,g) denote the set of these valid n-tuples: S ( n , g ) = ( m 1 , m 2 , … , m n ) m i ≥ m i − 1 , m i ∈ 1 , … , g , ∀ i = 1 , … , n . displaystyle S(n,g)= Big left(m_ 1 ,m_ 2 ,dots ,m_ n right) Big Big . m_ i geq m_ i-1 ,m_ i in left 1,dots ,gright ,forall i=1,dots ,n Big . (1)Then the quantity w ( n , g ) displaystyle displaystyle w(n,g) (defined above as the number of ways to distribute n displaystyle displaystyle n particles among the g displaystyle displaystyle g sublevels of an energy level) is the cardinality of S ( n , g ) displaystyle displaystyle S(n,g) , i.e., the number of elements (or valid n-tuples) in S ( n , g ) displaystyle displaystyle S(n,g) . Thus the problem of finding an expression for w ( n , g ) displaystyle displaystyle w(n,g) becomes the problem of counting the elements in S ( n , g ) displaystyle displaystyle S(n,g) . Example n = 4, g = 3: S ( 4 , 3 ) = ( 1111 ) , ( 1112 ) , ( 1113 ) ⏟ ( a ) , ( 1122 ) , ( 1123 ) , ( 1133 ) ⏟ ( b ) , ( 1222 ) , ( 1223 ) , ( 1233 ) , ( 1333 ) ⏟ ( c ) , displaystyle S(4,3)=left underbrace (1111),(1112),(1113) _ (a) ,underbrace (1122),(1123),(1133) _ (b) ,underbrace (1222),(1223),(1233),(1333) _ (c) ,right. ( 2222 ) , ( 2223 ) , ( 2233 ) , ( 2333 ) , ( 3333 ) ⏟ ( d ) displaystyle left.underbrace (2222),(2223),(2233),(2333),(3333) _ (d) right w ( 4 , 3 ) = 15 displaystyle displaystyle w(4,3)=15 (there are 15 displaystyle displaystyle 15 elements in S ( 4 , 3 ) displaystyle displaystyle S(4,3) )Subset ( a ) displaystyle displaystyle (a) is obtained by fixing all indices m i displaystyle displaystyle m_ i to 1 displaystyle displaystyle 1 , except for the last index, m n displaystyle displaystyle m_ n , which is incremented from 1 displaystyle displaystyle 1 to g = 3 displaystyle displaystyle g=3 . Subset ( b ) displaystyle displaystyle (b) is obtained by fixing m 1 = m 2 = 1 displaystyle displaystyle m_ 1 =m_ 2 =1 , and incrementing m 3 displaystyle displaystyle m_ 3 from 2 displaystyle displaystyle 2 to g = 3 displaystyle displaystyle g=3 . Due to the constraint m i ≥ m i − 1 displaystyle displaystyle m_ i geq m_ i-1 on the indices in S ( n , g ) displaystyle displaystyle S(n,g) , the index m 4 displaystyle displaystyle m_ 4 must automatically take values in 2 , 3 displaystyle displaystyle left 2,3right . The construction of subsets ( c ) displaystyle displaystyle (c) and ( d ) displaystyle displaystyle (d) follows in the same manner. Each element of S ( 4 , 3 ) displaystyle displaystyle S(4,3) can be thought of as a multiset of cardinality n = 4 displaystyle displaystyle n=4 ; the elements of such multiset are taken from the set 1 , 2 , 3 displaystyle displaystyle left 1,2,3right of cardinality g = 3 displaystyle displaystyle g=3 , and the number of such multisets is the multiset coefficient ⟨ 3 4 ⟩ = ( 3 + 4 − 1 3 − 1 ) = ( 3 + 4 − 1 4 ) = 6 ! 4 ! 2 ! = 15 displaystyle displaystyle leftlangle begin matrix 3\4end matrix rightrangle = 3+4-1 choose 3-1 = 3+4-1 choose 4 = frac 6! 4!2! =15 More generally, each element of S ( n , g ) displaystyle displaystyle S(n,g) is a multiset of cardinality n displaystyle displaystyle n (number of dice) with elements taken from the set 1 , … , g displaystyle displaystyle left 1,dots ,gright of cardinality g displaystyle displaystyle g (number of possible values of each die), and the number of such multisets, i.e., w ( n , g ) displaystyle displaystyle w(n,g) is the multiset coefficient w ( n , g ) = ⟨ g n ⟩ = ( g + n − 1 g − 1 ) = ( g + n − 1 n ) = ( g + n − 1 ) ! n ! ( g − 1 ) ! displaystyle displaystyle w(n,g)=leftlangle begin matrix g\nend matrix rightrangle = g+n-1 choose g-1 = g+n-1 choose n = frac (g+n-1)! n!