an update on gambit · an update on gambit anders kvellestad, nordita cern, december 13, 2016 on...

An update on GAMBITAnders Kvellestad, Nordita

CERN, December 13, 2016

on behalf of the GAMBIT collaboration

Anders Kvellestad

Outline

1. Global fits

2. GAMBIT

3. ColliderBit: a GAMBIT module for collider physics

4. Preliminary GAMBIT results

2

1. Global fits

Anders Kvellestad

Comparing theories to data

Many parameters and many constraints → perform a global fit

4

Anders Kvellestad



• Combine constraints in an overall likelihood function

…

4

Anders Kvellestad




• Explore likelihood across entire parameter space (smart sampling)

4

Anders Kvellestad





• Interpretation: frequentist/Bayesian

4

Anders Kvellestad





• Interpretation: frequentist/Bayesian

• Project down to 1 or 2 parameters (profile/marginalise)

4

Anders Kvellestad

1

⇥

[huge number of points required to explore parameter space]

[large number of observables]

⇥[long calculation time per observable per parameter point]

⇡

Main challenge

5

Anders Kvellestad

1

⇥




⇡

- Introduce nuisance parameters- Require many external tools

Main challenge

5

Anders Kvellestad

1

⇥




⇡

- E.g. simulating LHC searches

Main challenge

5

Anders Kvellestad

1

⇥




⇡ - Require smart sampling

Main challenge

5

Anders Kvellestad

Global fits require lots of work, but we shouldn’t have to start from scratch every time…

Need a global fit tool with easily replaceable

- models- theory calculators

- datasets and observables- scanning algorithms

6

2. GAMBIT

G A M B I T

Global fits for dark matter and new physics

G A M B I T

Pat Scott – Dec 1 2016 – AAO Colloquium Searches for dark matter with GAMBITAnders Kvellestad 8

• Code organized in physics modules

• A module is a collection of module functions

• To ensure modularity: No hardcoded function calls between module functions!

• Each function presents GAMBIT with its• allowed models • dependencies • capability

• Dependency resolution at runtime: GAMBIT connects functions as required

• A theory calculation can reside in• a GAMBIT module function (C++)• an external library (Fortran, C, C++, Mathematica*, …) loaded at runtime

