def test_elastic():
ref = 33.19098343826968
isclose(wr.rate_wimp_std(1, **opts), ref)
# Test numericalunits.reset_units() does not affect results
nu.reset_units(123)
isclose(wr.rate_wimp_std(1, **opts), ref)
# Test vectorized call
energies = np.linspace(0.01, 40, 100)
dr = wr.rate_wimp_std(energies, **opts)
assert dr[0] == wr.rate_wimp_std(0.01, **opts)
def test_halo_scaling():
#check that passing rho multiplies the rate correctly:
ref = 33.19098343826968
isclose(
wr.rate_wimp_std(1,
halo_model=wr.StandardHaloModel(rho_dm=0.3 * nu.GeV /
nu.c0**2 / nu.cm**3),
**opts), ref)
def __init__(self, *args, wimp_kwargs=None, **kwargs):
# Compute the energy spectrum in a given time range
# Times used by wimprates are J2000 timestamps
assert self.n_time_bins >= 1, "Need >= 1 time bin"
if hasattr(self, 'n_in'):
raise RuntimeError(
"n_in is gone! Use n_time_bins to control accuracy, or set "
"pretend_wimps_dont_modulate to use a time-averaged spectrum.")
times = np.linspace(wr.j2000(self.t_start.value),
wr.j2000(self.t_stop.value), self.n_time_bins + 1)
time_centers = self.bin_centers(times)
if wimp_kwargs is None:
# No arguments given at all;
# use default mass, xsec and energy range
wimp_kwargs = dict(mw=self.mw,
sigma_nucleon=self.sigma_nucleon,
es=self.es)
else:
assert 'mw' in wimp_kwargs and 'sigma_nucleon' in wimp_kwargs, \
"Pass at least 'mw' and 'sigma_nucleon' in wimp_kwargs"
if 'es' not in wimp_kwargs:
# Energies not given, use default energy bin edges
wimp_kwargs['es'] = self.es
es = wimp_kwargs['es']
es_centers = self.bin_centers(es)
del wimp_kwargs['es'] # To avoid confusion centers / edges
# Transform wimp_kwargs to arguments that can be passed to wimprates
# which means transforming es from edges to centers
spectra = np.array([
wr.rate_wimp_std(t=t, es=es_centers, **wimp_kwargs) * np.diff(es)
for t in time_centers
])
assert spectra.shape == (len(time_centers), len(es_centers))
self.energy_hist = Histdd.from_histogram(spectra,
bin_edges=(times, es))
if self.pretend_wimps_dont_modulate:
self.energy_hist.histogram = (
np.ones_like(self.energy_hist.histogram) *
self.energy_hist.sum(axis=0).histogram.reshape(1, -1) /
self.n_time_bins)
# Initialize the rest of the source, needs to be after energy_hist is
# computed because of _populate_tensor_cache
super().__init__(*args, **kwargs)
def __init__(self, *args, wimp_kwargs=None, **kwargs):
# Compute the energy spectrum in a given time range
# Times used by wimprates are J2000 timestamps
assert self.n_in > 1, \
f"Number of time bin edges needs to be at least 2"
times = np.linspace(wr.j2000(date=self.t_start),
wr.j2000(date=self.t_stop), self.n_in)
time_centers = self.bin_centers(times)
if wimp_kwargs is None:
# No arguments given at all;
# use default mass, xsec and energy range
wimp_kwargs = dict(mw=self.mw,
sigma_nucleon=self.sigma_nucleon,
es=self.es)
else:
assert 'mw' in wimp_kwargs and 'sigma_nucleon' in wimp_kwargs, \
"Pass at least 'mw' and 'sigma_nucleon' in wimp_kwargs"
if 'es' not in wimp_kwargs:
# Energies not given, use default energy bin edges
wimp_kwargs['es'] = self.es
es = wimp_kwargs['es']
es_centers = self.bin_centers(es)
del wimp_kwargs['es'] # To avoid confusion centers / edges
# Transform wimp_kwargs to arguments that can be passed to wimprates
# which means transforming es from edges to centers
spectra = np.array([
wr.rate_wimp_std(t=t, es=es_centers, **wimp_kwargs) * np.diff(es)
for t in time_centers
])
assert spectra.shape == (len(time_centers), len(es_centers))
self.energy_hist = Histdd.from_histogram(spectra,
bin_edges=(times, es))
# Initialize the rest of the source, needs to be after energy_hist is
# computed because of _populate_tensor_cache
super().__init__(*args, **kwargs)
def test_brems():
isclose(wr.rate_wimp_std(1, detection_mechanism='bremsstrahlung', **opts),
0.00017062652972332665)
def test_migdal():
isclose(wr.rate_wimp_std(1, detection_mechanism='migdal', **opts),
0.2610240963512907)
def test_spindependent():
isclose(wr.rate_wimp_std(1, interaction='SD_n_central', **opts),
0.00021779266679860948)
def test_lightmediator():
isclose(wr.rate_wimp_std(1, m_med=1e-3, **opts), 0.0005502663384403058)
This file is adapted from the laidbax tutorial notebook - https://github.com/XENON1T/laidbax/blob/master/notebooks/Tutorial.ipynb
For a WIMP with mass 500 GeV/c^2 and cross section 1e-45
"""
import numpy as np
import random
import matplotlib.pyplot as plt
import wimprates
import numericalunits as nu
import blueice as bi
from laidbax import base_model
import pandas as pd
energies = np.linspace(0.01, 100, 100)
dr = wimprates.rate_wimp_std(energies, mw=500,
sigma_nucleon=1e-45) / (1000 * 365)
plt.plot(energies, dr)
plt.xlabel("Recoil energy [keV]")
plt.ylabel("Rate [events per (keV kg day)]")
plt.title("$m_\chi = 500$ GeV/c${}^2$, $\sigma_\chi = 10^{-45}$ cm${}^2$")
plt.xlim(0, energies.max())
plt.ylim(0, None)
plt.show()
#m = bi.Model(base_model.config)
source_config = {
'energy_distribution': (energies, dr),
'name': 'WIMP',
'label': 'WIMP particles',
'recoil_type': 'nr',