snorer.nxSpike¶

snorer.nxSpike(r,mx,profile='MW',sigv=None,tBH=1e+10,alpha='3/2')¶

Dark matter number density of Milky Way of Large Magellanic Cloud at distance \(r\) to the galactic center. Spike feature is included.

r : array_like
    Distance to galactic center, kpc

mx : array_like
    Dark matter mass, MeV

profile : str
    'MW' or 'LMC', stands for MW halo or LMC halo

sigv : scalar
    DM annihilation cross section, in the unit of \(10^{-26}\) cm³ s⁻¹. None indicates no annihilation

tBH : float
    Supermassive black hole age, years

alpha : str
    Slope of the spike, '3/2' or '7/3'

out : scalar/ndarray
    Dark matter number density at r with spike in the center, cm⁻³

Let's plot \(n_\chi\) for different \(\langle \sigma v\rangle\).

import numpy as np
import matplotlib.pyplot as plt
import snorer as sn

# DM mass, keV
mx = 0.01
# radius, kpc
r_vals = np.logspace(-5,2,100)
# profiles

sigv_vals = [None,0.01,0.1,3]

for sigv in sigv_vals:
    # calculate nx
    nx_vals = nxSpike(r_vals,mx,sigv=sigv,profile='LMC')
    if sigv is None: sigv = 0 # legend label
    plt.plot(r_vals,nx_vals,label=r'$\langle\sigma v\rangle=$' + str(sigv))
plt.xscale('log')
plt.yscale('log')
plt.xlabel(r'$r$ [kpc]')
plt.ylabel(r'$n_\chi(r)$ [cm$^{-3}$]')
plt.title(fr'LMC with spike and $m_\chi = {mx:.2f}$ MeV')
plt.legend()
plt.show()

To realize \(n_\chi\) with spike feature we initialized a snorer.HaloSpike instance inside the function snorer.nxSpike and utilize the callable feature. However, such callable function does not support vectorization. To mimic vectorized inputs/outputs, we employ numpy.nditer. It could become clumsy if the points to be calculated are massive.