"""
This Python script, inertDoubletModel.py,
implements an extension of the Standard Model by
an inert SU(2) doublet. This is a special case of the
Two Higgs Doublet Model.
The top quark and W-bosons are out of equilibrium, and
QCD and weak interactions are considered in the collisions.
Features:
- Definition of the extended model parameters including the inert doublet.
- Definition of the out-of-equilibrium particles.
- Implementation of the one-loop thermal potential, without high-T expansion.
Usage:
- This script is intended to compute the wall speed of the model.
Dependencies:
- NumPy for numerical calculations
- the WallGo package
- CollisionIntegrals in read-only mode using the default path for the collision
integrals as the "CollisonOutput" directory
Note:
This benchmark model was used to compare against the results of
S. Jiang, F. Peng Huang, and X. Wang, Bubble wall velocity during electroweak
phase transition in the inert doublet model, Phys.Rev.D 107 (2023) 9, 095005
doi:10.1103/PhysRevD.107.095005
"""
import sys
import pathlib
from typing import TYPE_CHECKING
import numpy as np
# WallGo imports
import WallGo # Whole package, in particular we get WallGo._initializeInternal()
from WallGo import Fields, GenericModel, Particle
from WallGo.PotentialTools import EffectivePotentialNoResum, EImaginaryOption
# Add the Models folder to the path; need to import the base example
# template
modelsBaseDir = pathlib.Path(__file__).resolve().parent.parent
sys.path.append(str(modelsBaseDir))
from wallGoExampleBase import WallGoExampleBase # pylint: disable=C0411, C0413, E0401
from wallGoExampleBase import ExampleInputPoint
if TYPE_CHECKING:
import WallGoCollision
# Inert doublet model, as implemented in 2211.13142
class InertDoubletModel(GenericModel):
r"""
Inert doublet model.
The tree-level potential is given by
V = msq |phi|^2 + msq2 |eta|^2 + lambda |phi|^4 + lambda2 |eta|^4
+ lambda3 |phi|^2 |eta|^2 + lambda4 |phi^dagger eta|^2
+ (lambda5 (phi^dagger eta)^2 +h.c.)
Note that there are some differences in normalization compared to
Jiang, Peng Huang, and Wang.
Only the Higgs field undergoes the phase transition, the new scalars only
modify the effective potential.
This class inherits from the GenericModel class and implements the necessary
methods for the WallGo package.
"""
# ~
def __init__(self) -> None:
"""
Initialize the InertDoubletModel.
"""
self.modelParameters: dict[str, float] = {}
# Initialize internal effective potential
self.effectivePotential = EffectivePotentialIDM(self)
# Create a list of particles relevant for the Boltzmann equations
self.defineParticles()
# ~ GenericModel interface
@property
def fieldCount(self) -> int:
"""How many classical background fields"""
return 1
def getEffectivePotential(self) -> "EffectivePotentialIDM":
return self.effectivePotential
# ~
def defineParticles(self) -> None:
"""
Define the particles for the model.
Note that the particle list only needs to contain the
particles that are relevant for the Boltzmann equations.
The particles relevant to the effective potential are
included independently.
Parameters
----------
None
Returns
----------
None
"""
self.clearParticles()
## === Top quark ===
# The msqVacuum function of an out-of-equilibrium particle must take
# a Fields object and return an array of length equal to the number of
# points in fields.
def topMsqVacuum(fields: Fields) -> Fields:
return 0.5 * self.modelParameters["yt"] ** 2 * fields.getField(0) ** 2
# The msqDerivative function of an out-of-equilibrium particle must take
# a Fields object and return an array with the same shape as fields.
def topMsqDerivative(fields: Fields) -> Fields:
return self.modelParameters["yt"] ** 2 * fields.getField(0)
topQuarkL = Particle(
name="TopL",
index=0,
msqVacuum=topMsqVacuum,
msqDerivative=topMsqDerivative,
statistics="Fermion",
totalDOFs=6,
)
self.addParticle(topQuarkL)
topQuarkR = Particle(
name="TopR",
index=1,
msqVacuum=topMsqVacuum,
msqDerivative=topMsqDerivative,
statistics="Fermion",
totalDOFs=6,
)
self.addParticle(topQuarkR)
## === SU(2) gauge boson ===
def WMsqVacuum(fields: Fields) -> Fields: # pylint: disable=invalid-name
return self.modelParameters["g2"] ** 2 * fields.getField(0) ** 2 / 4
def WMsqDerivative(fields: Fields) -> Fields: # pylint: disable=invalid-name
return self.modelParameters["g2"] ** 2 * fields.getField(0) / 2
wBoson = Particle(
name="W",
index=4,
msqVacuum=WMsqVacuum,
msqDerivative=WMsqDerivative,
statistics="Boson",
totalDOFs=9,
)
self.addParticle(wBoson)
## === A, Hpm scalars ===
def heavyScalarMsqVacuum(fields: Fields) -> Fields:
return self.modelParameters["msq2"] + self.modelParameters["lambda3"]*fields.getField(0)**2 / 2
def heavyScalarMsqDerivative(fields: Fields) -> Fields:
return self.modelParameters["lambda3"]*fields.getField(0)
heavyScalar = Particle(
name="A",
index=6,
msqVacuum=heavyScalarMsqVacuum,
msqDerivative=heavyScalarMsqDerivative,
statistics="Boson",
totalDOFs=3,
)
self.addParticle(heavyScalar)
## Go from whatever input params --> action params
def calculateLagrangianParameters(
self, inputParameters: dict[str, float]
) -> dict[str, float]:
"""
Calculate the model parameters based on the input parameters.
Parameters
----------
inputParameters: dict[str, float]
A dictionary of input parameters for the model.
Returns
----------
modelParameters: dict[str, float]
A dictionary of calculated model parameters.
"""
modelParameters = {}
# Zero-tempreature Higgs vev
v0 = inputParameters["v0"]
modelParameters["v0"] = v0
modelParameters["vevCollisions"] = inputParameters["vevCollisions"]
# Higgs parameters
massh = inputParameters["mh"]
modelParameters["lambda"] = 0.5 * massh**2 / v0**2
modelParameters["msq"] = -modelParameters["lambda"] * v0**2
## Then the Yukawa sector
massT = inputParameters["Mt"]
modelParameters["yt"] = np.sqrt(2.0) * massT / v0
## Then the inert doublet parameters
massH = inputParameters["mH"]
massA = inputParameters["mA"]
massHp = inputParameters["mHp"]
lambda5 = (massH**2 - massA**2) / v0**2
lambda4 = -2 * (massHp**2 - massA**2) / v0**2 + lambda5
lambda3 = 2 * inputParameters["lambdaL"] - lambda4 - lambda5
msq2 = massHp**2 - lambda3 * v0**2 / 2
modelParameters["msq2"] = msq2
modelParameters["lambda3"] = lambda3
modelParameters["lambda4"] = lambda4
modelParameters["lambda5"] = lambda5
## Some couplings are input parameters
modelParameters["g1"] = inputParameters["g1"]
modelParameters["g2"] = inputParameters["g2"]
modelParameters["g3"] = inputParameters["g3"]
modelParameters["lambda2"] = inputParameters["lambda2"]
modelParameters["lambdaL"] = inputParameters["lambdaL"]
return modelParameters
def updateModel(self, newInputParams: dict[str, float]) -> None:
"""Computes new Lagrangian parameters from given input and caches
them internally. These changes automatically propagate to the
associated EffectivePotential, particle masses etc.
"""
newParams = self.calculateLagrangianParameters(newInputParams)
# Copy to the model dict, do NOT replace the reference.
# This way the changes propagate to Veff and particles
self.modelParameters.update(newParams)
class EffectivePotentialIDM(EffectivePotentialNoResum):
"""
Effective potential for the InertDoubletModel.
This class inherits from the EffectivePotentialNoResum class and provides the
necessary methods for calculating the effective potential.
For this benchmark model we use the 4D potential without high-temperature expansion.
"""
# ~ EffectivePotential interface
fieldCount = 1
"""How many classical background fields"""
effectivePotentialError = 1e-8
"""
Relative accuracy at which the potential can be computed. Here it is set by the
error tolerance of the thermal integrals Jf/Jb.