Main design principle

Anders Kvellestad 9 * Not for first release

Dependency tree

CMSSM_parametersType: ModelParameters

Function: primary_parametersModule: CMSSM

unimproved_MSSM_spectrumType: const Spectrum*

Function: get_CMSSM_spectrumModule: SpecBit

DarkSUSY_PointInitType: bool

Function: DarkSUSY_PointInit_MSSMModule: DarkBit

DarkMatter_IDType: std::string

Function: DarkMatter_ID_MSSM30atQModule: DarkBit

NUHM1_parametersType: ModelParameters

Function: NUHM1_parametersModule: CMSSM

FeynHiggs_2_11_initType: void

Function: FeynHiggs_2_11_initModule: BackendIniBit

MSSM_spectrumType: const Spectrum*

Function: make_MSSM_precision_spectrumModule: PrecisionBit

MSSM78atQ_parametersType: ModelParameters

Function: MSSM78atQ_parametersModule: MSSM78atMGUT

TH_ProcessCatalogType: DarkBit::TH_ProcessCatalog

Function: TH_ProcessCatalog_MSSMModule: DarkBit

nuyield_ptrType: nuyield_info

Function: nuyield_from_DSModule: DarkBit

SetWIMP_DDCalc0Type: bool

Function: SetWIMP_DDCalc0Module: DarkBit

GA_AnnYieldType: Funk::Funk

Function: GA_AnnYield_GeneralModule: DarkBit

GA_missingFinalStatesType: std::vector<std::string>

Function: GA_missingFinalStatesModule: DarkBit

mwimpType: double

Function: mwimp_genericModule: DarkBit

sigmavType: double

Function: sigmav_late_universeModule: DarkBit

NUHM2_parametersType: ModelParameters

Function: NUHM2_parametersModule: NUHM1

FH_HiggsProdType: fh_HiggsProd

Function: FH_HiggsProdModule: ColliderBit

FH_MSSMMassesType: fh_MSSMMassObs

Function: FH_MSSMMassesModule: SpecBit

prec_HiggsMassesType: fh_HiggsMassObs

Function: FH_HiggsMassesModule: SpecBit

Higgs_CouplingsType: fh_Couplings

Function: FH_CouplingsModule: SpecBit

FH_PrecisionType: fh_PrecisionObs

Function: FH_PrecisionObsModule: PrecisionBit

HB_ModelParametersType: hb_ModelParameters

Function: MSSMHiggs_ModelParametersModule: ColliderBit

SUSY_HIT_1_5_initType: void

Function: SUSY_HIT_1_5_initModule: BackendIniBit

ALEPH_Selectron_LLikeType: double

Function: ALEPH_Selectron_Conservative_LLikeModule: ColliderBit

L3_Selectron_LLikeType: double

Function: L3_Selectron_Conservative_LLikeModule: ColliderBit

ALEPH_Smuon_LLikeType: double

Function: ALEPH_Smuon_Conservative_LLikeModule: ColliderBit

L3_Smuon_LLikeType: double

Function: L3_Smuon_Conservative_LLikeModule: ColliderBit

ALEPH_Stau_LLikeType: double

Function: ALEPH_Stau_Conservative_LLikeModule: ColliderBit

L3_Stau_LLikeType: double

Function: L3_Stau_Conservative_LLikeModule: ColliderBit

FlavBit_fillType: parameters

Function: SI_FlavBit_fillModule: FlavBit

MicrOmegas_3_5_5_initType: void

Function: MicrOmegas_3_5_5_initModule: BackendIniBit

ColliderOperatorType: void

Function: operateLHCLoopModule: ColliderBit

HardScatteringSimType: ColliderBit::SpecializablePythia

Function: getPythiaModule: ColliderBit

LEP208_xsec_selselbarType: triplet<double>

Function: LEP208_SLHA1_convention_xsec_selselbarModule: ColliderBit

LEP208_xsec_serserbarType: triplet<double>

Function: LEP208_SLHA1_convention_xsec_serserbarModule: ColliderBit

LEP208_xsec_smulsmulbarType: triplet<double>

Function: LEP208_SLHA1_convention_xsec_smulsmulbarModule: ColliderBit

LEP208_xsec_smursmurbarType: triplet<double>

Function: LEP208_SLHA1_convention_xsec_smursmurbarModule: ColliderBit

LEP208_xsec_stau1stau1barType: triplet<double>

Function: LEP208_SLHA1_convention_xsec_stau1stau1barModule: ColliderBit



LEP208_xsec_chi00_12Type: triplet<double>

Function: LEP208_SLHA1_convention_xsec_chi00_12Module: ColliderBit

OPAL_Neutralino_Hadronic_LLikeType: double

Function: OPAL_Neutralino_Hadronic_Conservative_LLikeModule: ColliderBit





LEP208_xsec_chipm_11Type: triplet<double>

Function: LEP208_SLHA1_convention_xsec_chipm_11Module: ColliderBit

OPAL_Chargino_Hadronic_LLikeType: double

Function: OPAL_Chargino_Hadronic_Conservative_LLikeModule: ColliderBit

OPAL_Chargino_SemiLeptonic_LLikeType: double

Function: OPAL_Chargino_SemiLeptonic_Conservative_LLikeModule: ColliderBit

OPAL_Chargino_Leptonic_LLikeType: double

Function: OPAL_Chargino_Leptonic_Conservative_LLikeModule: ColliderBit

OPAL_Chargino_All_Channels_LLikeType: double

Function: OPAL_Chargino_All_Channels_Conservative_LLikeModule: ColliderBit



LEP205_xsec_selselbarType: triplet<double>

Function: LEP205_SLHA1_convention_xsec_selselbarModule: ColliderBit

LEP205_xsec_serserbarType: triplet<double>

Function: LEP205_SLHA1_convention_xsec_serserbarModule: ColliderBit

LEP205_xsec_smulsmulbarType: triplet<double>

Function: LEP205_SLHA1_convention_xsec_smulsmulbarModule: ColliderBit

LEP205_xsec_smursmurbarType: triplet<double>

Function: LEP205_SLHA1_convention_xsec_smursmurbarModule: ColliderBit







L3_Neutralino_All_Channels_LLikeType: double

Function: L3_Neutralino_All_Channels_Conservative_LLikeModule: ColliderBit

L3_Neutralino_Leptonic_LLikeType: double

Function: L3_Neutralino_Leptonic_Conservative_LLikeModule: ColliderBit







L3_Chargino_All_Channels_LLikeType: double

Function: L3_Chargino_All_Channels_Conservative_LLikeModule: ColliderBit

L3_Chargino_Leptonic_LLikeType: double

Function: L3_Chargino_Leptonic_Conservative_LLikeModule: ColliderBit



mwType: triplet<double>

Function: mw_from_MSSM_spectrumModule: PrecisionBit

