def get_amp(self, data, data_c=None, **kwargs):
m = data["m"]
mass1 = self.mass1()
mass2 = self.mass2()
width1 = self.width1()
width2 = self.width2()
q = data_c["|q|"]
mdaughter1 = kwargs["all_data"]["particle"][self.decay[0].outs[0]]["m"]
mdaughter2 = kwargs["all_data"]["particle"][self.decay[0].outs[1]]["m"]
q1 = get_relative_p(mass1, mdaughter1, mdaughter2)
q2 = get_relative_p(mass2, mdaughter1, mdaughter2)
mlist = tf.stack([mass1, mass2])
wlist = tf.stack([width1, width2])
qlist = tf.stack([q1, q2])
Klist = []
for mi, wi, qi in zip(mlist, wlist, qlist):
rw = Gamma(m, wi, q, qi, self.bw_l, mi, self.d)
Klist.append(mi * rw / (mi**2 - m**2))
KK = tf.reduce_sum(Klist, axis=0)
KK += self.alpha()
beta_term = self.get_beta(
m=m,
mlist=mlist,
wlist=wlist,
q=q,
qlist=qlist,
Klist=Klist,
**kwargs,
)
MM = tf.complex(np.float64(1), -KK)
MM = beta_term / MM
return MM + self.KNR()
def spline_x_matrix(x, xi):
"""build matrix of x for spline interpolation"""
ones = tf.ones_like(x)
x2 = x * x
x3 = x2 * x
x_p = tf.stack([ones, x, x2, x3], axis=-1)
x = tf.expand_dims(x, axis=-1)
zeros = tf.zeros_like(x)
def poly_i(i):
cut = (x >= xi[i]) & (x < xi[i + 1])
return tf.where(cut, x_p, zeros)
xs = [poly_i(i) for i in range(len(xi) - 1)]
return tf.stack(xs, axis=-2)
def get_matrix_interp1d3_v2(x, xi):
N = len(xi) - 1
zeros = tf.zeros_like(x)
ones = tf.ones_like(x)
# @pysnooper.snoop()
def poly_i(i):
tmp = zeros
x_i = (xi[i] + xi[i - 1]) / 2
for j in range(i - 1, i + 3):
if j < 0 or j > N - 1:
continue
r = ones
for k in range(j - 1, j + 3):
if k == i or k < 1 or k > N:
continue
x_k = (xi[k] + xi[k - 1]) / 2
r = r * (x - x_k) / (x_i - x_k)
r = tf.where(
(x >= (xi[j] + xi[j - 1]) / 2) & (x < (xi[j] + xi[j + 1]) / 2),
r,
zeros,
)
tmp = tmp + r
return tmp
h = tf.stack([poly_i(i) for i in range(1, N)], axis=-1)
b = tf.zeros_like(x)
return h, b
def interp(self, m):
# q = data_extra[self.outs[0]]["|q|"]
# a = self.a()
p = self.point_value()
zeros = tf.zeros_like(m)
ones = tf.ones_like(m)
def poly_i(i, xi):
tmp = zeros
for j in range(i - 1, i + 1):
if j < 0 or j > self.interp_N - 1:
continue
r = ones
for k in range(j, j + 2):
if k == i:
continue
r = r * (m - xi[k]) / (xi[i] - xi[k])
r = tf.where((m >= xi[j]) & (m < xi[j + 1]), r, zeros)
tmp = tmp + r
return tmp
h = tf.stack(
[poly_i(i, self.points) for i in range(1, self.interp_N - 1)],
axis=-1,
)
h = tf.stop_gradient(h)
p_r = tf.math.real(p)
p_i = tf.math.imag(p)
ret_r = tf.reduce_sum(h * p_r, axis=-1)
ret_i = tf.reduce_sum(h * p_i, axis=-1)
return tf.complex(ret_r, ret_i)
def get_helicity_amp(self, data, data_p, **kwargs):
q0 = self.get_relative_momentum(data_p, False)
data["|q0|"] = q0
if "|q|" in data:
q = data["|q|"]
else:
q = self.get_relative_momentum(data_p, True)
data["|q|"] = q
bf = barrier_factor([min(self.get_l_list())], q, q0, self.d)
H = tf.stack(self.H())
bf = tf.cast(tf.reshape(bf, (-1, 1, 1)), H.dtype)
return H * bf
def get_helicity_amp(self, data, data_p, **kwargs):
n_b = len(self.outs[0].spins)
n_c = len(self.outs[1].spins)
H_part = tf.stack(self.H())
if self.part_H == 0:
H = tf.concat(
[H_part, self.parity_term * H_part[(n_b - 2) // 2::-1]],
axis=0,
)
else:
H = tf.concat(
[H_part, self.parity_term * H_part[:, (n_c - 2) // 2::-1]],
axis=1,
)
return H
def interp(self, m):
zeros = tf.zeros_like(m)
p = self.point_value()
xs = []
def poly_i(i):
x = 1.0
for j in range(self.interp_N):
if i == j:
continue
x = (x * (m - self.points[j]) /
(self.points[i] - self.points[j]))
return x
xs = tf.stack([poly_i(i) for i in range(self.interp_N)], axis=-1)
zeros = tf.zeros_like(xs)
xs = tf.complex(xs, zeros)
ret = tf.reduce_sum(xs[:, 1:-1] * p, axis=-1)
return ret
def get_matrix_interp1d3(x, xi):
N = len(xi) - 1
zeros = tf.zeros_like(x)
ones = tf.ones_like(x)
# @pysnooper.snoop()
def poly_i(i):
tmp = zeros
for j in range(i - 1, i + 3):
if j < 0 or j > N - 1:
continue
r = ones
for k in range(j - 1, j + 3):
if k == i or k < 0 or k > N:
continue
r = r * (x - xi[k]) / (xi[i] - xi[k])
r = tf.where((x >= xi[j]) & (x < xi[j + 1]), r, zeros)
tmp = tmp + r
return tmp
h = tf.stack([poly_i(i) for i in range(1, N)], axis=-1)
b = tf.zeros_like(x)
return h, b
def interp(self, m):
p = self.point_value()
ones = tf.ones_like(m)
zeros = tf.zeros_like(m)
def add_f(x, bl, br):
return tf.where((x > bl) & (x <= br), ones, zeros)
x_bin = tf.stack(
[
add_f(
m,
(self.points[i] + self.points[i + 1]) / 2,
(self.points[i + 1] + self.points[i + 2]) / 2,
) for i in range(self.interp_N - 2)
],
axis=-1,
)
p_r = tf.math.real(p)
p_i = tf.math.imag(p)
x_bin = tf.stop_gradient(x_bin)
ret_r = tf.reduce_sum(x_bin * p_r, axis=-1)
ret_i = tf.reduce_sum(x_bin * p_i, axis=-1)
return tf.complex(ret_r, ret_i)
def interp(self, m):
p = self.point_value()
ret = interp1d3(m, self.points, tf.stack(p))
return ret
def get_helicity_amp(self, data, data_p, **kwargs):
return tf.stack(self.H())