def xenonlikefunc(x,mX,fracxsigmaT): #flat space erf fit version.
#x=WIMP-nucleon SI cross section
#mX=WIMP mass
#fracxsigmaT=fractional uncertainty in cross section due to theory
#For given WIMP mass, need to work out appropriate limit and sigma
#based on obslimitcurve and exp1sigmacurve:
#print x
# micromegas output gives cross section in pb, while Xenon limit is
#stated in cm^2. Must convert to cm^2 to apply limit.
#print x
x=x*(10**-36) #(1 pb = 10^-36 cm^2)
xsigmaT=x*fracxsigmaT #compute absolute uncertainty in sigmaT
if log10(mX)<minmX or log10(mX)>maxmX:
return 0 #do not apply constraints outside of mass range reported by xenon, don't really know what happens there.
else:
if log10(mX)<=1.3: #below this the curves basically merge and the cutoff is sharp, although my curve digitizer linearly extrapolated the exp+1sigma curve (ignore this section of curve data)
limit = 10**obslimitcurve.__call__(log10(mX)) #returns the x value at the observed xenon limit for this WIMP mass
sigma = 0 #reduces the erf to a step function
else: #we can approximate the width of the erf in this region (IN FLAT SPACE THIS TIME)
Y1 = Ytransf(Dchi2obs)
X1 = 10**obslimitcurve.__call__(log10(mX)) #returns the x value at the observed xenon limit for this WIMP mass
Y2 = Ytransf(Dchi2exp1sigma)
X2 = 10**exp1sigmacurve.__call__(log10(mX)) #returns the x value at the expected+1sigma xenon limit for this WIMP mass
limit=(X1*Y2-X2*Y1)/(Y2-Y1) #determine the mean of the matching erf likelihood function
sigma=(1/sqrt(2))*abs((X2-X1)/(Y2-Y1)) #determine the width of " " "
#compute theory error contribution
#print x, log10(x), log10(x)+36, 10**(log10(x)+36), sigma, xsigmaT
wtheory = LFs.logerfupper(x,limit,sqrt(sigma**2+xsigmaT**2)) #erf fit done in log space
bare = LFs.logerfupper(x,limit,sigma)
#print wtheory
#return wtheory#, bare #erf fit done in log space
return bare #erf fit done in log space
def globlikefunc(obsdict):
"""Compute log likelihoods and global log likelihood
Output format:
likedict = {likename: (logL, uselike),...}
"""
likedict = {} #reset likelihood dictionary
#Extract individual observables dictionaries from container dictionary
specdict = obsdict['spectrum']
if usemicrOmegas: darkdict = obsdict['darkmatter']
if useSuperISO: flavdict = obsdict['flavour']
#NO HDECAY STUFF IN HERE
#if useHDecay: decadict = obsdict['decay']
#===========================================================
# EFFECTIVE PRIOR
#===========================================================
likedict['CCRprior'] = (CCRprior(specdict['BQ'], specdict['tanbQ'],
specdict['muQ']), useCCRprior)
#===============================================================
# DEFINE LIKELIHOOD CONTRIBUTIONS
#===============================================================
#Note: some things left commented in old pysusy2 format because
#they contain useful information and I can't be bothered
#reformatting it.