SLHA1_violationType: int

Function: check_first_sec_gen_mixingModule: DecayBit

SLHA_pseudonymsType: DecayBit::mass_es_pseudonyms

Function: get_mass_es_pseudonymsModule: DecayBit

gluino_decay_ratesType: DecayTable::EntryFunction: gluino_decays

Module: DecayBit

stop_1_decay_ratesType: DecayTable::EntryFunction: stop_1_decays

Module: DecayBit

stop_2_decay_ratesType: DecayTable::EntryFunction: stop_2_decays

Module: DecayBit

sbottom_1_decay_ratesType: DecayTable::Entry

Function: sbottom_1_decaysModule: DecayBit

sbottom_2_decay_ratesType: DecayTable::Entry

Function: sbottom_2_decaysModule: DecayBit

sup_l_decay_ratesType: DecayTable::EntryFunction: sup_l_decays

Module: DecayBit

sup_r_decay_ratesType: DecayTable::EntryFunction: sup_r_decays

Module: DecayBit

sdown_l_decay_ratesType: DecayTable::EntryFunction: sdown_l_decays

Module: DecayBit

sdown_r_decay_ratesType: DecayTable::Entry

Function: sdown_r_decaysModule: DecayBit

scharm_l_decay_ratesType: DecayTable::Entry

Function: scharm_l_decaysModule: DecayBit

scharm_r_decay_ratesType: DecayTable::Entry

Function: scharm_r_decaysModule: DecayBit

sstrange_l_decay_ratesType: DecayTable::Entry

Function: sstrange_l_decaysModule: DecayBit

sstrange_r_decay_ratesType: DecayTable::Entry

Function: sstrange_r_decaysModule: DecayBit

selectron_l_decay_ratesType: DecayTable::Entry

Function: selectron_l_decaysModule: DecayBit

selectron_r_decay_ratesType: DecayTable::Entry

Function: selectron_r_decaysModule: DecayBit

smuon_l_decay_ratesType: DecayTable::Entry

Function: smuon_l_decaysModule: DecayBit

smuon_r_decay_ratesType: DecayTable::Entry

Function: smuon_r_decaysModule: DecayBit

stau_1_decay_ratesType: DecayTable::EntryFunction: stau_1_decays

Module: DecayBit

stau_2_decay_ratesType: DecayTable::EntryFunction: stau_2_decays

Module: DecayBit

snu_electronl_decay_ratesType: DecayTable::Entry

Function: snu_electronl_decaysModule: DecayBit

snu_muonl_decay_ratesType: DecayTable::Entry

Function: snu_muonl_decaysModule: DecayBit

snu_taul_decay_ratesType: DecayTable::EntryFunction: snu_taul_decays

Module: DecayBit

charginoplus_1_decay_ratesType: DecayTable::Entry

Function: charginoplus_1_decaysModule: DecayBit

charginoplus_2_decay_ratesType: DecayTable::Entry

Function: charginoplus_2_decaysModule: DecayBit

neutralino_1_decay_ratesType: DecayTable::Entry

Function: neutralino_1_decaysModule: DecayBit







stopbar_1_decay_ratesType: DecayTable::Entry

Function: stopbar_1_decaysModule: DecayBit

stopbar_2_decay_ratesType: DecayTable::Entry

Function: stopbar_2_decaysModule: DecayBit

sbottombar_1_decay_ratesType: DecayTable::Entry

Function: sbottombar_1_decaysModule: DecayBit

sbottombar_2_decay_ratesType: DecayTable::Entry

Function: sbottombar_2_decaysModule: DecayBit

supbar_l_decay_ratesType: DecayTable::EntryFunction: supbar_l_decays

Module: DecayBit

supbar_r_decay_ratesType: DecayTable::Entry

Function: supbar_r_decaysModule: DecayBit

sdownbar_l_decay_ratesType: DecayTable::Entry

Function: sdownbar_l_decaysModule: DecayBit

sdownbar_r_decay_ratesType: DecayTable::Entry

Function: sdownbar_r_decaysModule: DecayBit

scharmbar_l_decay_ratesType: DecayTable::Entry

Function: scharmbar_l_decaysModule: DecayBit

scharmbar_r_decay_ratesType: DecayTable::Entry

Function: scharmbar_r_decaysModule: DecayBit

sstrangebar_l_decay_ratesType: DecayTable::Entry

Function: sstrangebar_l_decaysModule: DecayBit

sstrangebar_r_decay_ratesType: DecayTable::Entry

Function: sstrangebar_r_decaysModule: DecayBit

selectronbar_l_decay_ratesType: DecayTable::Entry

Function: selectronbar_l_decaysModule: DecayBit

selectronbar_r_decay_ratesType: DecayTable::Entry

Function: selectronbar_r_decaysModule: DecayBit

smuonbar_l_decay_ratesType: DecayTable::Entry

Function: smuonbar_l_decaysModule: DecayBit

smuonbar_r_decay_ratesType: DecayTable::Entry

Function: smuonbar_r_decaysModule: DecayBit

staubar_1_decay_ratesType: DecayTable::Entry

Function: staubar_1_decaysModule: DecayBit

staubar_2_decay_ratesType: DecayTable::Entry

Function: staubar_2_decaysModule: DecayBit

snubar_electronl_decay_ratesType: DecayTable::Entry

Function: snubar_electronl_decaysModule: DecayBit

snubar_muonl_decay_ratesType: DecayTable::Entry

Function: snubar_muonl_decaysModule: DecayBit

snubar_taul_decay_ratesType: DecayTable::Entry

Function: snubar_taul_decaysModule: DecayBit

charginominus_1_decay_ratesType: DecayTable::Entry

Function: charginominus_1_decaysModule: DecayBit

charginominus_2_decay_ratesType: DecayTable::Entry

Function: charginominus_2_decaysModule: DecayBit

t_decay_ratesType: DecayTable::EntryFunction: FH_t_decays

Module: DecayBit

Higgs_decay_ratesType: DecayTable::Entry

Function: FH_MSSM_h0_1_decaysModule: DecayBit

h0_2_decay_ratesType: DecayTable::Entry

Function: FH_h0_2_decaysModule: DecayBit

A0_decay_ratesType: DecayTable::EntryFunction: FH_A0_decays

Module: DecayBit

Hplus_decay_ratesType: DecayTable::Entry

Function: FH_Hplus_decaysModule: DecayBit

SuperIso_3_4_initType: void

Function: SuperIso_3_4_initModule: BackendIniBit

Hminus_decay_ratesType: DecayTable::EntryFunction: Hminus_decays

Module: DecayBit

cascadeMC_HistogramsType: DarkBit::simpleHistContainterFunction: cascadeMC_Histograms

