Physical Applications of Distribution Functions分布函数的物理应用
Fundamental to our understanding of classical molecular phenomena is the Boltzmann distribution, which tells us that the probability that any one molecule will be found with energy E decreases exponentially with energy; i.e., any one molecule is highly unlikely to grab much more than its average share of the total energy available to all the molecules. Mathematically, the Boltzmann distribution can be written in the form 我们对经典分子现象的理解基础在于玻尔兹曼分布,它告诉我们,任何单个分子具有能量E的概率会随着能量的增加呈指数衰减;即,任何单个分子很难获得比其平均份额更多的总能量。数学上,玻尔兹曼分布可以写成以下形式
This distribution can be made plausible by a numerical example, particularly when put in graphical form, but the rigorous mathematical development by Boltzmann still stands as a major achievement in the mathematics of physics. We will take it as a postulate in the development of physical models in kinetic theory. 这种分布可以通过一个数值例子来合理说明,尤其是在图形表示时更为明显,但玻尔兹曼对这一问题的严谨数学发展仍被视为物理学数学中的重大成就。我们在发展气体动理论的物理模型时,将把它作为公理来使用。
This idea that each "particle" is unlikely to have much more or less than its "fair share" of the energy can be extended to "modes" in wave phenomena such as the electromagnetic wave modes in a cavity, although this application shows its classical limitations in the Rayleigh-Jeans law. 这一想法,即每个‘粒子’不太可能拥有比其‘公平份额’多很多或少很多的‘能’,可以推广到波现象中的‘模式’,例如腔体中的电磁波模式,尽管这种应用在瑞利-金斯定律中显示出其经典局限性。
Another idea contained in the Boltzmann distribution above is that if the general "energy economy" is improved by increasing the temperature so that the total energy available to the particles is increased, a given particle is more likely to get a specified amount of energy. If the overall financial economy of the country is better with more money in circulation, then for any particular financial threshold you picked, there would be a higher probability that any given individual would reach that threshold. By analogy, if there is some particular threshold energy for a given phenomenon, such as ionization or excitation of vibrational states, then the likelihood of that phenomenon happening will increase with temperature in a generally predictable way. 玻尔兹曼分布中还包含另一个想法:如果通过提高温度改善总体的‘能量经济’,使得粒子可获得的总能量增加,那么某个特定粒子获得指定能量的可能性会增加。如果一个国家的总体经济状况改善,货币流通量增加,那么对于任何特定的财务门槛,任何个人达到该门槛的概率都会更高。类比地,如果某种现象有特定的能量门槛,例如离子化或振动态的激发,那么该现象发生的可能性将随着温度的升高而以一种普遍可预测的方式增加。
The statistical behavior of many-particle systems is described by the product of the density of states and the distribution function for these states. (See energy distribution as an example.) One of the simplest cases is that for radioactive decay since you are dealing with pure probability. The density of states can just be taken as a constant since there is no preference for one decay time over another, and the distribution function is simply 许多粒子系统的统计行为由态密度与这些态的分布函数的乘积来描述。(见能量分布作为例子。)其中一种最简单的例子是放射性衰变,因为你在处理纯粹的概率。由于没有一种衰变时间更受青睐,所以态密度可以简单地取为常数,而分布函数则 simply
![]() For the speed of molecules in a gas, however, the density of states can be modeled as an effective "volume" in "velocity space" which gives it the form 然而,对于气体中分子的速度,密度的态可以被建模为一个有效的‘体积’,在‘速度空间’中,这赋予了它特定的形式
![]() so that the distribution arising from the Boltzmann foundation is 使得从玻尔兹曼基础产生的分布是
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Comment on Statistical Methods in Physics关于物理学中统计方法的评论
While it may seem disturbing to describe physical phenomena in the same language you would use for coin flips or throwing dice, consider that in describing natural systems of atoms or molecules, we have on the order of Avogadro's number of events. If you are content to know things within a certain margin of error, say 1%, then the statistics of large numbers can give you that. 虽然用你用来描述抛硬币或掷骰子的语言来描述物理现象似乎令人不安,但考虑一下,在描述原子或分子的自然系统时,我们涉及的事件数量大约有阿伏伽德罗常数之多。如果你愿意在一定误差范围内了解事物,比如1%,那么大量事件的统计学就能提供这种精度。
Taking coin flips as an example and using the binomial distribution, you know the mean number of "heads" is going to be the number of flips (n) times the probability (p=0.5) so that np = half the total. ![]() In terms of percent deviation from the expected result, it is clear that the statistical result from a large number of events is much more precise. 从百分比偏差来看,大量事件的统计结果显然更加精确。
以硬币翻转为例,使用二项分布,你知道“正面”的平均数量将是翻转次数(n)乘以概率(p=0.5),因此np等于总数的一半。 |
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