"""
def __init__(self, owningModel: InertDoubletModel):
"""
Initialize the EffectivePotentialIDM.
"""
super().__init__(
integrals=None,
useDefaultInterpolation=True,
imaginaryOption=EImaginaryOption.PRINCIPAL_PART,
)
assert owningModel is not None, "Invalid model passed to Veff"
self.owner = owningModel
self.modelParameters = self.owner.modelParameters
# Count particle degrees-of-freedom to facilitate inclusion of light particle
# contributions to ideal gas pressure
self.numBosonDof = 32
self.numFermionDof = 90
def evaluate(
self, fields: Fields, temperature: float
) -> float | np.ndarray:
"""
Evaluate the effective potential.
Parameters
----------
fields: Fields
The field configuration
temperature: float
The temperature
Returns
----------
potentialTotal: complex | np.ndarray
The value of the effective potential
"""
# For this benchmark we don't use the high-T approximation in the evaluation of
# the one-loop thermal potential. We do use daisy-resummed masses.
# The RG-scale in the CW-potential is given by the zero-temperature mass
# of the relevant particle.
# phi ~ 1/sqrt(2) (0, v)
fields = Fields(fields)
v = fields.getField(0)
msq = self.modelParameters["msq"]
lam = self.modelParameters["lambda"]
# tree level potential
potentialTree = 0.5 * msq * v**2 + 0.25 * lam * v**4
# Particle masses and coefficients for the CW potential
bosonInformation = self.bosonInformation(fields)
fermionInformation = self.fermionInformation(fields)
# Particle masses and coefficients for the one-loop thermal potential
bosonInformationResummed = self.bosonInformationResummed(fields, temperature)
potentialTotal = (
potentialTree
+ self.constantTerms(temperature)
+ self.potentialOneLoop(bosonInformation, fermionInformation)
+ self.potentialOneLoopThermal(
bosonInformationResummed, fermionInformation, temperature
)
)
return np.array(potentialTotal)
def jCW(
self,
massSq: np.ndarray,
degreesOfFreedom: int | np.ndarray,
c: float | np.ndarray,
rgScale: float | np.ndarray,
) -> float | np.ndarray:
"""
One-loop Coleman-Weinberg contribution to the effective potential,
as implemented in Jiang, Peng Huang, and Wang.
Parameters
----------
msq : array_like
A list of the boson particle masses at each input point `X`.
degreesOfFreedom : float or array_like
The number of degrees of freedom for each particle. If an array
(i.e., different particles have different d.o.f.), it should have
length `Ndim`.
c: float or array_like
A constant used in the one-loop zero-temperature effective
potential. If an array, it should have length `Ndim`. Generally
`c = 1/2` for gauge boson transverse modes, and `c = 3/2` for all
other bosons.
rgScale : float or array_like
Renormalization scale in the one-loop zero-temperature effective
potential. If an array, it should have length `Ndim`. Typically, one
takes the same rgScale for all particles, but different scales
for each particle are possible.
Returns
-------
jCW : float or array_like
One-loop Coleman-Weinberg potential for given particle spectrum.
"""
# Note that we are taking the absolute value of the mass in the log here,
# instead of using EImaginaryOption = ABS_ARGUMENT, because we do not
# want the absolute value in the product of massSq and rgScale
return degreesOfFreedom*np.array(
massSq * massSq * (np.log(np.abs(massSq / rgScale**2)) - c)
+ 2 * massSq * rgScale**2
) / (64 * np.pi * np.pi)
def fermionInformation(self, fields: Fields) -> tuple[
np.ndarray,
float | np.ndarray,
float | np.ndarray,
float | np.ndarray,
]:
"""
Computes parameters for the one-loop potential (Coleman-Weinberg and thermal).