#-----------STANDARD MODEL DATA ------------
# this is used to constrain the nuisance parameters
# -NB-REMOVED -> USING GAUSSIAN PRIORS INSTEAD.
#ialphaemL = Observable(slhafile=setupobjects[SPECTRUMfile], block="SMINPUTS",
# index=1, average=127.918, sigma=0.018,likefunc=LFs.lognormallike)
# #Jun 2009 hep-ph/0906.0957v2, they reference PDG 2008, but I can't find the value myself.
#alphasL = Observable(slhafile=setupobjects[SPECTRUMfile], block="SMINPUTS",
# index=3, average=0.1184, sigma=0.0007,likefunc=LFs.lognormallike)
# #PDG 2010 pg 101 - Physical Constants
#MZL = Observable(slhafile=setupobjects[SPECTRUMfile], block="SMINPUTS",
# index=4, average=91.1876, sigma=0.0021,likefunc=LFs.lognormallike)
# #PDG 2010 pg 101 - Physical Constants
#MtopL = Observable(slhafile=setupobjects[SPECTRUMfile], block="SMINPUTS",
# index=6, average=173.3, sigma=1.1,likefunc=LFs.lognormallike)
# #1007.3178, tevatron, 19 Jul 2010
#MbotL = Observable(slhafile=setupobjects[SPECTRUMfile], block="SMINPUTS",
# index=5, average=4.19, sigma=0.18,likefunc=LFs.lognormallike)
# #PDG 2010 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)
likedict['MW'] = (LFs.lognormallike(
x=specdict['MW'], mean=80.399,
sigma=sqrt(0.023**2 + 0.015**2)), useMW)
#PDG 2010, 2011 and partial 2012 update
#from
#http://lepewwg.web.cern.ch/LEPEWWG/ - extracted Apr 8 2011 (Jul 2010 average value)
#theory (second component) uncertainty stolen from 1107.1715, need proper source (they
#do not give one)
#-----------MAIN CONTRIBUTORS TO LIKELIHOOD-----------------
#=====Electroweak precision, RELIC DENSITY and (g-2)_mu========
if usemicrOmegas:
# delta rho parameter, describes MSSM corrections to electroweak observables
likedict['deltarho'] = (LFs.lognormallike(
x=darkdict['deltarho'], mean=0.0008, sigma=0.0017),
usedeltarho)
#PDG Standard Model Review: Electroweak model and constraints on new physics, pg 33 eq 10.47.
#Taking larger of the 1 sigma confidence internal values
#(likelihood function is actually highly asymmetric, PDG gives
#1 sigma: 1.0008 +0.0017,-0.0007
#2 sigma: 1.0004 +0.0029,-0.0011
#We are ignoring these complexities. The 0.0017 sigma should be quite on the conservative
#side, and contributions seem to only be positive, so the weird lower sigmas are essentially
#irrelevant anyway, in the CMSSM at least. I do not know if other MSSM models will be different,
#I assume probably not.
#In ISAJET have sin**2(thetaw), consider replacing deltarho with this. Values in different schemes
#in same part of pdg, pg 30, Table 10.8.
#Dark matter relic density
likedict['omegah2'] = (omegalikefunc(
darkdict['omegah2'], 0.1123,
sqrt((0.0035)**2 + (0.10 * 0.1123)**2)), useomegah2)
#Jan 2010, 1001.4538v2, page 3 table 1 (WMAP + BAO + H0 mean)
#theory (second component) uncertainty stolen from 1107.1715, need proper source
#Anomalous muon magnetic moment
likedict['deltaamu'] = (LFs.lognormallike(
x=darkdict['deltaamu'], mean=33.53e-10, sigma=8.24e-10),
usedeltaamu)
#1106.1315v1, Table 10, Solution B (most conservative)
#=====FLAVOUR OBSERVABLES FROM DARKMATTERfile======
# Note: Ditching these for now. Don't use them and they just make the output confusing.
# micrOmegas computes some of these essentially 'for free', so we may
# as well record them for comparison.
# NOTE: See equivalent flavour file observables definitions for references.
#
#bsgmoM = Observable(slhafile=setupobjects[DARKMATTERfile], block="CONSTRAINTS",
#index=2, average=3.55e-4, sigma=sqrt((0.26e-4)**2+(0.30e-4)**2),likefunc=LFs.lognormallike, uselike=False)
#
#bmumuM = Observable(slhafile=setupobjects[DARKMATTERfile], block="CONSTRAINTS",
#index=3, average=4.3e-8, sigma=0.14*4.3e-8,likefunc=CMSLHCbBsmumulikefunc(CLscurve,minBR,maxBR), uselike=False)
#
#obsorder += ['bsgmoM','bmumuM']