Module: DarkBit

cascadeMC_DecayTableType: DarkBit::DecayChain::DecayTable

Function: cascadeMC_DecayTableModule: DarkBit

cascadeMC_LoopManagementType: void

Function: cascadeMC_LoopManagerModule: DarkBit

IC79WH_dataType: nudata

Function: IC79WH_fullModule: DarkBit

IC79WL_dataType: nudata

Function: IC79WL_fullModule: DarkBit

IC79SL_dataType: nudata

Function: IC79SL_fullModule: DarkBit

CalcRates_XENON100_2012_DDCalc0Type: bool

Function: CalcRates_XENON100_2012_DDCalc0Module: DarkBit

CalcRates_LUX_2013_DDCalc0Type: bool

Function: CalcRates_LUX_2013_DDCalc0Module: DarkBit

lnL_FermiLATdwarfsType: double

Function: lnL_FermiLATdwarfs_gamLikeModule: DarkBit

cascadeMC_gammaSpectraType: DarkBit::stringFunkMap

Function: cascadeMC_gammaSpectraModule: DarkBit

cascadeMC_InitialStateType: std::string

Function: cascadeMC_InitialStateModule: DarkBit

sigma_SI_pType: double

Function: sigma_SI_p_simpleModule: DarkBit

sigma_SI_nType: double

Function: sigma_SI_n_simpleModule: DarkBit

sigma_SD_pType: double

Function: sigma_SD_p_simpleModule: DarkBit

sigma_SD_nType: double

Function: sigma_SD_n_simpleModule: DarkBit

capture_rate_SunType: double

Function: capture_rate_Sun_constant_xsecModule: DarkBit

equilibration_time_SunType: double

Function: equilibration_time_SunModule: DarkBit

MSSM30atMGUT_parametersType: ModelParameters

Function: MSSM30atMGUT_parametersModule: NUHM2

deltarhoType: triplet<double>

Function: FH_precision_deltarhoModule: PrecisionBit

prec_mwType: triplet<double>

Function: FH_precision_mwModule: PrecisionBit

prec_sinW2_effType: triplet<double>

Function: FH_precision_sinW2Module: PrecisionBit

HB_LEP_lnLType: double

Function: HB_LEP_lnLModule: ColliderBit

HS_LHC_lnLType: double

Function: HS_LHC_lnLModule: ColliderBit

Bsmumu_untagType: double

Function: SI_Bsmumu_untagModule: FlavBit

BdmumuType: double

Function: SI_BdmumuModule: FlavBit

BtaunuType: double

Function: SI_BtaunuModule: FlavBit

BDtaunuType: double

Function: SI_BDtaunuModule: FlavBit

BDtaunu_BDenuType: double

Function: SI_BDtaunu_BDenuModule: FlavBit

Kmunu_pimunuType: double

Function: SI_Kmunu_pimunuModule: FlavBit

DstaunuType: double

Function: SI_DstaunuModule: FlavBit

DsmunuType: double

Function: SI_DsmunuModule: FlavBit

DmunuType: double

Function: SI_DmunuModule: FlavBit

BRBKstarmumu_11_25Type: Flav_KstarMuMu_obs

Function: SI_BRBKstarmumu_11_25Module: FlavBit











b2sll_MType: FlavBit::Flav_measurement_assym

Function: b2sll_measurementsModule: FlavBit

b2sll_LLType: double

Function: b2sll_likelihoodModule: FlavBit

b2ll_LLType: double

Function: b2ll_likelihoodModule: FlavBit

b2ll_MType: FlavBit::Flav_measurement_assym

Function: b2ll_measurementsModule: FlavBit

SL_MType: FlavBit::Flav_measurement_assym

Function: SL_measurementsModule: FlavBit

SL_LLType: double

Function: SL_likelihoodModule: FlavBit

DD_couplingsType: DarkBit::DD_couplings

Function: DD_couplings_MicrOmegasModule: DarkBit

RD_oh2Type: double

Function: RD_oh2_MicrOmegasModule: DarkBit

HardScatteringEventType: Pythia8::Event

Function: generatePythia8EventModule: ColliderBit

ConvertedScatteringEventType: HEPUtils::Event

Function: convertPythia8ParticleEventModule: ColliderBit

SimpleSmearingSimType: ColliderBit::BuckFastSmear

Function: getBuckFastModule: ColliderBit

AnalysisContainerType: HEPUtilsAnalysisContainer

Function: getAnalysisContainerModule: ColliderBit

ReconstructedEventType: HEPUtils::Event

Function: reconstructBuckFastEventModule: ColliderBit

AnalysisNumbersType: ColliderLogLikesFunction: runAnalysesModule: ColliderBit

lnL_W_massType: double

Function: lnL_W_mass_chi2Module: PrecisionBit

decay_ratesType: DecayTable

Function: all_decaysModule: DecayBit

tbar_decay_ratesType: DecayTable::Entry

Function: tbar_decaysModule: DecayBit

cascadeMC_ChainEventType: DarkBit::DecayChain::ChainContainer

Function: cascadeMC_GenerateChainModule: DarkBit

cascadeMC_EventCountType: DarkBit::stringIntMap

Function: cascadeMC_EventCountModule: DarkBit

IC79WH_loglikeType: double

Function: IC79WH_loglikeModule: DarkBit

IC79WH_bgloglikeType: double

Function: IC79WH_bgloglikeModule: DarkBit

IC79WL_loglikeType: double

Function: IC79WL_loglikeModule: DarkBit

IC79WL_bgloglikeType: double

Function: IC79WL_bgloglikeModule: DarkBit

IC79SL_loglikeType: double

Function: IC79SL_loglikeModule: DarkBit

IC79SL_bgloglikeType: double

Function: IC79SL_bgloglikeModule: DarkBit

lnL_XENON100_2012Type: double

Function: XENON100_2012_LogLikelihood_DDCalc0Module: DarkBit

lnL_LUX_2013Type: double

Function: LUX_2013_LogLikelihood_DDCalc0Module: DarkBit

annihilation_rate_SunType: double

Function: annihilation_rate_SunModule: DarkBit

MSSM78atMGUT_parametersType: ModelParameters

Function: MSSM78atMGUT_parametersModule: MSSM30atMGUT

lnL_deltarhoType: double

Function: lnL_deltarho_chi2Module: PrecisionBit

lnL_sinW2_effType: double

Function: lnL_sinW2_eff_chi2Module: PrecisionBit

RD_fractionType: double

Function: RD_fraction_from_oh2Module: DarkBit

lnL_oh2Type: double

Function: lnL_oh2_SimpleModule: DarkBit

LHC_Combined_LogLikeType: double

Function: calc_LHC_LogLikeModule: ColliderBit

IC79_loglikeType: double

Function: IC79_loglikeModule: DarkBit

DDCalc0_0_0_initType: void

Function: DDCalc0_0_0_initModule: BackendIniBit

StandardModel_SLHA2_parametersType: ModelParameters

Function: primary_parametersModule: StandardModel_SLHA2

SMINPUTSType: SMInputs

Function: get_SMINPUTSModule: SpecBit

nuclear_params_sigma0_sigmal_parametersType: ModelParameters

Function: nuclear_params_sigma0_sigmal_parametersModule: nuclear_params_sigmas_sigmal

