if cfg.getboolean('list','listmode')==False:
#parameter/prior scan settings set here:
uselog = cfg.getboolean('prioroptions','uselog') #whether to use linear or log priors for mass parameters
massprior = priors.logprior if uselog else priors.linear #choose the mass parameter prior
#get ranges for scan (must be tuples of length 2, i.e. (min,max))
M0range = cfg.get2tuple('prioroptions','M0range')
M12range = cfg.get2tuple('prioroptions','M12range')
TanBrange = cfg.get2tuple('prioroptions','TanBrange')
A0range = cfg.get2tuple('prioroptions','A0range')
#nuis par tuple values interpreted as mean and std of gaussian prior
Mtopmeanstd = cfg.get2tuple('prioroptions','Mtopmeanstd')
priorslist = [ ('M0', massprior(*M0range)),
('M12', massprior(*M12range)),
('TanB', priors.linear(*TanBrange)),
('A0', priors.linear(*A0range)),
# NOTE: Was using MZ too, but currently ISAJET will ignore everything except Mtop.
#('ialphaem', priors.gaussian(127.918,0.018)), #Jun 2009 hep-ph/0906.0957v2, they reference PDG 2008, but I can't find the value myself.
#('alphas', priors.gaussian(0.1184,0.0007)), #PDG 2011 - Physical Constants
#('MZ', priors.gaussian(91.1876,0.0021)), #PDG 2011 - Physical Constants
#('Mbot', priors.gaussian(4.19,0.18)), #PDG 2011 quark summary - http://pdg.lbl.gov/2010/tables/rpp2010-sum-quarks.pdf (NOTE - lower uncertainty is 0.06 not 0.18, altered distribution to make it symmetric for simplicity)
('Mtop', priors.gaussian(*Mtopmeanstd)), #PDG 2011 quark summary - http://pdg.lbl.gov/2010/tables/rpp2010-sum-quarks.pdf
]
#=================SET PRIOR=================================
#----------(to be used by PyScanner)---------------------------
self.scanargs['prior'] = priors.genindepprior(priorslist)
self.scanargs['parorder'] = ['M0','M12','TanB','A0','Mtop']
#==========LIKELIHOOD FUNCTION SETUP============================
def setupCNMSSMAk():
"""Same as CNMSSM, but with Akappa GUT unification broken, i.e.
Akappa is scanned independently"""
# 'whichcode' is used to initialise the nmspec wrapper.
# Determines whether nmspec or nmhdecay is used to generate
# spectrum.
self.whichcode = 'mSUGRA' #->nmspec
print "Setting up prior for CNMSSMAk..."
#parameter/prior scan settings set here:
uselog = cfg.getboolean('prioroptions','uselog')
massprior = priors.logprior if uselog else priors.linear
#get ranges for scan (must be tuples of length 2, i.e. (min,max))
M0range = cfg.get2tuple('prioroptions','M0range')
M12range = cfg.get2tuple('prioroptions','M12range')
TanBrange = cfg.get2tuple('prioroptions','TanBrange')
A0range = cfg.get2tuple('prioroptions','A0range')
Akapparange = cfg.get2tuple('prioroptions','Akapparange')
lambdarange = cfg.get2tuple('prioroptions','lambdarange')
#nuis par tuple values interpreted as mean and std of gaussian prior
Mtopmeanstd = cfg.get2tuple('prioroptions','Mtopmeanstd')
priorslist = [ ('M0', massprior(*M0range)),
('M12', massprior(*M12range)),
('TanB', priors.linear(*TanBrange)),
('A0', priors.linear(*A0range)),
('Akappa', priors.linear(*Akapparange)),
('lambda', priors.linear(*lambdarange)),
('Mtop', priors.gaussian(*Mtopmeanstd)),
]
def CNMSSMAkparlinks(paramvector):
# No extra parameter links to add, just copies paramvector
# into inputvector. See NMSSM10 for example of what this
# feature can be used for.
# paramvector - dictionary of scan parameter values,
# transformed by self.prior (from pyscanner, sent via
# likelihood function defined below)
p = paramvector
try:
i = self.inputvector
except AttributeError:
# if we haven't yet, need to create the inputvector dict
self.inputvector = p.copy()
i = self.inputvector
# copy all the new values from p to i, except for 's'
# because it doesn't exist in i.
for key, val in p.items():
i[key] = val
return i
self.addlinks = CNMSSMAkparlinks # called by likelihood function
#=================SET PRIOR=================================
#----------(to be used by PyScanner)---------------------------
self.scanargs['prior'] = priors.genindepprior(priorslist)
# Parse parameters to cluster from config file
allpars = ['M0','M12','TanB','A0','Akappa','lambda','Mtop']
self.scanargs['parorder'] = orderpars(clusterpars,allpars)
def setupNMSSM11():
"""Same as NMSSM9, but also varying Alambda and Akappa"""
# 'whichcode' is used to initialise the nmspec wrapper.
# Determines whether nmspec or nmhdecay is used to generate
# spectrum.
self.whichcode = 'generalNMSSM' #->nmhdecay
print "Setting up prior for NMSSM11..."