#=====DARK MATTER DIRECT DETECTION========
# LSP-nucleon cross sections # uncertainty in LSP-proton SI cross-section
# Note, this is based on uncertainties in hadronic scalar coefficients, currently hardcoded into micromegas.
# These uncertainties are propagated through a modified version of micromegas alongside the amplitude calculation.
# LSP-proton SI cross-section
likedict['sigmaLSPpSI'] = (xenonlikefunc(
darkdict['sigmaLSPpSI'], specdict['Mneut1'],
darkdict['dsigmaLSPpSI']), useDMdirect)
#likelihood function built from information in fig 5 of 1104.2549, 100 days of Xenon100 data (Apr 2011)
#=====FLAVOUR OBSERVABLES=============
if useSuperISO:
#Branching ratios
# BF(b->s gamma) - USING SUPERISO VALUE FOR LIKELIHOOD. Micromegas value recorded for comparison
likedict['bsgmo'] = (LFs.lognormallike(
x=flavdict['bsgmo'], mean=b2sg[0], sigma=b2sg[1]), True)
# BF(Bs -> mu+mu-) - USING SUPERISO VALUE FOR LIKELIHOOD. Micromegas value recorded for comparison
#likedict['bmumu'] = (Bsmumulikefunc(flavdict['bmumu']), True) #previous fancy version, using simplified likelihood for now
likedict['bmumu'] = (LFs.logerfupper(
x=flavdict['bmumu'], limit=b2mumu[0], sigma=b2sg[1]), True)
#might as well leave the other limits in unenforced
# BF(B_u -> tau nu) / BF(B_u -> tau nu)_SM
# ButaunuButaunuSM = MultiIndexObservable(setupobjects[LOWENERGYfile],LOWENERGYblock,(521,2,0,2,-15,16),
# average=1.28, sigma=0.38,likefunc=LFs.lognormallike)
# #copying 1107.1715, need to find proper source.
#REPLACING THE ABOVE RATIO, it is more straightforward to impose constraints directly on the branching ratio itself
#BF(B_u -> tau nu)
likedict['Butaunu'] = (LFs.lognormallike(
x=flavdict['Butaunu'], mean=1.67e-4, sigma=0.39e-4), False)
#Heavy Flavour Averaging Group (HFAG)
#1010.1589v3 (updated 6 Sep 2011), retrieved 12 Oct 2011
#Table 127, pg 180
# Delta0(B->K* gamma) (hope this is the same as Delta0-, seems like it might be)
# (isospin asymmetry)
likedict['Delta0'] = (LFs.lognormallike(
x=flavdict['Delta0'],
mean=0.029,
sigma=sqrt(0.029**2 + 0.019**2 + 0.018**2)), False)
# BaBAR, 2008 - 0808.1915, pg 17. No theory error included, pieces are different aspects of experimental error.
# BR(B+->D0 tau nu)/BR(B+-> D0 e nu)
likedict['BDtaunuBDenu'] = (LFs.lognormallike(
x=flavdict['BDtaunuBDenu'], mean=0.416, sigma=0.128),
False)
# BaBAR, 2008 - 0709.1698, pg 7, Table 1 (R value). No theory error included.
# R_l23: involves helicity suppressed K_l2 decays. Equals 1 in SM.
likedict['Rl23'] = (LFs.lognormallike(
x=flavdict['Rl23'], mean=1.004, sigma=0.007), False)
# FlaviaNet Working Group on Kaon Decays
# 0801.1817v1, pg 29, Eq. 4.19. No theory error included (meaning SuperISO error, as for other observables)
# BR(D_s->tau nu)
likedict['Dstaunu'] = (LFs.lognormallike(
x=flavdict['Dstaunu'],
mean=0.0538,
sigma=sqrt((0.0032)**2 + (0.002)**2)), False)
#Heavy Flavour Averaging Group (HFAG)
#1010.1589v3 (updated 6 Sep 2011), retrieved 12 Oct 2011
#Figure 68, pg 225. Theory error stolen from 1107.1715, need to find proper source.
# BR(D_s->mu nu)
likedict['Dsmunu'] = (LFs.lognormallike(
x=flavdict['Dsmunu'],
mean=0.00581,
sigma=sqrt((0.00043)**2 + (0.0002)**2)), False)
#Heavy Flavour Averaging Group (HFAG)
#1010.1589v3 (updated 6 Sep 2011), retrieved 14 Oct 2011
#Figure 67, pg 224. Theory error stolen from 1107.1715, need to find proper source.
