SpecBit, DecayBit and PrecisionBit: GAMBIT modules for computing mass spectra, particle decay rates and precision observables

Athron, Peter; Balázs, Csaba; Dal, Lars A.; Edsjö, Joakim; Farmer, Benjamin; Gonzalo, Tomás E.; Kvellestad, Anders; McKay, James; Putze, A.; Rogan, C.; Scott, Pat; Weniger, Christoph; White, M. J.

doi:10.1140/epjc/s10052-017-5390-8

Cited by 53 publications

(101 citation statements)

References 190 publications

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…[10], we do not include the prediction of ΔM s from FeynHiggs nor the resulting B mass asymmetry likelihood (deltaMB_LL). We employ nuisance likelihoods from PrecisionBit [14] to constrain α s and m t .…”

Section: Examplesmentioning

confidence: 99%

“…The other two likelihood functions available in FlavBit (based on the B meson mass asymmetry and tree-level leptonic and semi-leptonic decays) have no dependence on the three Wilson coefficients that we vary. We also scan over the M S b quark mass and the strong coupling as nuisance parameters, computing associated nuisance likelihoods with PrecisionBit [14]. We sample the parameter space with nested sampling [125,126], using 20 000 live points and a tolerance of 0.1; see Ref.…”

Section: Wilson Coefficient Fitmentioning

confidence: 99%

“…FlavBit takes into account all theoretical and experimental correlations between the different observables. The resulting likelihoods can be incorporated into the GAMBIT global likelihood to scan the parameter spaces of various models for new physics [4,[8][9][10][11], taking into account complementary constraints from direct production [12], dark matter searches [13], and SM and related precision measurements [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

FlavBit: a GAMBIT module for computing flavour observables and likelihoods

Bernlochner¹,

Chrząszcz²,

Dal³

et al. 2017

Eur. Phys. J. C

Self Cite

View full text Add to dashboard Cite

Flavour physics observables are excellent probes of new physics up to very high energy scales. Here we present FlavBit, the dedicated flavour physics module of the globalfitting package GAMBIT. FlavBit includes custom implementations of various likelihood routines for a wide range of flavour observables, including detailed uncertainties and correlations associated with LHCb measurements of rare, leptonic and semileptonic decays of B and D mesons, kaons and pions. It provides a generalised interface to external theory codes such as SuperIso, allowing users to calculate flavour observables in and beyond the Standard Model, and then test them in detail against all relevant experimental data. We describe FlavBit and its constituent physics in some detail, then give examples from supersymmetry and effective field theory illustrating how it can be used both as a standalone library for flavour physics, and within GAMBIT.

show abstract

Section: Examplesmentioning

confidence: 99%

Section: Wilson Coefficient Fitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

FlavBit: a GAMBIT module for computing flavour observables and likelihoods

Bernlochner¹,

Chrząszcz²,

Dal³

et al. 2017

Eur. Phys. J. C

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the up and down quark masses, we take the central values from the 2014 review, and scan over a range of ±20% around the central values. This is intended to capture the ±3σ range implied by the likelihoods in PrecisionBit [31], which deal with correlated mass-ratio measurements. The nuclear couplings also incorporate a range of ±3σ around the best estimates.…”

Section: Scanner Performance Comparisonsmentioning

confidence: 99%

Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module

Martinez¹,

McKay²,

Farmer³

et al. 2017

Eur. Phys. J. C

Self Cite

102

160

View full text Add to dashboard Cite

We introduce ScannerBit, the statistics and sampling module of the public, open-source global fitting framework GAMBIT. ScannerBit provides a standardised interface to different sampling algorithms, enabling the use and comparison of multiple computational methods for inferring profile likelihoods, Bayesian posteriors, and other statistical quantities. The current version offers random, grid, raster, nested sampling, differential evolution, Markov Chain Monte Carlo (MCMC) and ensemble Monte Carlo samplers. We also announce the release of a new standalone differential evolution sampler, Diver, and describe its design, usage and interface to ScannerBit. We subject Diver and three other samplers (the nested sampler MultiNest, the MCMC GreAT, and the native ScannerBit implementation of the ensemble Monte Carlo algorithm T-Walk) to a battery of statistical tests. For this we use a realistic physical likelihood function, based on the scalar singlet model of dark matter. We examine the performance of each sampler as a function of its adjustable settings, and the dimensionality of the sampling problem. We evaluate performance on four metrics: optimality of the best fit found, completeness in exploring the best-fit region, number of likelihood evaluations, and total runtime. For Bayesian posterior estimation at high resolution, T-Walk provides the most accurate and timely mapping of the full parameter space. For profile likelihood analysis in less than about ten dimensions, we find that Diver and MultiNest score similarly in terms of best fit and speed, outperforming GreAT a

show abstract

“…2 SpecBit, DecayBit and PrecisionBit updates GAMBIT 1.1 also includes a simultaneous version update to SpecBit, DecayBit and PrecisionBit [15]. In this section we briefly discuss the changes made to the module functions and structure of the code.…”

Section: Diagnosticsmentioning

confidence: 99%