Parameters
----------
fields: Fields
The field configuration
Returns
----------
massSq: array_like
A list of the fermion particle masses at each input point `field`.
degreesOfFreedom: array_like
The number of degrees of freedom for each particle.
c: array_like
A constant used in the one-loop effective potential
rgScale : array_like
Renormalization scale in the one-loop zero-temperature effective
potential
"""
v = fields.getField(0)
# Just top quark, others are taken massless
yt = self.modelParameters["yt"]
mtsq = yt**2 * v**2 / 2 + 1e-100
mtsq0T = yt**2 * self.modelParameters["v0"] ** 2 / 2
massSq = np.stack((mtsq,), axis=-1)
massSq0T = np.stack((mtsq0T,), axis=-1)
degreesOfFreedom = np.array([12])
return massSq, degreesOfFreedom, 3 / 2, np.sqrt(massSq0T)
def bosonInformation( # pylint: disable=too-many-locals
self, fields: Fields
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""
Computes parameters for the one-loop potential (Coleman-Weinberg).
Parameters
----------
fields: Fields
The field configuration
Returns
----------
massSq: array_like
A list of the boson particle masses at each input point `field`.
degreesOfFreedom: array_like
The number of degrees of freedom for each particle.
c: array_like
A constant used in the one-loop effective potential
rgScale : array_like
Renormalization scale in the one-loop zero-temperature effective
potential
"""
v = fields.getField(0)
v0 = self.modelParameters["v0"]
msq = self.modelParameters["msq"]
lam = self.modelParameters["lambda"]
msq2 = self.modelParameters["msq2"]
lam3 = self.modelParameters["lambda3"]
lam4 = self.modelParameters["lambda4"]
lam5 = self.modelParameters["lambda5"]
g1 = self.modelParameters["g1"]
g2 = self.modelParameters["g2"]
# Scalar masses
mhsq = msq + 3 * lam * v**2
mHsq = msq2 + (lam3 + lam4 + lam5) / 2 * v**2
mAsq = msq2 + (lam3 + lam4 - lam5) / 2 * v**2
mHpmsq = msq2 + lam3 / 2 * v**2
# Scalar masses at the zero-temperature vev (for RG-scale)
mhsq0T = msq + 3 * lam * v0**2
mHsq0T = msq2 + (lam3 + lam4 + lam5) / 2 * v0**2
mAsq0T = msq2 + (lam3 + lam4 - lam5) / 2 * v0**2
mHpmsq0T = msq2 + lam3 / 2 * v0**2
# Gauge boson masses
mWsq = g2**2 * v**2 / 4.0 + 1e-100
mZsq = (g1**2 + g2**2) * v**2 / 4.0 + 1e-100
# Gauge boson masses at the zero temperature vev (for RG-scale)
mWsq0T = g2**2 * v0**2 / 4.0
mZsq0T = (g1**2 + g2**2) * v0**2 / 4.0
# W, Z, h, H, A, Hpm
massSq = np.column_stack((mWsq, mZsq, mhsq, mHsq, mAsq, mHpmsq))
massSq0 = np.column_stack((mWsq0T, mZsq0T, mhsq0T, mHsq0T, mAsq0T, mHpmsq0T))
degreesOfFreedom = np.array([6, 3, 1, 1, 1, 2])
c = 3 / 2 * np.ones(6)
return massSq, degreesOfFreedom, c, np.sqrt(massSq0)
def bosonInformationResummed( # pylint: disable=too-many-locals
self, fields: Fields, temperature: float | np.ndarray
) -> tuple[np.ndarray, np.ndarray | float, np.ndarray | float, np.ndarray | float]:
"""
Computes parameters for the thermal one-loop potential.
Parameters
----------
fields: Fields
The field configuration
Returns
----------
massSq: array_like
A list of the boson particle masses at each input point `field`.
degreesOfFreedom: array_like
The number of degrees of freedom for each particle.
