One of the simplest viable models for dark matter is an additional neutral scalar, stabilised by a Z 2 symmetry. Using the GAMBIT package and combining results from four independent samplers, we present Bayesian and frequentist global fits of this model. We vary the singlet mass and coupling along with 13 nuisance parameters, including nuclear uncertainties relevant for direct detection, the local dark matter density, and selected quark masses and couplings. We include the dark matter relic density measured by Planck, direct searches with LUX, PandaX, SuperCDMS and XENON100, limits on invisible Higgs decays from the a cornellj@physics.mcgill.ca Large Hadron Collider, searches for high-energy neutrinos from dark matter annihilation in the Sun with IceCube, and searches for gamma rays from annihilation in dwarf galaxies with the Fermi-LAT. Viable solutions remain at couplings of order unity, for singlet masses between the Higgs mass and about 300 GeV, and at masses above ∼1 TeV. Only in the latter case can the scalar singlet constitute all of dark matter. Frequentist analysis shows that the low-mass resonance region, where the singlet is about half the mass of the Higgs, can also account for all of dark matter, and remains viable. However, Bayesian considerations show this region to be rather fine-tuned.

We introduce DarkBit, an advanced software code for computing dark matter constraints on various extensions to the Standard Model of particle physics, comprising both new native code and interfaces to external packages. This release includes a dedicated signal yield calculator for gamma-ray observations, which significantly extends current tools by implementing a cascade decay Monte Carlo, as well as a dedicated likelihood calculator for current and future experiments (gamLike). This provides a general solution for studying complex particle physics models that predict dark matter annihilation to a multitude of final states. We also supply a direct detection package that models a large range of direct detection experiments (DDCalc), and provides the corresponding likelihoods for arbitrary combinations of spin-independent and spin-dependent scattering processes. Finally, we provide custom relic density routines along with interfaces to DarkSUSY, micrOMEGAs, and the neutrino telescope likelihood package nulike. DarkBit is written in the framework of the Global And Modular Beyond the Standard Model Inference Tool (GAMBIT), providing seamless integration into a comprehensive statistical fitting framework that allows users to explore new models with both particle and astrophysics constraints, and a cona

We study the seven-dimensional Minimal Supersymmetric Standard Model (MSSM7) with the new GAM-BIT software framework, with all parameters defined at the weak scale. Our analysis significantly extends previous weak-scale, phenomenological MSSM fits, by adding more and newer experimental analyses, improving the accuracy and detail of theoretical predictions, including dominant uncertainties from the Standard Model, the Galactic dark matter halo and the quark content of the nucleon, and employing novel and highly-efficient statistical sampling methods to scan the parameter space. We find regions of the MSSM7 a

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