Bug in the C++ standard library in std::poisson_distribution?

I think I have encountered an incorrect behaviour of std::poisson_distribution from C++ standard library.

Questions:

1. Could you confirm it is indeed a bug and not my error?
2. What exactly is wrong in the standard library code of poisson_distribution function, assuming that it is indeed a bug?

Details:

The following C++ code (file poisson_test.cc) is used to generate Poisson-distributed numbers:

#include <array>
#include <cmath>
#include <iostream>
#include <random>

int main() {
  // The problem turned out to be independent on the engine
  std::mt19937_64 engine;

  // Set fixed seed for easy reproducibility
  // The problem turned out to be independent on seed
  engine.seed(1);
  std::poisson_distribution<int> distribution(157.17);

  for (int i = 0; i < 1E8; i++) {
    const int number = distribution(engine);
    std::cout << number << std::endl;
  }
}

I compile this code as follows:

clang++ -o poisson_test -std=c++11 poisson_test.cc
./poisson_test > mypoisson.txt

The following python script was used to analyze the sequence of random numbers from file mypoisson.txt:

import numpy as np
import matplotlib.pyplot as plt

def expectation(x, m):
    " Poisson pdf " 
    # Use Ramanujan formula to get ln n!
    lnx = x * np.log(x) - x + 1./6. * np.log(x * (1 + 4*x*(1+2*x))) + 1./2. * np.log(np.pi)
    return np.exp(x*np.log(m) - m - lnx)

data = np.loadtxt('mypoisson.txt', dtype = 'int')

unique, counts = np.unique(data, return_counts = True)
hist = counts.astype(float) / counts.sum()
stat_err = np.sqrt(counts) / counts.sum()
plt.errorbar(unique, hist, yerr = stat_err, fmt = '.', \
             label = 'Poisson generated \n by std::poisson_distribution')
plt.plot(unique, expectation(unique, expected_mean), \
         label = 'expected probability \n density function')
plt.legend()
plt.show()

# Determine bins with statistical significance of deviation larger than 3 sigma
deviation_in_sigma = (hist - expectation(unique, expected_mean)) / stat_err
d = dict((k, v) for k, v in zip(unique, deviation_in_sigma) if np.abs(v) > 3.0)
print d

The script produces the following plot:

You can see the problem by bare eye. The deviation at n = 158 is statistically significant, it is in fact a 22σ deviation!

Close-up of the previous plot.