c: array_like
A constant used in the one-loop effective potential
rgScale : array_like
Renormalization scale in the one-loop zero-temperature effective
potential
"""
v = fields.getField(0)
msq = self.modelParameters["msq"]
lam = self.modelParameters["lambda"]
msq2 = self.modelParameters["msq2"]
lam2 = self.modelParameters["lambda2"]
lam3 = self.modelParameters["lambda3"]
lam4 = self.modelParameters["lambda4"]
lam5 = self.modelParameters["lambda5"]
yt = self.modelParameters["yt"]
g1 = self.modelParameters["g1"]
g2 = self.modelParameters["g2"]
# Thermal masses of the scalars
piPhi = (
temperature**2
/ 12.0
* (6 * lam + 2 * lam3 + lam4 + 3 / 4 * (3 * g2**2 + g1**2) + 3 * yt**2)
) # Eq. (15) of 2211.13142 (note the different normalization of lam)
piEta = (
temperature**2
/ 12.0
* (6 * lam2 + 2 * lam3 + lam4 + 3 / 4 * (3 * g2**2 + g1**2))
) # Eq. (16) of 2211.13142 (note the different normalization of lam2)
# Scalar masses including thermal contribution
# Need to take the absolute value because we can not
# use EImaginaryOption = ABS_ARGUMENT for the full potential
mhsq = np.abs(msq + 3 * lam * v**2 + piPhi)
mGsq = np.abs(msq + lam * v**2 + piPhi) # Goldstone bosons
mHsq = msq2 + (lam3 + lam4 + lam5) / 2 * v**2 + piEta
mAsq = msq2 + (lam3 + lam4 - lam5) / 2 * v**2 + piEta
mHpmsq = msq2 + lam3 / 2 * v**2 + piEta
# Gauge boson masses, with thermal contribution to longitudinal W mass
mWsq = g2**2 * v**2 / 4.0
mWsqL = g2**2 * v**2 / 4.0 + 2 * g2**2 * temperature**2
mZsq = (g1**2 + g2**2) * v**2 / 4.0
# Eigenvalues of the Z&B-boson mass matrix
piB = 2 * g1**2 * temperature**2
piW = 2 * g2**2 * temperature**2
m1sq = g1**2 * v**2 / 4
m2sq = g2**2 * v**2 / 4
m12sq = -g1 * g2 * v**2 / 4
msqEig1 = (
m1sq
+ m2sq
+ piB
+ piW
- np.sqrt(4 * m12sq**2 + (m2sq - m1sq - piB + piW) ** 2)
) / 2
msqEig2 = (
m1sq
+ m2sq
+ piB
+ piW
+ np.sqrt(4 * m12sq**2 + (m2sq - m1sq - piB + piW) ** 2)
) / 2
# HACK make sure the masses have the right shape
if mWsq.shape != mWsqL.shape:
mWsq = mWsq * np.ones(mWsqL.shape[0])
mZsq = mZsq * np.ones(mWsqL.shape[0])
# W, Wlong, Z,Zlong,photonLong, h, Goldstone H, A, Hpm
massSq = np.column_stack(
(mWsq, mWsqL, mZsq, msqEig1, msqEig2, mhsq, mGsq, mHsq, mAsq, mHpmsq)
)
degreesOfFreedom = np.array([4, 2, 2, 1, 1, 1, 3, 1, 1, 2])
# As c and the RG-scale don't enter in the one-loop effective potential,
# we just set them to 0
return massSq, degreesOfFreedom, 0.0, 0.0
def constantTerms(self, temperature: float | np.ndarray) -> float | np.ndarray:
"""Need to explicitly compute field-independent but T-dependent parts
that we don't already get from field-dependent loops. At leading order in
high-T expansion these are just (minus) the ideal gas pressure of light
particles that were not integrated over in the one-loop part.
See Eq. (39) in hep-ph/0510375 for general LO formula
Parameters
----------
temperature: array-like (float)
The temperature
Returns
----------
constantTerms: array-like (float)
The value of the field-independent contribution to the effective potential
"""
# How many degrees of freedom we have left. The number of DOFs
# that were included in evaluate() is hardcoded
dofsBoson = self.numBosonDof - 17
dofsFermion = self.numFermionDof - 12 ## we only did top quark loops
# Fermions contribute with a magic 7/8 prefactor as usual. Overall minus
# sign since Veff(min) = -pressure
return -(dofsBoson + 7.0 / 8.0 * dofsFermion) * np.pi**2 * temperature**4 / 90.0
class InertDoubletModelExample(WallGoExampleBase):
"""
Sets up the Inert doublet model, computes or loads the collison
integrals, and computes the wall velocity.
