def check_min_mass(element='fluorine',
Qmin=1.,
v_esc=544.,
v_lag=220.,
mx_guess=1.):
experiment = dmdd.Experiment('test', element, Qmin, 40., 100.,
eff.efficiency_unit)
res = experiment.find_min_mass(v_esc=v_esc, v_lag=v_lag, mx_guess=mx_guess)
print res, 'GeV'
if res < 0:
print 'Problem: try another mx_guess...'
SI_Higgs, millicharge, SD_flavoruniversal, SD_Zmediated, SD_Moira, anapole,
magdip_heavy, magdip_0, elecdip_heavy, elecdip_0, f1, f2_Higgs,
f2_flavoruniversal, f3_Higgs, f3_flavoruniversal
]
MODELS1 = [SI_Higgs, anapole]
SIMMODELS = [anapole] #[SI_Higgs,elecdip_heavy,elecdip_0]
FITMODELS = MODELS1
##get upper limits for a given mass:
pandax = dmdd.Experiment('PandaX',
'xenon',
4.,
30.,
188.,
dmdd.eff.efficiency_Xe,
0.,
1.,
energy_resolution=True)
lux = dmdd.Experiment('LUX',
'xenon',
5.,
50.,
70.,
dmdd.eff.efficiency_Xe,
0.,
1.,
energy_resolution=True)
cdmslite = dmdd.Experiment('CDMSlite',
'germanium',
elif args.timeonly == 'F':
timeonly = False
if args.GF == 'T':
GF = True
elif args.GF == 'F':
GF = False
experiments = []
for i, experiment in enumerate(args.exps):
efficiency = dmdd.eff.efficiency_Xe
experiments.append(
dmdd.Experiment(experiment,
Target[experiment],
Qmin[experiment],
Qmax[experiment],
Exposure[experiment],
efficiency,
Start[experiment],
End[experiment],
energy_resolution=EnergyResolution[experiment]))
fixedsim_params = {}
for i, par in enumerate(args.fixedsimnames):
fixedsim_params[par] = args.fixedsimvals[i]
fixedfit_params = {}
for i, par in enumerate(args.fixedfitnames):
fixedfit_params[par] = args.fixedfitvals[i]
simmodel = dmdd.UV_Model(args.simmodelname,
args.simpars,
def make_experiments(return_experiments=False):
xe = dmdd.Experiment('Xe', 'xenon', 5., 40., 100., eff.efficiency_unit)
ge = dmdd.Experiment('Ge', 'germanium', 0.4, 100., 100.,
eff.efficiency_unit)
if return_experiments:
return [xe, ge]
def test_UVrate():
experiment = dmdd.Experiment('Xe', 'xenon', 5., 40., 10000.,
eff.efficiency_unit)
models = make_UVmodels(return_models=True)
mass = 40.
qs = np.array([15.])
v_lag = 200.
v_rms = 100.
v_esc = 600.
rho_x = 0.4
sigma_names = {}
fnfp_names = {}
fnfp_vals = {}
for m in models:
sigma_names[m.name] = m.param_names[1]
if len(m.fixed_params) > 0:
fnfp_names[m.name] = m.fixed_params.keys()[0]
fnfp_vals[m.name] = m.fixed_params.values()[0]
else:
fnfp_names[m.name] = None
fnfp_vals[m.name] = None
dRdQs = np.zeros(len(models))
Rs = np.zeros(len(models))
for i, m in enumerate(models):
kwargs = {sigma_names[m.name]: 1.}
if fnfp_names[m.name] is not None:
kwargs[fnfp_names[m.name]] = fnfp_vals[m.name]
dRdQs[i] = dmdd.rate_UV.dRdQ(qs,
mass=mass,
element=experiment.element,
v_lag=v_lag,
v_rms=v_rms,
v_esc=v_esc,
rho_x=rho_x,
**kwargs)
Rs[i] = dmdd.rate_UV.R(eff.efficiency_unit,
mass=mass,
element=experiment.element,
Qmin=experiment.Qmin,
Qmax=experiment.Qmax,
v_lag=v_lag,
v_rms=v_rms,
v_esc=v_esc,
rho_x=rho_x,
**kwargs)
#print 'dRdQs = {}\n'.format(dRdQs)
#print 'Rs = {}\n'.format(Rs)
dRdQs_correct = [
1.27974652e-12, 1.67031585e-13, 6.28936205e-13, 7.76864477e-13,
7.71724584e-13, 5.66164037e-13, 8.40579288e-13, 6.16678247e-13,
4.72480605e-13, 2.59857470e-16, 9.59390104e-16, 1.14295679e-13
]
Rs_correct = [
6.15358778e-11, 3.10857259e-11, 3.14982315e-11, 4.14119198e-11,
1.82181891e-11, 3.84877268e-11, 2.35638282e-11, 5.50063883e-11,
1.34702925e-11, 5.82472177e-15, 1.64213483e-14, 2.26028126e-12
]
assert np.allclose(dRdQs_correct, dRdQs)
assert np.allclose(Rs_correct, Rs)
###
qs = np.array([8.3, 15.7])
logtest1 = dmdd.rate_UV.loglikelihood(qs,
eff.efficiency_unit,
mass=mass,
sigma_si=1.,
fnfp_si=1.,
element=experiment.element,
Qmin=experiment.Qmin,
Qmax=experiment.Qmax,
exposure=experiment.exposure,
energy_resolution=True,
v_lag=v_lag,
v_rms=v_rms,
v_esc=v_esc,
rho_x=rho_x)
logtest2 = dmdd.rate_UV.loglikelihood(qs,
eff.efficiency_unit,
mass=mass,
sigma_si=1.,
fnfp_si=1.,
element=experiment.element,
Qmin=experiment.Qmin,
Qmax=experiment.Qmax,
exposure=experiment.exposure,
energy_resolution=False,
v_lag=v_lag,
v_rms=v_rms,
v_esc=v_esc,
rho_x=rho_x)
#print 'logtest1_correct={}'.format(logtest1)
#print 'logtest2_correct={}'.format(logtest2)
logtest1_correct = -19.6890210901
logtest2_correct = -13.4627188661
print('correct={} got={}\n'.format(logtest1_correct, logtest1))
print('correct={} got={}\n'.format(logtest2_correct, logtest2))
assert np.isclose(logtest1_correct, logtest1)
assert np.isclose(logtest2_correct, logtest2)