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arXiv 2502.06384math-phmath.MP
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多项式超可积哈密顿量在笛卡尔坐标中的分离

Polynomially Superintegrable Hamiltonians Separating in Cartesian Coordinates

Ian Marquette, Anthony Parr

AI总结:

本文提出了一种新方法,用于计算笛卡尔坐标中分离变量的超可积哈密顿量的系数,无需将势能分为标准和非标准类别,并给出了四阶标准势能及一般族的猜想。

摘要:

寻找超可积哈密顿量及其运动积分的问题可以归结为求解一系列相容性方程,这些方程源于对易关系或泊松括号关系的超定性。计算相容性方程需要一个通用公式来表示系数,而这些系数又必须依赖于待求解的势能。这通常是一个非线性问题,相当困难。因此,研究重点是将势能类别分为标准和非标准类别,以便设置部分参数为零,从而在更简单的设置中获得系数。我们开发了一种新的方法,适用于经典和量子情形,在笛卡尔可分离哈密顿量的情况下,无需依赖这种分类即可直接得到不变量的系数公式。尽管它们允许变量分离,因为它们通常涉及更高超越函数和超几何函数之外的势能形式,但它们却是相当复杂的模型。我们得到的表达式通常在动量上非多项式,其分数项可以任意设为零。这些条件等同于相容性方程,但除了势能外,唯一的未知数是常数参数。我们还给出了四阶标准势能,并对一般族提出猜想。

英文摘要:

The problem of finding superintegrable Hamiltonians and their integrals of motion can be reduced to solving a series of compatibility equations that result from the overdetermination of the commutator or Poisson bracket relations. The computation of the compatibility equations requires a general formula for the coefficients, which in turn must depend on the potential to be solved for. This is in general a nonlinear problem and quite difficult. Thus, research has focused on dividing the classes of potential into standard and exotic ones so that a number of parameters may be set to zero and the coefficients may be obtained in a simpler setting. We have developed a new method in both the classical and quantum setting which readily yields a formula for the coefficients of the invariant without recourse to this division in the case of Cartesian-separable Hamiltonians. Even though they allow separation of variables as they in general involve potential in terms of higher transcendental and beyond hypergeometric for their wavefunctions, they are quite non-trivial models. The expressions we obtain are in general non-polynomial in the momenta whose fractional terms can be arbitrarily set to zero. These conditions are equivalent to the compatibility equations, but the only unknowns in addition to the potential are constant parameters. We also give the fourth-order standard potentials, and conjectures about general families.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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