"""
def __init__(self) -> None:
""""""
self.bShouldRecalculateMatrixElements = False
self.bShouldRecalculateCollisions = False
self.matrixElementFile = pathlib.Path(
self.exampleBaseDirectory / "MatrixElements/matrixElements.idm.json"
)
# ~ Begin WallGoExampleBase interface
def getDefaultCollisionDirectory(self, momentumGridSize: int) -> pathlib.Path:
"""Returns the path to the directory with collisions"""
return super().getDefaultCollisionDirectory(momentumGridSize)
def initWallGoModel(self) -> "WallGo.GenericModel":
"""
Initialize the model. This should run after cmdline argument parsing
so safe to use them here.
"""
return InertDoubletModel()
def initCollisionModel(
self, wallGoModel: "InertDoubletModel"
) -> "WallGoCollision.PhysicsModel":
"""Initialize the Collision model and set the seed."""
import WallGoCollision # pylint: disable = C0415
# Collision integrations utilize Monte Carlo methods, so RNG is involved. We can
# set the global seed for collision integrals as follows.
# This is optional; by default the seed is 0.
WallGoCollision.setSeed(0)
# This example comes with a very explicit example function on how to setup and
# configure the collision module. It is located in a separate module (same
# directory) to avoid bloating this file. Import and use it here.
from exampleCollisionDefs import (
setupCollisionModel_IDM,
) # pylint: disable = C0415
collisionModel = setupCollisionModel_IDM(
wallGoModel.modelParameters,
)
return collisionModel
def updateCollisionModel(
self,
inWallGoModel: "InertDoubletModel",
inOutCollisionModel: "WallGoCollision.PhysicsModel",
) -> None:
"""Propagete changes in WallGo model to the collision model."""
import WallGoCollision # pylint: disable = C0415
changedParams = WallGoCollision.ModelParameters()
g3 = inWallGoModel.modelParameters["g3"] # names differ for historical reasons
gw = inWallGoModel.modelParameters["g2"] # names differ for historical reasons
g1 = inWallGoModel.modelParameters["g1"]
yt = inWallGoModel.modelParameters["yt"]
lam1H = inWallGoModel.modelParameters["lambda"]
lam3H = inWallGoModel.modelParameters["lambda3"]
lam4H = inWallGoModel.modelParameters["lambda4"]
v = inWallGoModel.modelParameters["vevCollisions"]
changedParams.add("g3", g3)
changedParams.add("gw", gw)
changedParams.add("g1", g1)
changedParams.add("yt1",yt)
changedParams.add("lam1H", lam1H)
changedParams.add("lam3H", lam3H)
changedParams.add("lam4H", lam4H)
changedParams.add("v",v)
# The values for the quark, gluon, W and A are taken from 2211.13142
# For the Higgs and Goldstone we take the thermal mass
changedParams.add(
"mq2", g3**2 / 6.0
) # quark thermal mass^2 in units of T
changedParams.add(
"ml2", 3*gw**2 / 32.0
) # lepton thermal mass^2 in units of T
changedParams.add(
"mg2", 2.0 * g3**2
) # gluon thermal mass^2 in units of T
changedParams.add(
"mw2", 11.0 * gw**2 / 6.0
) # W boson thermal mass^2 in units of T
changedParams.add(
"mh2", (6* lam1H+ 2 * lam3H +lam4H+ 3*(3 * gw ** 2 + g1**2) / 4 + 3 *yt**2) / 12.0
) # Higgs and Goldstone thermal mass^2 in units of T
changedParams.add(
"mH2", lam3H / 24.0
) # H thermal mass^2 in units of T
changedParams.add(
"mA2", lam3H / 24.0
) # A thermal mass^2 in units of T
inOutCollisionModel.updateParameters(changedParams)
def configureCollisionIntegration(
self, inOutCollisionTensor: "WallGoCollision.CollisionTensor"
) -> None:
"""Non-abstract override"""
import WallGoCollision # pylint: disable = C0415
"""Configure the integrator. Default settings should be reasonably OK
so you can modify only what you need, or skip this step entirely.
Here we set everything manually to show how it's done.