#=================Direct sparticle search limits=============================
nmass = np.abs(
specdict['Mneut1']
) #get abs because spectrum generator returns negative values sometimes
smass = np.abs(specdict['MseL'])
likedict['MseL'] = (LFs.logsteplower(
x=smass,
limit=99 if nmass < 84 else 96 if smass - nmass > 6 else 73),
useDSL) #Phys. Lett. B544 p73 (2002)
smass = np.abs(specdict['MsmuL'])
likedict['MsmuL'] = (LFs.logsteplower(
x=smass, limit=94.4 if smass - nmass > 6 else 73),
useDSL) #Phys. Lett. B544 p73 (2002)
smass = np.abs(specdict['Mstau1'])
likedict['Mstau1'] = (LFs.logsteplower(
x=smass, limit=86 if smass - nmass > 8 else 73),
useDSL) #Phys. Lett. B544 p73 (2002)
smass = np.abs(specdict['MesnuL'])
likedict['MesnuL'] = (LFs.logsteplower(
x=smass, limit=43 if smass - nmass < 10 else 94),
useDSL) #Phys. Lett. B544 p73 (2002)
smass = np.abs(specdict['Mchar1'])
likedict['Mchar1'] = (
LFs.logsteplower(
x=smass, limit=97.1 if (smass - nmass) > 3 else 45), useDSL
) #45 GeV limit from PDG 2010, unconditional limit from invisible Z width, other limit from Eur. Phys. J. C31 p421 (2003)
#NOTE: USING SAME LIMITS FOR L AND R SQUARKS, CHECK THAT THIS IS FINE.
smass = np.abs(specdict['Mstop1'])
likedict['Mstop1'] = (
LFs.logsteplower(
x=smass, limit=95 if smass - nmass > 8 else 63), useDSL
) #? Damn don't seem to have written down where this comes from. Need to find out.
smass = np.abs(
specdict['Msbot1']
) #NOTE: stop2 heavier than stop1 by definition so only need to bother applying limit to stop1. (1 and 2 are the mass eigenstates, mixtures of L and R interaction eigenstates). Same for sbot2
likedict['Msbot1'] = (LFs.logsteplower(
x=smass, limit=93 if smass - nmass > 8 else 63), useDSL)
#NOTEL USING SAME LIMITS FOR REST OF SQUARKS, CHECK THIS ABOVE, 'squarklikefunc'.
#likelihood function defined above, usage: squarklikefunc(smassIN,nmassIN) (smass - THIS sparticle mass, nmass - neutralino 1 mass)
#NOTE: L and R states are same as 1 and 2 states for other squarks because off diagonal terms in the mixing matrices are
#negligible. This is not true for the third generation squarks which is why we are sure to label them 1 and 2 above (1=lightest)
for Msquark in [
'MsupL', 'MsupR', 'MsdownL', 'MsdownR', 'MsstrangeL',
'MsstrangeR', 'MscharmL', 'MscharmR'
]:
likedict[Msquark] = (squarklikefunc(specdict[Msquark], nmass),
useDSL)
likedict['Mgluino'] = (
LFs.logerflower(x=specdict['Mgluino'], limit=289,
sigma=15), useDSL
) #from 08093792 - not well checked, probably need to update with LHC data.
# ---------------NOTE: LEP HIGGS MASS LIMIT-------------------. Using digitized LEP limit from hep-ph/0603247 (2006), fig 3a
# Using digitized curve to compute m_h bound appropriate to g_ZZh coupling for each model point with estimated 3 GeV sigma for m_h returned by SoftSusy
# Need the following observables to be extracted to compute Higgs likelihood function:
# alpha,tanbetaQ #RGE running of tanb is slow so tanbQ should be approximately the EW tanb value
# Likelihood function for this is written above (needs to return a lower limit erf likelihood depending on g_ZZh):
#higgs=lightest higgs mass;alpha=higgs scalar mixing angle;tanbeta=tan(higgs VEV ratio)
mhiggs = np.abs(specdict['Mh0'])
#likedict['Mh0'] = (LFs.logerflower(x=mhiggs, limit=higgslimitfunc(specdict['alpha'],specdict['tanbQ'],mHlimitcurve), sigma=3), useHiggs)
#now using LHC measured higgs mass
#NOTE! SET TO TRUE! IGNORES WHATEVER IS SPECIFIED IN CONFIG FILE!
likedict['Mh0'] = (LFs.lognormallike(
x=mhiggs, mean=mh[0], sigma=mh[1]), True)
#No fancy HDecay stuff in here.
#===============GLOBAL LOG LIKELIHOOD=======================
LogL = sum(
logl for logl, uselike in likedict.itervalues() if uselike)
return LogL, likedict
