# scipy.stats.rel_breitwigner

scipy.stats.rel_breitwigner = <scipy.stats._continuous_distns.rel_breitwigner_gen object>

  A relativistic Breit-Wigner random variable.

  As an instance of the `rv_continuous` class, `rel_breitwigner` object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

  See also

  `cauchy`

    Cauchy distribution, also known as the Breit-Wigner distribution.

  Notes

  The probability density function for `rel_breitwigner` is

  \[f(x, \rho) = \frac{k}{(x^2 - \rho^2)^2 + \rho^2}\]

  where

  \[k = \frac{2\sqrt{2}\rho^2\sqrt{\rho^2 + 1}} {\pi\sqrt{\rho^2 + \rho\sqrt{\rho^2 + 1}}}\]

  The relativistic Breit-Wigner distribution is used in high energy physics to model resonances [1]. It gives the uncertainty in the invariant mass, \(M\) [2], of a resonance with characteristic mass \(M_0\) and decay-width \(\Gamma\), where \(M\), \(M_0\) and \(\Gamma\) are expressed in natural units. In SciPy’s parametrization, the shape parameter \(\rho\) is equal to \(M_0/\Gamma\) and takes values in \((0, \infty)\).

  Equivalently, the relativistic Breit-Wigner distribution is said to give the uncertainty in the center-of-mass energy \(E_{\text{cm}}\). In natural units, the speed of light \(c\) is equal to 1 and the invariant mass \(M\) is equal to the rest energy \(Mc^2\). In the center-of-mass frame, the rest energy is equal to the total energy [3].

  The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the `loc` and `scale` parameters. Specifically, `rel_breitwigner.pdf(x, rho, loc, scale)` is identically equivalent to `rel_breitwigner.pdf(y, rho) / scale` with `y = (x - loc) / scale`. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.

  \(\rho = M/\Gamma\) and \(\Gamma\) is the scale parameter. For example, if one seeks to model the \(Z^0\) boson with \(M_0 \approx 91.1876 \text{ GeV}\) and \(\Gamma \approx 2.4952\text{ GeV}\) [4] one can set `rho=91.1876/2.4952` and `scale=2.4952`.

  To ensure a physically meaningful result when using the `fit` method, one should set `floc=0` to fix the location parameter to 0.

  References

  [1]

  Relativistic Breit-Wigner distribution, Wikipedia, https://en.wikipedia.org/wiki/Relativistic_Breit-Wigner_distribution

  [2]

  Invariant mass, Wikipedia, https://en.wikipedia.org/wiki/Invariant_mass

  [3]

  Center-of-momentum frame, Wikipedia, https://en.wikipedia.org/wiki/Center-of-momentum_frame

  [4]

  M. Tanabashi et al. (Particle Data Group) Phys. Rev. D 98, 030001 - Published 17 August 2018

  Examples

      >>> import numpy as np
      >>> from scipy.stats import rel_breitwigner
      >>> import matplotlib.pyplot as plt
      >>> fig, ax = plt.subplots(1, 1)

  Calculate the first four moments:

      >>> rho = 36.5
      >>> mean, var, skew, kurt = rel_breitwigner.stats(rho, moments='mvsk')

  Display the probability density function (`pdf`):

      >>> x = np.linspace(rel_breitwigner.ppf(0.01, rho),
      ...                 rel_breitwigner.ppf(0.99, rho), 100)
      >>> ax.plot(x, rel_breitwigner.pdf(x, rho),
      ...        'r-', lw=5, alpha=0.6, label='rel_breitwigner pdf')

  Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

  Freeze the distribution and display the frozen `pdf`:

      >>> rv = rel_breitwigner(rho)
      >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

  Check accuracy of `cdf` and `ppf`:

      >>> vals = rel_breitwigner.ppf([0.001, 0.5, 0.999], rho)
      >>> np.allclose([0.001, 0.5, 0.999], rel_breitwigner.cdf(vals, rho))
      True

  Generate random numbers:

      >>> r = rel_breitwigner.rvs(rho, size=1000)

  And compare the histogram:

      >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
      >>> ax.set_xlim([x[0], x[-1]])
      >>> ax.legend(loc='best', frameon=False)
      >>> plt.show()

  Methods

  rvs(rho, loc=0, scale=1, size=1, random_state=None) | Random variates.
  pdf(x, rho, loc=0, scale=1) | Probability density function.
  logpdf(x, rho, loc=0, scale=1) | Log of the probability density function.
  cdf(x, rho, loc=0, scale=1) | Cumulative distribution function.
  logcdf(x, rho, loc=0, scale=1) | Log of the cumulative distribution function.
  sf(x, rho, loc=0, scale=1) | Survival function (also defined as `1 - cdf`, but sf is sometimes more accurate).
  logsf(x, rho, loc=0, scale=1) | Log of the survival function.
  ppf(q, rho, loc=0, scale=1) | Percent point function (inverse of `cdf` — percentiles).
  isf(q, rho, loc=0, scale=1) | Inverse survival function (inverse of `sf`).
  moment(order, rho, loc=0, scale=1) | Non-central moment of the specified order.
  stats(rho, loc=0, scale=1, moments=’mv’) | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
  entropy(rho, loc=0, scale=1) | (Differential) entropy of the RV.
  fit(data) | Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments.
  expect(func, args=(rho,), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) | Expected value of a function (of one argument) with respect to the distribution.
  median(rho, loc=0, scale=1) | Median of the distribution.
  mean(rho, loc=0, scale=1) | Mean of the distribution.
  var(rho, loc=0, scale=1) | Variance of the distribution.
  std(rho, loc=0, scale=1) | Standard deviation of the distribution.
  interval(confidence, rho, loc=0, scale=1) | Confidence interval with equal areas around the median.