"""
integrationOptions = WallGoCollision.IntegrationOptions()
integrationOptions.calls = 50000
integrationOptions.maxTries = 10
# collision integration momentum goes from 0 to maxIntegrationMomentum.
# This is in units of temperature
integrationOptions.maxIntegrationMomentum = 20
integrationOptions.absoluteErrorGoal = 1e-5
integrationOptions.relativeErrorGoal = 1e-1
inOutCollisionTensor.setIntegrationOptions(integrationOptions)
"""We can also configure various verbosity settings that are useful when
you want to see what is going on in long-running integrations. These include
progress reporting and time estimates, as well as a full result dump of each
individual integral to stdout. By default these are all disabled. Here we
enable some for demonstration purposes.
"""
verbosity = WallGoCollision.CollisionTensorVerbosity()
verbosity.bPrintElapsedTime = (
True # report total time when finished with all integrals
)
"""Progress report when this percentage of total integrals (approximately
have been computed. Note that this percentage is per-particle-pair, ie.
each (particle1, particle2) pair reports when this percentage of their own
integrals is done. Note also that in multithreaded runs the progress tracking
is less precise.
"""
verbosity.progressReportPercentage = 0.25
# Print every integral result to stdout? This is very slow and verbose,
# intended only for debugging purposes
verbosity.bPrintEveryElement = False
inOutCollisionTensor.setIntegrationVerbosity(verbosity)
def configureManager(self, inOutManager: "WallGo.WallGoManager") -> None:
inOutManager.config.loadConfigFromFile(
pathlib.Path(self.exampleBaseDirectory / "inertDoubletModelConfig.ini")
)
super().configureManager(inOutManager)
def updateModelParameters(
self, model: "InertDoubletModel", inputParameters: dict[str, float]
) -> None:
"""Convert Inert Doublet Model inputs to Lagrangian params and update
internal model parameters. This example is constructed so that the
effective potential and particle mass functions refer to model.modelParameters,
so be careful not to replace that reference here.
"""
oldParams = model.modelParameters # pylint: disable = W0612
model.updateModel(inputParameters)
newParams = model.modelParameters # pylint: disable = W0612
"""Collisions integrals for this example depend on the QCD and Electroweak
coupling, if it changes we must recompute collisions before running the
wall solver. The bool flag here is inherited from WallGoExampleBase and
checked in runExample(). But since we want to keep the example simple, we
skip this check and assume the existing data is OK.
(FIXME?)
"""
self.bNeedsNewCollisions = False # pylint: disable = W0201
"""
if (
not oldParams
or newParams["g3"] != oldParams["g3"]
or newParams["g2"] != oldParams["g2"]
):
self.bNeedsNewCollisions = True
"""
def getBenchmarkPoints(self) -> list[ExampleInputPoint]:
"""
Input parameters, phase info, and settings for the effective potential and
wall solver for the inert doublet model benchmark point.
"""
output: list[ExampleInputPoint] = []
output.append(
ExampleInputPoint(
{
"v0": 246.22,
# This is hardcoded to be half of the vev at the nucleation temperature of BMA
"vevCollisions": 74.8354/117.1,
"Mt": 172.76,
"g1": 0.35,
"g2": 0.65,
"g3": 1.2279920495357861,
"lambda2": 0.1,
"lambdaL": 0.0015,
"mh": 125.0,
"mH": 62.66,
"mA": 300.0,
"mHp": 300.0,
# We don't use mHm as input parameter, as it is equal to mHp
},
WallGo.PhaseInfo(
temperature=117.1,
phaseLocation1=WallGo.Fields([0.0]),
phaseLocation2=WallGo.Fields([246.0]),
),
WallGo.VeffDerivativeSettings(temperatureVariationScale=1.0, fieldValueVariationScale=[10.0]),
WallGo.WallSolverSettings(
# we actually do both cases in the common example
bIncludeOffEquilibrium=True,
meanFreePathScale=50.0, # In units of 1/Tnucl
wallThicknessGuess=5.0, # In units of 1/Tnucl
),
)
)
return output
# ~ End WallGoExampleBase interface
if __name__ == "__main__":
example = InertDoubletModelExample()
example.runExample()