示例#1
0
def squarklikefunc(smassIN,nmassIN):    #common likelihood function for remaining squarks
        smass = np.abs(smassIN)
        nmass = np.abs(nmassIN)
        return LFs.logsteplower(x=smass, limit=97 if smass-nmass>10 else 44)
示例#2
0
        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