(g-1)! (2)which is exactly the same as the formula for w ( n , g ) displaystyle displaystyle w(n,g) , as derived above with the aid of a theorem involving binomial coefficients, namely ∑ k = 0 n ( k + a ) ! k ! a ! = ( n + a + 1 ) ! n ! ( a + 1 ) ! . displaystyle sum _ k=0 ^ n frac (k+a)! k!a! = frac (n+a+1)! n!(a+1)! . (3)To understand the decomposition w ( n , g ) = ∑ k = 0 n w ( n − k , g − 1 ) = w ( n , g − 1 ) + w ( n − 1 , g − 1 ) + ⋯ + w ( 1 , g − 1 ) + w ( 0 , g − 1 ) displaystyle displaystyle w(n,g)=sum _ k=0 ^ n w(n-k,g-1)=w(n,g-1)+w(n-1,g-1)+cdots +w(1,g-1)+w(0,g-1) (4)or for example, n = 4 displaystyle displaystyle n=4 and g = 3 displaystyle displaystyle g=3 w ( 4 , 3 ) = w ( 4 , 2 ) + w ( 3 , 2 ) + w ( 2 , 2 ) + w ( 1 , 2 ) + w ( 0 , 2 ) , displaystyle displaystyle w(4,3)=w(4,2)+w(3,2)+w(2,2)+w(1,2)+w(0,2), let us rearrange the elements of S ( 4 , 3 ) displaystyle displaystyle S(4,3) as follows S ( 4 , 3 ) = ( 1111 ) , ( 1112 ) , ( 1122 ) , ( 1222 ) , ( 2222 ) ⏟ ( α ) , ( 111 3 = ) , ( 112 3 = ) , ( 122 3 = ) , ( 222 3 = ) ⏟ ( β ) , displaystyle S(4,3)=left underbrace (1111),(1112),(1122),(1222),(2222) _ (alpha ) ,underbrace (111 color Red underset = 3 ),(112 color Red underset = 3 ),(122 color Red underset = 3 ),(222 color Red underset = 3 ) _ (beta ) ,right. ( 11 33 == ) , ( 12 33 == ) , ( 22 33 == ) ⏟ ( γ ) , ( 1 333 === ) , ( 2 333 === ) ⏟ ( δ ) ( 3333 ==== ) ⏟ ( ω ) . displaystyle left.underbrace (11 color Red underset == 33 ),(12 color Red underset == 33 ),(22 color Red underset == 33 ) _ (gamma ) ,underbrace (1 color Red underset === 333 ),(2 color Red underset === 333 ) _ (delta ) underbrace ( color Red underset ==== 3333 ) _ (omega ) right . Clearly, the subset ( α ) displaystyle displaystyle (alpha ) of S ( 4 , 3 ) displaystyle displaystyle S(4,3) is the same as the set S ( 4 , 2 ) = ( 1111 ) , ( 1112 ) , ( 1122 ) , ( 1222 ) , ( 2222 ) displaystyle displaystyle S(4,2)=left (1111),(1112),(1122),(1222),(2222)right .By deleting the index m 4 = 3 displaystyle displaystyle m_ 4 =3 (shown in red with double underline) in the subset ( β ) displaystyle displaystyle (beta ) of S ( 4 , 3 ) displaystyle displaystyle S(4,3) , one obtains the set S ( 3 , 2 ) = ( 111 ) , ( 112 ) , ( 122 ) , ( 222 ) displaystyle displaystyle S(3,2)=left (111),(112),(122),(222)right .In other words, there is a one-to-one correspondence between the subset ( β ) displaystyle displaystyle (beta ) of S ( 4 , 3 ) displaystyle displaystyle S(4,3) and the set S ( 3 , 2 ) displaystyle displaystyle S(3,2) . We write ( β ) ⟷ S ( 3 , 2 ) displaystyle displaystyle (beta )longleftrightarrow S(3,2) .Similarly, it is easy to see that ( γ ) ⟷ S ( 2 , 2 ) = ( 11 ) , ( 12 ) , ( 22 ) displaystyle displaystyle (gamma )longleftrightarrow S(2,2)=left (11),(12),(22)right ( δ ) ⟷ S ( 1 , 2 ) = ( 1 ) , ( 2 ) displaystyle displaystyle (delta )longleftrightarrow S(1,2)=left (1),(2)right ( ω ) ⟷ S ( 0 , 2 ) = ∅ displaystyle displaystyle (omega )longleftrightarrow S(0,2)=varnothing (empty set).Thus we can write S ( 4 , 3 ) = ⋃ k = 0 4 S ( 4 − k , 2 ) displaystyle displaystyle S(4,3)=bigcup _ k=0 ^ 4 S(4-k,2) or more generally, S ( n , g ) = ⋃ k = 0 n S ( n − k , g − 1 ) displaystyle displaystyle S(n,g)=bigcup _ k=0 ^ n S(n-k,g-1) ;(5)and since the sets S ( i , g − 1 )   ,   f o r   i = 0 , … , n displaystyle displaystyle S(i,g-1) , rm for i=0,dots ,n are non-intersecting, we thus have w ( n , g ) = ∑ k = 0 n w ( n − k , g − 1 ) displaystyle displaystyle w(n,g)=sum _ k=0 ^ n w(n-k,g-1) ,(6)with the convention that w ( 0 , g ) = 1   , ∀ g   , a n d   w ( n , 0 ) = 1   , ∀ n displaystyle displaystyle w(0,g)=1 ,forall g , rm and w(n,0)=1 ,forall n .(7)Continuing the process, we arrive at the following formula w ( n , g ) = ∑ k 1 = 0 n ∑ k 2 = 0 n − k 1 w ( n − k 1 − k 2 , g − 2 ) = ∑ k 1 = 0 n ∑ k 2 = 0 n − k 1 ⋯ ∑ k g = 0 n − ∑ j = 1 g − 1 k j w ( n − ∑ i = 1 g k i , 0 ) . displaystyle displaystyle w(n,g)=sum _ k_ 1 =0 ^ n sum _ k_ 2 =0 ^ n-k_ 1 w(n-k_ 1 -k_ 2 ,g-2)=sum _ k_ 1 =0 ^ n sum _ k_ 2 =0 ^ n-k_ 1 cdots sum _ k_ g =0 ^ n-sum _ j=1 ^ g-1 k_ j w(n-sum _ i=1 ^ g k_ i ,0). Using the convention (7)2 above, we obtain the formula w ( n , g ) = ∑ k 1 = 0 n ∑ k 2 = 0 n − k 1 ⋯ ∑ k g = 0 n − ∑ j = 1 g − 1 k j 1 , displaystyle displaystyle w(n,g)=sum _ k_ 1 =0 ^ n sum _ k_ 2 =0 ^ n-k_ 1 cdots sum _ k_ g =0 ^ n-sum _ j=1 ^ g-1 k_ j 1, (8)keeping in mind that for q displaystyle displaystyle q and p displaystyle displaystyle p being constants, we have ∑ k = 0 q p = q p displaystyle displaystyle sum _ k=0 ^ q p=qp .(9)It can then be verified that (8) and (2) give the same result for w ( 4 , 3 ) displaystyle displaystyle w(4,3) , w ( 3 , 3 ) displaystyle displaystyle w(3,3) , w ( 3 , 2 ) displaystyle displaystyle w(3,2) , etc.Interdisciplinary applications Viewed as a pure probability distribution, the Bose–Einstein distribution has found application in other fields:In recent years, Bose Einstein Einstein statistics have also been used as a method for term weighting in information retrieval. The method is one of a collection of DFR ("Divergence From Randomness") models, the basic notion being that Bose Einstein Einstein statistics may be a useful indicator in cases where a particular term and a particular document have a significant relationship that would not have occurred purely by chance. Source code for implementing this model is available from the Terrier project at the University of Glasgow.Main article: Bose– Einstein Einstein condensation (network theory) The evolution of many complex systems, including the World Wide Web, business, and citation networks, is encoded in the dynamic web describing the interactions between the system's constituents. Despite their irreversible and nonequilibrium nature these networks follow Bose statistics and can undergo Bose– Einstein Einstein condensation. Addressing the dynamical properties of these nonequilibrium systems within the framework of equilibrium quantum gases predicts that the "first-mover-advantage," "fit-get-rich(FGR)," and "winner-takes-all" phenomena observed in competitive systems are thermodynamically distinct phases of the underlying evolving networks.See alsoBose– Einstein Einstein correlations Einstein Einstein solid Higgs boson Parastatistics Planck's law Planck's law of black body radiation Superconductivity Fermi–Dirac statistics Maxwell–Boltzmann statisticsNotes^ See p. 14, note 3, of the Ph.D. Thesis entitled Bose–Einstein condensation: analysis of problems and rigorous results, presented by Alessandro Michelangeli to the International School for Advanced Studies, Mathematical Physics Sector, October 2007 for the degree of Ph.D. See: "Archived copy". Archived from the original on 2013-11-06. Retrieved 2012-03-25. ?show=full, and download from "Archived copy". Archived from the original on 2013-11-06. Retrieved 2012-03-25.  ^ Bose (2 July 1924). " Planck's law Planck's law and the hypothesis of light quanta" (PostScript). University of Oldenburg. Retrieved 30 November 2016.  ^ Bose (1924), "Plancks Gesetz und Lichtquantenhypothese", Zeitschrift für Physik (in German), 26: 178–181, Bibcode:1924ZPhy...26..178B, doi:10.1007/BF01327326  ^ Srivastava, R. K.; Ashok, J. (2005). "Chapter 7". Statistical Mechanics. New Delhi: PHI Learning Pvt. Ltd. ISBN 9788120327825.  ^ "Chapter 6". Statistical Mechanics. ISBN 9788120327825.  ^ The BE distribution can be derived also from thermal field theory. ^ R.B. Dingle, Asymptotic Expansions: Their Derivation and Interpretation, Academic Press (1973), pp. 267–271. ^ H.J.W. Müller-Kirsten, Basics of Statistical Physics, 2nd ed., World Scientific (2013), ISBN 978-981-4449-53-3. ^ Ziff R. M; Kac, M.; Uhlenbeck, G. E. (1977). "The ideal Bose- Einstein Einstein gas, revisited." Phys. Reports 32: 169-248. ^ See McQuarrie in citations ^ Amati, G.; C. J. Van Rijsbergen (2002). "Probabilistic models of information retrieval based on measuring the divergence from randomness " ACM TOIS 20(4):357–389. ^ Bianconi, G.; Barabási, A.-L. (2001). "Bose– Einstein Einstein Condensation in Complex Networks." Phys. Rev. Lett. 86: 5632–35.ReferencesAnnett, James F. (2004). Superconductivity, Superfluids and Condensates. New York: Oxford University Press. ISBN 0-19-850755-0.  Carter, Ashley H. (2001). Classical and Statistical Thermodynamics. Upper Saddle River, New Jersey: Prentice Hall. ISBN 0-13-779208-5.  Griffiths, David J. (2005). Introduction to Quantum Mechanics (2nd ed.). Upper Saddle River, New Jersey: Pearson, Prentice Hall. ISBN 0-13-191175-9.  McQuarrie, Donald A. (2000). Statistical Mechanics (1st ed.). Sausalito, California 94965: University Science Books. p. 55. ISBN 1-891389-15-7. v t eStatistical mechanicsStatistical ensemblesMicrocanonical Canonical Grand canonical Isothermal–isobaric Isoenthalpic–isobaric ensembleStatistical thermodynamicsCharacteristic state functionsPartition functionsTranslational Vibrational RotationalEquations of stateDieterici Van der Waals/Real gas law Ideal gas law Birch–MurnaghanEntropySackur–Tetrode equation Tsallis entropy Von Neumann entropyParticle statisticsMaxwell–Boltzmann statistics Fermi–Dirac statistics Bose– Einstein Einstein statisticsStatistical field theoryConformal field theory Osterwalder–Schrader axioms Quantum statistical mechanicsDensity matrix Gibbs measure Partition function Phase space formulation Phase space formulation of quantum mechanics Slater determinantSee alsoProbability distribution Elementary particlesv t eAlbert EinsteinPhysicsWorks Annus Mirabilis papers Annus Mirabilis papers (1905) "Investigations on the Theory of Brownian Movement" (1905) Relativity: The Special Special and the General Theory (1916) The World as I See It (1934) "Why Socialism?" 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