def Pag(self, EMeV, g11, m_neV, bfield='jansson12'):
"""
Compute the photon-ALP conversion probability
Parameters
----------
EMeV: array-like
n-dim array with energies in MeV
g11: float
ALP coupling in 10^-11 GeV-1
m_neV: float
ALP mass in neV
{options}
bfield: str
identifier for Milky Way magnetic field. default: 'jansson12'
Returns
-------
Array with averaged ALP flux in units of MeV-1 s-1
"""
# calculate conversion probability
m = ModuleList(ALP(m=m_neV, g=g11),
self._src,
pin=np.diag([0., 0., 1.]),
EGeV=EMeV / 1000.)
m.add_propagation("GMF", 0, model=bfield)
px, py, pa = m.run()
return np.squeeze(px + py)
def propagation(self, EGeV, m=1., g=1., B0=10., seed=None, nsim=1):
'''Runs the propagation in magnetic fields, copied from me-manu, NGC1257 example notebook.
Returns: Probabilities of finding photon (transversal1, 2) and ALP.
Currently not used. Rather use read_probs() to use pre-computed probabilities saving time and thus
being able to run on standard BIRD-computation slots.
'''
alp = ALP(m, g)
ngc1275 = Source(z=0.017559, ra='03h19m48.1s', dec='+41d30m42s')
pin = np.diag((1., 1., 0.)) * 0.5
lambda_max = 35.
lambda_min = 0.7
kL = 2. * np.pi / lambda_max
kH = 2. * np.pi / lambda_min
m = ModuleList(alp, ngc1275, pin=pin, EGeV=EGeV)
print('m:'), m.alp.m
print('g:'), m.alp.g
print('B:'), B0
m.add_propagation(
"ICMGaussTurb",
0, # position of module counted from the source.
nsim=nsim, # number of random B-field realizations
B0=B0, # rms of B field
n0=39., # normalization of electron density
n2=
4.05, # second normalization of electron density, see Churazov et al. 2003, Eq. 4
r_abell=500., # extension of the cluster
r_core=
80., # electron density parameter, see Churazov et al. 2003, Eq. 4
r_core2=
280., # electron density parameter, see Churazov et al. 2003, Eq. 4
beta=
1.2, # electron density parameter, see Churazov et al. 2003, Eq. 4
beta2=
0.58, # electron density parameter, see Churazov et al. 2003, Eq. 4
eta=0.5, # scaling of B-field with electron denstiy
kL=
kL, # maximum turbulence scale in kpc^-1, taken from A2199 cool-core cluster, see Vacca et al. 2012
kH=
kH, # minimum turbulence scale, taken from A2199 cool-core cluster, see Vacca et al. 2012
q=-2.80, # turbulence spectral index, taken from A2199 cool-core cluster, see Vacca et al. 2012
seed=
seed # random seed for reproducability, set to None for random seed.
)
m.add_propagation(
"EBL", 1, model='dominguez'
) # EBL attenuation comes second, after beam has left cluster
m.add_propagation(
"GMF", 2, model='jansson12',
model_sum='ASS') # finally, the beam enters the Milky Way Field
px, py, pa = m.run()
self.px = px
self.py = py
self.pa = pa
self.p = px + py
eta=0.5, # scaling of B-field with electron denstiy
kL=
kL, # maximum turbulence scale in kpc^-1, taken from A2199 cool-core cluster, see Vacca et al. 2012
kH=
kH, # minimum turbulence scale, taken from A2199 cool-core cluster, see Vacca et al. 2012
q=-2.80, # turbulence spectral index, taken from A2199 cool-core cluster, see Vacca et al. 2012
seed=
seed # random seed for reproducability, set to None for random seed.
)
m.add_propagation(
"EBL", 1, model='dominguez'
) # EBL attenuation comes second, after beam has left cluster
m.add_propagation(
"GMF", 2, model='jansson12',
model_sum='ASS') # finally, the beam enters the Milky Way Field
p_gamma_x, p_gamma_y, p_a = m.run()
p_gamma_tot = p_gamma_x + p_gamma_y
p_orig = p_gamma_tot.copy()
# np.savetxt(f'/nfs/astrop/n1/kuhlmann/NGC_1275/ts_limit/grid_survival_prob/new_probs/part_{part}.dat', p_orig)
del p_a
del p_gamma_x
del p_gamma_y
del p_gamma_tot
p_orig = p_orig.reshape((nsim, EGeV.shape[0], ppb))
p_orig = np.average(p_orig, axis=-1)
# print(p_orig.shape)
# print(p_gamma_tot.shape)
# for _sim in range(nsim):
# for _bin in range(nbins):
# # print(p_gamma_tot[_sim, _bin*ppb:(_bin+1)*ppb])
# p_gamma_av[_sim, _bin] = np.average(p_gamma_tot[_sim, _bin*ppb:(_bin+1)*ppb], axis=-1)
for t in tsn:
src = Source(z = float(t['z']),
ra = float(t['ra']),
dec = float(t['dec']))
mod = ModuleList(ALP(m=1.,g=args.gref), src, pin = pin, EGeV = EMeV / 1e3)
mod.add_propagation("GMF",0, model = 'jansson12', model_sum = 'ASS')
c['time'].append(ts)
for i,m in enumerate(mneV):
if not '{0:.3f}'.format(m) in c.keys():
c['{0:.3f}'.format(m)] = []
mod.alp.m = m
px,py,pa = mod.run(multiprocess=2)
# calculate photon flux in photons / MeV / s / cm^2:
d = cosmo.luminosity_distance(t['z'])
flux = f * (px[0] + py[0]) / 4. / np.pi / d.to('cm') ** 2.
# integrate flux over energy to obtain light curve in photons / s / cm^2
flux_int = simps(flux * EMeV, np.log(EMeV), axis = 1)
c['{0:.3f}'.format(m)].append(flux_int)
del mod
result = Table(c)
result.write('ALPSNSignal_Mprog{0:.0f}.fits'.format(asn.Mprog),
overwrite = True)