lnL_t_massType: double

Function: lnL_t_mass_chi2Module: PrecisionBit

lnL_mbmbType: double

Function: lnL_mbmb_chi2Module: PrecisionBit

lnL_alpha_emType: double

Function: lnL_alpha_em_chi2Module: PrecisionBit

lnL_alpha_sType: double

Function: lnL_alpha_s_chi2Module: PrecisionBit

lnL_light_quark_massesType: double

Function: lnL_light_quark_masses_chi2Module: PrecisionBit

nuclear_params_fnq_parametersType: ModelParameters

Function: nuclear_params_fnq_parametersModule: nuclear_params_sigma0_sigmal

nuclear_params_sigmas_sigmal_parametersType: ModelParameters

Function: primary_parametersModule: nuclear_params_sigmas_sigmal

lnL_SI_nuclear_parametersType: double

Function: lnL_sigmas_sigmalModule: DarkBit

HiggsBounds_4_2_1_initType: void

Function: HiggsBounds_4_2_1_initModule: BackendIniBit

nulike_1_0_0_initType: void

Function: nulike_1_0_0_initModule: BackendIniBit

Pythia_8_209_initType: void

Function: Pythia_8_209_initModule: BackendIniBit

DarkSUSY_5_1_1_initType: void

Function: DarkSUSY_5_1_1_initModule: BackendIniBit

SimYieldTableType: DarkBit::SimYieldTable

Function: SimYieldTable_DarkSUSYModule: DarkBit

gamLike_1_0_0_initType: void

Function: gamLike_1_0_0_initModule: BackendIniBit

HiggsSignals_1_4_initType: void

Function: HiggsSignals_1_4_initModule: BackendIniBit

cascadeMC_FinalStatesType: std::vector<std::string>

Function: cascadeMC_FinalStatesModule: DarkBit

Debug_CapType: bool

Function: DebugModule: FlavBit

Debug_Cap_LLType: bool

Function: Debug_LLModule: FlavBit

W_plus_decay_ratesType: DecayTable::EntryFunction: W_plus_decays

Module: DecayBit

W_minus_decay_ratesType: DecayTable::Entry

Function: W_minus_decaysModule: DecayBit

Z_decay_ratesType: DecayTable::Entry

Function: Z_decaysModule: DecayBit

mu_plus_decay_ratesType: DecayTable::Entry

Function: mu_plus_decaysModule: DecayBit

mu_minus_decay_ratesType: DecayTable::Entry

Function: mu_minus_decaysModule: DecayBit

tau_plus_decay_ratesType: DecayTable::EntryFunction: tau_plus_decays

Module: DecayBit

tau_minus_decay_ratesType: DecayTable::Entry

Function: tau_minus_decaysModule: DecayBit

pi_0_decay_ratesType: DecayTable::Entry

Function: pi_0_decaysModule: DecayBit

pi_plus_decay_ratesType: DecayTable::EntryFunction: pi_plus_decays

Module: DecayBit

pi_minus_decay_ratesType: DecayTable::Entry

Function: pi_minus_decaysModule: DecayBit

eta_decay_ratesType: DecayTable::Entry

Function: eta_decaysModule: DecayBit

rho_0_decay_ratesType: DecayTable::EntryFunction: rho_0_decays

Module: DecayBit

rho_plus_decay_ratesType: DecayTable::Entry

Function: rho_plus_decaysModule: DecayBit

rho_minus_decay_ratesType: DecayTable::Entry

Function: rho_minus_decaysModule: DecayBit

omega_decay_ratesType: DecayTable::EntryFunction: omega_decays

Module: DecayBit

CMSSM

Anders Kvellestad 10

• ColliderBit: Higgs data, SUSY searches at LHC & LEP

• DarkBit: Relic density, direct detection, indirect detection

• FlavBit: B, D, K decays, angular obs., theory uncertainties, correlations(See Nazila and Marcin’s talk yesterday)

• SpecBit: Generic BSM spectrum object. RGE running, masses, mixings, etc via interchangeable interface to RGE codes.

• DecayBit: Decay widths and BRs

• PrecisionBit: SM likelihoods (nuisance par.), EW precision tests, g-2

• ScannerBit: Scanning algorithms (differential evolution, nested sampling, MCMC, t-walk, grid scan, "random sampling", …)

• [Your own module here]

GAMBIT modules


3. ColliderBit

Eur. Phys. J. C manuscript No.

(will be inserted by the editor)

ColliderBit: a GAMBIT module for the calculation of

high-energy collider observables and likelihoods

Csaba Balazs

1,2

, Andy Buckley

3

, Lars Dal

4

, Ben Farmer

5

, Paul Jackson

2,6

,

Abram Krislock

4

, Anders Kvellestad

7

, Antje Putze

8

, Are Raklev

4

,

Christopher Rogan

9

, Aldo Saavedra

2,10

, Pat Scott

11

, Christoph Weniger

12

,

Martin White

2,6

1School of Physics and Astronomy, Monash University, Melbourne, Victoria 3800 Australia2Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale3SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK4Department of Physics, University of Oslo, N-0316 Oslo, Norway5Oskar Klein Centre for Cosmoparticle Physics, AlbaNova University Centre, SE-10691 Stockholm, Sweden6Department of Physics, University of Adelaide, Adelaide, South Australia 5005, Australia7NORDITA, Roslagstullsbacken 23, SE-10691 Stockholm, Sweden8LAPTh, Universite de Savoie, CNRS, 9 chemin de Bellevue B.P.110, F-74941 Annecy-le-Vieux, France9Department of Physics, Harvard University, Cambridge, MA 02138, USA10Centre for Translational Data Science, Faculty of Engineering and Information Technologies, School of Physics, The University ofSydney, NSW 2006, Australia11Department of Physics, Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ, UK12GRAPPA, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands

the date of receipt and acceptance should be inserted later

Abstract We describe a new code for the calculationof high energy collider observables in theories of physicsbeyond the Standard Model (BSM). The ColliderBitcode features a generic interface to BSM models, aunique parallelised Monte Carlo event generation schemesuitable for large-scale supercomputer applications, anda number of LHC analyses, covering a reasonable rangeof the BSM signatures currently sought by ATLAS andCMS. ColliderBit also calculates likelihoods for Higgssector observables, and LEP searches for BSM particles.These features are provided by a combination of newcode unique to ColliderBit, and interfaces to existingstate-of-the-art public codes. ColliderBit is at the sametime an important part of the GAMBIT framework forBSM inference, and a standalone tool for e�cientlyapplying collider constraints to theories of new physics.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 12 Physics and implementation . . . . . . . . . . . . . . . . 3

2.1 LHC likelihood calculation . . . . . . . . . . . . . . 32.1.1 LHC constraints included in ColliderBit . . 32.1.2 Strategy for applying LHC constraints

without model dependence . . . . . . . . . 42.1.3 Cross-section calculations . . . . . . . . . . 42.1.4 Monte Carlo event generation . . . . . . . . 52.1.5 Event record . . . . . . . . . . . . . . . . . 62.1.6 Detector simulation . . . . . . . . . . . . . 7