#parameter/prior scan settings set here:
uselog = cfg.getboolean('prioroptions','uselog')
massprior = priors.logprior if uselog else priors.linear
#mass2prior = priors.logprior if uselog else None
#parameters: MTOP TANB M2 ML3=ME3=MD3 MQ3 MU3
#LAMBDA KAPPA MHD^2 MHD^2
#get ranges for scan (must be tuples of length 2, i.e. (min,max))
M2range = cfg.get2tuple('prioroptions','M2range')
MD3range = cfg.get2tuple('prioroptions','MD3range')
MQ3range = cfg.get2tuple('prioroptions','MQ3range')
MU3range = cfg.get2tuple('prioroptions','MU3range')
TanBrange = cfg.get2tuple('prioroptions','TanBrange')
AU3range = cfg.get2tuple('prioroptions','AU3range')
Alambdarange = cfg.get2tuple('prioroptions','Alambdarange')
Akapparange = cfg.get2tuple('prioroptions','Akapparange')
lambdarange = cfg.get2tuple('prioroptions','lambdarange')
kapparange = cfg.get2tuple('prioroptions','kapparange')
srange = cfg.get2tuple('prioroptions','srange')
#nuis par tuple values interpreted as mean and std of gaussian prior
Mtopmeanstd = cfg.get2tuple('prioroptions','Mtopmeanstd')
priorslist = [ ('M2', massprior(*M2range)),
('MD3', massprior(*MD3range)),
('MQ3', massprior(*MQ3range)),
('MU3', massprior(*MU3range)),
('TanB', priors.linear(*TanBrange)),
('AU3', priors.linear(*AU3range)),
('lambda', priors.linear(*lambdarange)),
('kappa', priors.linear(*kapparange)),
('Alambda', priors.linear(*Alambdarange)),
('Akappa', priors.linear(*Akapparange)),
('s', priors.linear(*srange)),
('Mtop', priors.gaussian(*Mtopmeanstd)),
]
# First and second generation sfermions all set to high masses
# in template file.
# Trilinears (except AU3=AU2=AU1, Alambda, Akappa) all set to zero
# We set ML3=ME3=MD3 manually below
# nmhdecay then sets
# MD3=MD2=MD1,
# ME3=ME2=ME1 and
# ML3=ML2=ML1.
# also
# MQ3=MQ2=MQ1
# MU3=MU2=MU1
# AU3=AU2=AU1
# Extra links between SLHA input parameters
def NMSSM11parlinks(paramvector):
# Need to link the scanned parameters to the ones actually
# written to the nmspec input template file. In this case we
# actually only need to add a couple of extra ones and set
# them equal to MD3.
# paramvector - dictionary of scan parameter values,
# transformed by self.prior (from pyscanner, sent via
# likelihood function defined below)
p = paramvector
try:
i = self.inputvector
except AttributeError:
# if we haven't yet, need to create the inputvector dict
self.inputvector = p.copy()
i = self.inputvector
del i['s'] #this parameter is scanned only, doesn't
#exist in nmspec SLHA input file. Is
#transformed to mueff.
# copy all the new values from p to i, except for 's'
# because it doesn't exist in i.
for key, val in p.items():
if key!='s': i[key] = val
# MD3 links
i['ML3'] = p['MD3'] # nmspec then sets ML3=ML2=ML1 by default
i['ME3'] = p['MD3'] # nmspec then sets ME3=ME2=ME1 by default
# scanning s (singlet vev) but nmhdecay wants mu_eff as input
i['mueff'] = p['lambda']*p['s']
return i
self.addlinks = NMSSM11parlinks # called by likelihood function
# New feature: specify a valid set of parameters to use which
# will generate the full set of observables to be recorded. Will
# speed up the 'testing' loop of pyscanner.
#self.testvals = {'M2':,'MD3':,'MQ3':,'MU3':,'MHU^2':,
# 'MHD^2':,'TanB':,'AU3':,'lambda':,'Mtop':]
#=================SET PRIOR=================================
#----------(to be used by PyScanner)---------------------------
self.scanargs['prior'] = priors.genindepprior(priorslist)
# Parse parameters to cluster from config file
allpars = ['M2','s','MD3','MQ3','MU3','TanB',\
'AU3','lambda','kappa','Alambda','Akappa','Mtop']
self.scanargs['parorder'] = orderpars(clusterpars,allpars)
uselog = cfg.getboolean('prioroptions','uselog') #whether to use linear or log priors for mass parameters
massprior = priors.logprior if uselog else priors.linear #choose the mass parameter prior
#get ranges for scan (must be tuples of length 2, i.e. (min,max))
M0range = cfg.get2tuple('prioroptions','M0range')
M12range = cfg.get2tuple('prioroptions','M12range')
TanBrange = cfg.get2tuple('prioroptions','TanBrange')
A0range = cfg.get2tuple('prioroptions','A0range')
lambdarange = cfg.get2tuple('prioroptions','lambdarange')
Akapparange = cfg.get2tuple('prioroptions','Akapparange')
#nuis par tuple values interpreted as mean and std of gaussian prior
Mtopmeanstd = cfg.get2tuple('prioroptions','Mtopmeanstd')
priorslist = [ ('M0', massprior(*M0range)),
('M12', massprior(*M12range)),
('TanB', priors.linear(*TanBrange)),
('A0', priors.linear(*A0range)),
('lambda', priors.linear(*lambdarange)),
('Akappa', priors.linear(*Akapparange)),
('Mtop', priors.gaussian(*Mtopmeanstd)),
]
#=================SET PRIOR=================================
#----------(to be used by PyScanner)---------------------------
self.scanargs['prior'] = priors.genindepprior(priorslist)
self.scanargs['parorder'] = ['M0','M12','TanB','A0',
'lambda','Akappa','Mtop']
#==========LIKELIHOOD FUNCTION SETUP============================
#----------Initialise likelihood calculator---------------------
LFCalc = LFmod.LikeFuncCalculator(configfile) #Initialise likelihood calculator object, according to options set in configfile