def second_generation_mass_spin(
dataset,
alpha,
beta,
mmin,
mmax,
lam,
mpp,
sigpp,
):
second_generation_mass = two_component_primary_mass_ratio(
dataset=dataset,
alpha=alpha,
beta=beta * 4,
mmin=mmin * 2,
mmax=mmax * 2,
lam=lam,
mpp=mpp * 2,
sigpp=sigpp * 2,
)
alpha_2g, beta_2g, _ = mu_chi_var_chi_max_to_alpha_beta_max(mu_chi=0.67,
var_chi=0.01,
amax=1)
second_generation_spin = iid_spin_magnitude_beta(dataset=dataset,
alpha_chi=alpha_2g,
beta_chi=beta_2g,
amax=1)
return second_generation_mass * second_generation_spin
def test_two_component_primary_mass_ratio_zero_below_mmin(self):
m2s = self.dataset["mass_1"] * self.dataset["mass_ratio"]
for ii in range(self.n_test):
parameters = self.power_prior.sample()
parameters.update(self.gauss_prior.sample())
p_m = mass.two_component_primary_mass_ratio(self.dataset, **parameters)
self.assertEqual(xp.max(p_m[m2s <= parameters["mmin"]]), 0.0)
def test_single_peak_delta_m_zero_matches_two_component_primary_mass_ratio(self):
max_diffs = list()
for ii in range(self.n_test):
parameters = self.power_prior.sample()
parameters.update(self.gauss_prior.sample())
p_m1 = mass.two_component_primary_mass_ratio(self.dataset, **parameters)
parameters["delta_m"] = 0
p_m2 = mass.SinglePeakSmoothedMassDistribution()(self.dataset, **parameters)
max_diffs.append(_max_abs_difference(p_m1, p_m2))
self.assertAlmostEqual(max(max_diffs), 0.0)
def first_generation_mass_ratio(self, alpha, beta, mmin, mmax, lam, mpp,
sigpp):
first_generation_mass = two_component_primary_mass_ratio(
dataset=self.first_generation_data,
alpha=alpha,
beta=beta,
mmin=mmin,
mmax=mmax,
lam=lam,
mpp=mpp,
sigpp=sigpp,
)
return trapz(first_generation_mass, self.mass_1s, axis=0)
def first_generation_mass_ratio(self, alpha, beta, mmin, mmax, lam, mpp,
sigpp):
grid_shift = (mmin - self.mass_1s[0] - self.dm / 2) % self.dm
self.first_generation_data['mass_1'] += grid_shift
first_generation_mass = two_component_primary_mass_ratio(
dataset=self.first_generation_data,
alpha=alpha,
beta=beta,
mmin=mmin,
mmax=mmax,
lam=lam,
mpp=mpp,
sigpp=sigpp,
)
self.first_generation_data['mass_1'] -= grid_shift
return trapz(first_generation_mass, self.mass_1s, axis=0)
def first_generation_mass_spin_big_grid(
dataset,
alpha,
beta,
mmin,
mmax,
lam,
mpp,
sigpp,
alpha_chi,
beta_chi,
delta_chi,
):
first_generation_mass = two_component_primary_mass_ratio(
dataset=dataset,
alpha=alpha,
beta=beta,
mmin=mmin,
mmax=mmax,
lam=lam,
mpp=mpp,
sigpp=sigpp,
)
first_generation_spin = first_generation_spin_magnitude_big_grid(
dataset["a_1"],
alpha=alpha_chi,
beta=beta_chi,
delta=delta_chi,
a_max=1,
spin_array=dataset['a_1']
[:, 0, 0, 0]) * first_generation_spin_magnitude_big_grid(
dataset["a_2"],
alpha=alpha_chi,
beta=beta_chi,
delta=delta_chi,
a_max=1,
spin_array=dataset['a_2'][0, :, 0, 0])
return first_generation_mass * first_generation_spin
def first_generation_mass_spin(
dataset,
alpha,
beta,
mmin,
mmax,
lam,
mpp,
sigpp,
alpha_chi,
beta_chi,
delta_chi,
):
first_generation_mass = two_component_primary_mass_ratio(
dataset=dataset,
alpha=alpha,
beta=beta,
mmin=mmin,
mmax=mmax,
lam=lam,
mpp=mpp,
sigpp=sigpp,
)
first_generation_spin = first_generation_spin_magnitude(
dataset["a_1"],
alpha=alpha_chi,
beta=beta_chi,
delta=delta_chi,
a_max=1,
) * first_generation_spin_magnitude(
dataset["a_2"],
alpha=alpha_chi,
beta=beta_chi,
delta=delta_chi,
a_max=1,
)
return first_generation_mass * first_generation_spin
def two_component_primary_mass_ratio_dynamical_without_spins(
dataset,
alpha,
beta,
mmin,
mmax,
lam,
mpp,
sigpp,
branch_1=0.12,
branch_2=0.01,
):
"""
Power law model for two-dimensional mass distribution, modelling primary
mass and conditional mass ratio distribution.
p(m1, q) = p(m1) * p(q | m1)
We also include the effect of dynamical mergers leading to 1.5 and 2nd
generation mergers.
Parameters
----------
dataset: dict
Dictionary of numpy arrays for 'mass_1' and 'mass_ratio'.
alpha: float
Negative power law exponent for more massive black hole.
mmin: float
Minimum black hole mass.
mmax: float
Maximum black hole mass.
beta: float
Power law exponent of the mass ratio distribution.
lam: float
Fraction of black holes in the Gaussian component.
mpp: float
Mean of the Gaussian component.
sigpp: float
Standard deviation fo the Gaussian component.
branch_1: float
Ratio of 1.5 generation mergers to 1st generation mergers.
The default value comes from a conversation with Eric Thrane.
branch_2: float
Ratio of 2nd generation mergers to 1st generation mergers.
The default value comes from a conversation with Eric Thrane.
"""
btot = 1 + branch_1 + branch_2
fraction_1_5 = branch_1 / btot
fraction_2 = branch_2 / btot
fraction_1 = 1 - fraction_1_5 - fraction_2
if fraction_1 < 0:
return np.zeros_like(dataset["mass_1"])
# assert branch_0 >= 0, "Branching fractions greater than 1."
first_generation = two_component_primary_mass_ratio(
dataset=dataset,
alpha=alpha,
beta=beta,
mmin=mmin,
mmax=mmax,
lam=lam,
mpp=mpp,
sigpp=sigpp,
)
params = dict(mmin=mmin * 2,
mmax=mmax * 2,
lam=lam,
mpp=mpp * 2,
sigpp=sigpp * 2)
one_point_five_generation = two_component_single(
dataset["mass_1"], alpha=alpha, **
params) * one_point_five_generation_mass_ratio(
dataset, spectal_index=beta * 1.5, mmin=mmin)
second_generation = two_component_primary_mass_ratio(
dataset=dataset,
alpha=alpha,
beta=beta * 4,
mmin=mmin * 2,
mmax=mmax * 2,
lam=lam,
mpp=mpp * 2,
sigpp=sigpp * 2,
)
return (fraction_1 * first_generation +
fraction_1_5 * one_point_five_generation +
fraction_2 * second_generation)