2.1.7 LHC event analysis framework . . . . . . . 82.1.8 LHC Statistics Calculations . . . . . . . . . 82.1.9 Validation of ColliderBit LHC constraints . 10

2.2 LEP likelihood calculation . . . . . . . . . . . . . . 112.3 Higgs likelihood calculation . . . . . . . . . . . . . 13

3 User interface . . . . . . . . . . . . . . . . . . . . . . . . 143.1 GAMBIT interface . . . . . . . . . . . . . . . . . . 14

3.1.1 LHC simulation capabilities . . . . . . . . . 143.1.2 LEP supersymmetry limit capabilities . . . 183.1.3 Higgs likelihood capabilities . . . . . . . . . 18

3.2 Standalone interface . . . . . . . . . . . . . . . . . 184 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.1 CMSSM example . . . . . . . . . . . . . . . . . . . 204.2 Generic Pythia model example . . . . . . . . . . . . 21

5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 22A Quick start guide . . . . . . . . . . . . . . . . . . . . . . 23

A.1 Building and running the standalone example . . . 23A.2 Running the ColliderBit example in GAMBIT . . . 23

B ColliderBit classes . . . . . . . . . . . . . . . . . . . . . . 23

1 Introduction

Despite decades of collider searches for physics beyondthe Standard Model (BSM), it remains the case thatwe lack an unambiguous discovery of such physics. Themany null results from the Large Hadron Collider (LHC)and other experiments allow us to constrain, to variousdegrees, the parameter spaces of many extension of theStandard Model (SM). These include “bottom-up” ef-fective theories and simplified models of dark matter,

• Higgs: Connect HiggsBounds and HiggsSignals as backends (more to come later)

• LEP limits (SUSY): Calculate , check against published cross section limits

• LHC particle searches: Poisson likelihood from "first principles"

• cross section• MC generation• detector sim• event analysis

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Cross-section calculationVeto point if smallDefault: Pythia 8

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Statistical routines

Fig. 1: Schematic diagram of the ColliderBit processing chain forLHC likelihoods.

For many models, these are the state-of-the-art. Formodels where an NLO (or better) calculation exists,e.g the MSSM, this is a conservative approximation, asthe k-factors are predominantly greater than one. TheLO+LL MSSM cross-sections are considerably quickerto evaluate than the full NLO results obtained usinge.g. Prospino [42–44]. A single evaluation of just thestrong production cross-sections for a CMSSM bench-mark point, with all relevant processes kinematicallyavailable, takes around 15 minutes of CPU time on amodern processor using Prospino 2.1 (Intel Core i5 at2.6GHz). This is clearly unusable in a scan where theevaluation of a single parameter point must be done intimes on the order of a few seconds. Although a fastinterpolation routine with added NLL corrections ex-ists in NLL-fast [45–49], this interpolation is limited tomodels with degenerate squark masses.

With the improvement to NLO+NLL, the error fromthe factorisation and renormalisation scales has beenshown to be as low as 10% [46] for a wide range ofprocesses and masses; however, PDF and ↵s uncertain-ties must be included in the total error budget. Theseincrease with the sparticle masses because the PDFs aremost poorly constrained at large scales and at large par-ton x. As an example, at 8 TeV NLL-fast 2.1 gives errorsof (+24.3%,�22.2%) and (+8.3%,�7.3%), for the PDFand ↵s, respectively, using the MSTW2008NLO PDFset [50], with gluino and squark masses set to 1.5 TeV.

Num. cores t (105 events) Speed-up

1 479 sec 14 148 sec 3.28 121 sec 4.016 79 sec 6.120 81 sec 5.9

Table 1: Time taken for the ColliderBit LHC likelihood calcula-tion as a function of the number of cores, for 100,000 SUSY eventsat the SPS1a parameter point [53, 54], including all sub-processes.The processes were run on a single computer node, with ISR, FSR,and full hadronisation enabled, but multiple parton interactionsand tau decay spin correlations disabled. GAMBIT was compiledwith full optimisation settings (cf. Section 11 of Ref. [1]).

Because 1.5 TeV is at the edge of the LHC reach at thatenergy, the total error budget here will not drop muchbelow 25% even with NLO+NLL cross-sections.3

In light of the above, we take the conservative pathof calculating likelihoods with the LO Pythia 8 cross-sections for the LHC. Assigning errors to these cross-sections is rather meaningless, considering the mono-tonic nature of LO scale-dependence, and the fact thatthe LO cross-sections in BSM models are known to al-most always lie significantly below the NLO and higherorder cross-section, sometimes by as much as a factorof two.4 The LO cross-sections are hence nearly alwaysmore conservative than the lower edge of the most pes-simistic NLO uncertainty band due to renormalisationscale systematics. We have verified that this choice, com-bined with the approximations used in the event anddetector simulation, results in limits equal to or moreconservative than those in the included ATLAS andCMS analyses (see Section 2.1.7). In future releases wewill allow the user to supply cross-sections as input tothe event generation, allowing one to calculate themusing any preferred choice of external code (known inGAMBIT as a “backend”).

2.1.4 Monte Carlo event generation

For the ColliderBit event generation, we supply an inter-face to the Pythia 8 [38, 39] event generator, alongsidecustom code that parallelises the main event loop ofPythia using OpenMP.5 This substantially reduces theruntime, as seen in Table 1.

For the purposes of BSM searches, many time-consuming generator components also add little to the

3With the CTEQ6.6M PDF set [51], the errors increase to(+63.1%,�38.5%) and (+15.6%,�10.3%); these uncertaintieswill reduce somewhat as PDF fits including higher-x LHC databecome available.4For a recent thorough exploration of K-factors in the MSSM upto approximate NNLO+NNLL order see [? ] and Fig. 2 within.5For an earlier similar approach, see Ref. [52].

s = � ⇥ ✏⇥ LCalculating


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MC eventgeneration

Default: Pythia 8

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(OpenMP)

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Default: Pythia 8

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• Use LO+LL cross-sections from MC generator by default

• All that exists for many models

• Behind state-of-the-art for SUSY(but gives conservative limits)

• In global fit: skip event generation if initial max cross section estimate is very small

• Future: option for user-supplied cross-sections


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MC eventgeneration

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• Connects Pythia 8 as backend

• Event generation loop parallelised in ColliderBit with OpenMP

• Can generate 104 events in a few seconds on 8 CPUs

• Further speed-up by turning off less important Pythia options (e.g. MPI)

• Can add matrix elements for new models via MadGraph-Pythia interface


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MC eventgeneration

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• Have interface to DELPHES…

• …but ROOT is not thread-safe

• BuckFast: Our own thread-safe detector sim based on four-vector smearing

• Run in the parallelised event loop

• Good agreement with DELPHES


5


MC eventgeneration

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Event analyses















• Have interface to DELPHES…

• …but ROOT is not thread-safe

• BuckFast: Our own thread-safe detector sim based on four-vector smearing

• Good agreement with DELPHES

• Run in the parallelised event loop

9

�

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Fig. 2: Comparisons of ATLAS event observables between the no-detector “truth” configuration, ColliderBit’s BuckFast 4-vectorsmearing simulation, and the Delphes fast simulation code, for a CMSSM point near the current ATLAS search limit (see main text).The ratio plots are computed relative to Delphes, to best evaluate the performance of BuckFast. The major e↵ects of detector simulationare seen to be due to lepton e�ciencies, with explicit resolution modelling producing relatively minor e↵ects. BuckFast and Delphestypically agree to within a few percent for leptons, but some larger di↵erences remain for b-jets and missing ET. The latter is currentlyunsmeared in BuckFast, but the origin of the deviation at high-Emiss

T is unclear since the reconstructed ATLAS EmissT closely matches

the truth value above 70 GeV [61].

9

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Fig. 2: Comparisons of ATLAS event observables between the no-detector “truth” configuration, ColliderBit’s BuckFast 4-vectorsmearing simulation, and the Delphes fast simulation code, for a CMSSM point near the current ATLAS search limit (see main text).The ratio plots are computed relative to Delphes, to best evaluate the performance of BuckFast. The major e↵ects of detector simulationare seen to be due to lepton e�ciencies, with explicit resolution modelling producing relatively minor e↵ects. BuckFast and Delphestypically agree to within a few percent for leptons, but some larger di↵erences remain for b-jets and missing ET. The latter is currentlyunsmeared in BuckFast, but the origin of the deviation at high-Emiss

T is unclear since the reconstructed ATLAS EmissT closely matches

the truth value above 70 GeV [61].


5


MC eventgeneration

Default: Pythia 8

Detectorsimulation

Default: BuckFast

Event analyses

. . .N cores

(OpenMP)

MC eventgeneration

Default: Pythia 8

Detectorsimulation

Default: BuckFast

Event analyses















• Analysis framework independent of MC generator and detector sim

• Uses public HepUtils classes

• Included analyses (8 TeV):• ATLAS SUSY searches:

- 0 lep - 0-1-2 lep stop - b-jet plus MET - 2 lep EW- 3 lep EW

• CMS multilepton SUSY search• CMS DM searches:

- top pair plus MET - mono-b- mono-jet

• 13 TeV analyses in the pipelineAnders Kvellestad 19

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• Poisson distribution, marginalised over systematic uncertainty (via nulike)

8

and CMS the jet momentum is smeared by a 3%Gaussian resolution.

b-jets: Truth-level jet tags are obtained by matchingjets to final b-partons for �R < 0.4; a more robustapproach using final b-hadrons is also available, butby construction agrees less well with the parton-based Delphes and LHC Run 1 tagging calibrations.As for taus, tagging e�ciencies and mistag rates areapplied in each analysis code, to allow the use ofdi↵erent tagger configurations in di↵erent analyses.

Missing energy (MET): MET is constructed at gen-erator level by summing the transverse momenta ofinvisible particles within the acceptance of the de-tector, and all particles outside the acceptance. No“soft-term” MET smearing is currently applied, sincefor events with real hard-process invisible particlesthe ATLAS reconstruction of Emiss

T

is within a fewpercent of the true value at all scales, and within1% above 70 GeV [61]. The same approach is takento define the “truth MET” in the “no simulation”mode.

BuckFast is significantly faster than Delphes. One reasonfor this is that the operations it performs are computa-tionally far simpler. The other is that the ROOT frame-work on which Delphes is based is not thread-safe, somust be run serially within an OpenMP critical block;in contrast, BuckFast can be run in parallel along withour parallelised version of Pythia 8 (cf. Section 2.1.4).

2.1.7 LHC event analysis framework

ColliderBit provides a simple analysis framework, builton the event record classes described in Sec. 2.1.5.Each analysis routine is a C++ class derived from theBaseAnalysis class, which provides the usual interface ofa pre-run init method and an in-run analyze method tobe called on each event. The user can choose which anal-yses to run in a given scan directly from the GAMBITconfiguration file. Using the generic ColliderBit eventrecord classes means that the analyses can be automat-ically run on either unsmeared truth records or onesto which detector e↵ects (other than jet tagging rates)have been applied.

The result of an analysis is a set of SignalRegionDataobjects. Each of these encodes the predicted event countsin a particular signal region of the analysis, from bothsignal and background processes. The signal numbers areobtained by normalising the distributions of simulatedevents to the integrated luminosity of the original exper-imental data analysis. The BaseAnalysis class providesadditional methods for statistically combining analyses(either equivalent or orthogonal), and for specifying thee↵ective luminosity simulated in the Monte Carlo step.

2.1.8 LHC Statistics Calculations

To determine the basic likelihood of observing n eventsin a certain signal region, given a signal prediction s,we use the marginalised form of Eq. 1 [62–64]. Thisallows us to incorporate systematic uncertainties on thesignal prediction (�s) and background estimate (�b) intothe calculation, by marginalising over the probabilitydistribution of a rescaling parameter ⇠:

L(n|s, b) =Z 1

0

[⇠(s+ b)]n e�⇠(b+s)

n!P (⇠)d⇠ . (3)

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2

⇠ = �

2

s + �

2

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P (⇠|�⇠) =1p2⇡�⇠

exp

"�1

2

✓1 � ⇠

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1

⇠

exp

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2

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The steps we have described so far allow ColliderBitto calculate the predicted number of events in any givensignal region, defined by a specific set of observables andkinematic cuts, and to compute the likelihood for that re-gion. However, certain ATLAS and CMS analyses makeuse of multiple signal regions, allowing analysis cuts tobe optimised according to the specific characteristics ofeach model being tested. These signal regions may over-lap, and so contain events in common. The likelihoodfunctions from overlapping signal regions are thereforenot independent. Ideally, information would be availablefrom the experiments about the degree to which thisoverlap occurs, which would allow GAMBIT analyses toinclude all signal regions and their correlations in thefinal likelihood for a given analysis.

As this information is not presently available, Collid-erBit computes the likelihood for a given analysis on thebasis of the signal region expected to give the strongestlimit. It does this individually for each model, by calcu-lating the expected number of events for every possiblesignal region considered in the the original ATLAS or

• For each analysis: Use likelihood from the signal region with the best expected sensitivity

• Combined likelihood on assumption that the different analyses are orthogonal (user’s responsibility!)


4. Some preliminary GAMBIT results

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Pat Scott – Dec 1 2016 – AAO Colloquium Searches for dark matter with GAMBIT

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23

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…and some ongoing work on other models…

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1000

1200C

P-o

ddH

iggs

mas

s(G

eV) R

elativeprobability

P/P

max


0.2

0.4

0.6

0.8


MultiNest



★

★ Best fit


G A M B I T�5

�4

�3

�2

�1

0

log 1

0

(�h /⇤

G

eV)

Relative

probabilityP

/Pm

ax

45 50 55 60 65 70m (GeV)

0.2

0.4

0.6

0.8






400

600

800

1000

1200C

P-o

ddH

iggs

mas

s(G

eV) R

elativeprobability

P/P

max


0.2

0.4

0.6

0.8


MultiNest



★

★ Best fit


G A M B I T�5

�4

�3

�2

�1

0

log 1

0

(�h /⇤

G

eV)

Relative

probabilityP

/Pm

ax

45 50 55 60 65 70m (GeV)

0.2

0.4

0.6

0.8




Pat Scott – Dec 1 2016 – AAO Colloquium Searches for dark matter with GAMBITAnders Kvellestad 26

• GAMBIT is a general framework for BSM global fits

• Public code release Jan/Feb 2017

• 9 papers in preparation: - 3 physics studies with GAMBIT - the main GAMBIT paper- 5 module papers (incl. ColliderBit)

• ColliderBit is a fast and model-independent tool for LHC recasting

• We would like to backend your code!

Summary and outlook


Backup slides

Screenshot of config file for SUSY scans

Anders Kvellestad

Anders KvellestadAnders Kvellestad

G A M B I T

Backends: mix and match


Diagnostics on connected backends


Other features

G A M B I T

Other nice technical features

Easy to add new observables and likelihoods (backupslides)User interface: yaml file (backup slides)Scanners: Nested sampling, differential evolution, MCMC,genetic algorithm, t-walk. . .Mixed-mode MPI + openMP parallelisation, mostlyautomated! scales to 10k+ coresdiskless generalisation of various Les Houches AccordsBOSS: dynamic loading of C++ classes from backends (!)all-in or module standalone modes – easily implementedfrom single cmake scriptautomatic getters for obtaining, configuring + compilingbackends1

flexible output streams (ASCII, databases, HDF5, . . . )more more more. . .

1if a backend won’t compile/crashes/shares your cat pics with the NSA,blame the authors (not us. . . except where we are the authors. . . )

Pat Scott – Dec 1 2016 – AAO Colloquium Searches for dark matter with GAMBIT slide from Pat Scott

G AM B I T


NormalDist

StandardModel_Higgs_running StandardModel_Higgs

MSSM63atQ

MSSM30atQ

MSSM63atMGUT

MSSM25atQ

MSSM24atQ

MSSM20atQStandardModel_SLHA2

MSSM19atQ MSSM16atQ

MSSM30atMGUT NUHM2 NUHM1 CMSSM

VCMSSM

mSUGRA

mSUGRAb

SingletDMZ3 SingletDM_running SingletDM

nuclear_params_fnq nuclear_params_sigma0_sigmal nuclear_params_sigmas_sigmal MSSM11atQ

MSSM15atQ

MSSM10atQ

MSSM10batQ

MSSM9atQ

MSSM7atQ

MSSM10catQ

Halo_gNFWHalo_gNFW_rho0

Halo_gNFW_rhosHalo_Einasto

Halo_Einasto_rho0

Halo_Einasto_rhos

G AM B I T

Hierarchical model database

Anders Kvellestad

limD!1

Vinteresting

Vtotal

= 0

Finding interesting parameter regions gets harder with increasing number of dimensions…

…

…so simply picking points «at random» will be highly inefficient…

Anders Kvellestad

…p

~x = (x1, x2, . . . , xD)xi ⇠ U(0, 1)

0 1

(

…and it will mainly explore the boundary of the parameter space!

P (boundary) = 1� P (not boundary) = 1� pD

Anders Kvellestad

from Roberto Trotta

Roberto Trotta

Marginalization vs profiling (maximising)

Marginal posterior:

P (�1|D) =�

L(�1, �2)p(�1, �2)d�2

Profile likelihood:

L(�1) = max�2L(�1, �2)

θ2

θ1

Best-fit (smallest chi-squared)

(2D plot depicts likelihood contours - prior assumed flat over wide range)

⊗Profile likelihood

Best-fit Posterior mean

Marginal posterior

} Volume effect

an update on gambit · an update on gambit anders kvellestad, nordita cern, december 13, 2016 on...

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