GAMBIT: the global and modular beyond-the-standard-model inference tool

Athron, Peter; Balázs, Csaba; Bringmann, Torsten; Buckley, A. G.; Chrząszcz, M.; Conrad, J.; Cornell, Jonathan M.; Dal, Lars A.; Dickinson, H. J.; Edsjö, Joakim; Farmer, Benjamin; Gonzalo, Tomás E.; Jackson, P.; Krislock, Abram; Kvellestad, Anders; Lundberg, J.; McKay, James; Mahmoudi, F.; Martinez, Gregory D.; Putze, A.; Raklev, Are; Ripken, J.; Rogan, C.; Saavedra, A. F.; Savage, Christopher; Scott, Pat; Seo, S. h.; Serra, N.; Weniger, Christoph; White, M. J.; Wild, Sebastian

doi:10.1140/epjc/s10052-017-5321-8

Cited by 91 publications

(155 citation statements)

References 259 publications

(382 reference statements)

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“…These hook the module functions up to each other and run them in an order that ensures that all dependencies are computed before the functions that depend on them. Full details of this process can be found in the main GAMBIT paper [4].…”

Section: Computational Frameworkmentioning

confidence: 99%

“…Observables that are calculated directly from the input model parameters (Table 1) are distinguished from those that involve the calculation of intermediate Wilson coefficients (Tables 2, 3). In FlavBit 1.0.0, observables are implemented for MSSM models ('MSSM63atQ' and descendants; see [4]), and for a flavour EFT model ('WC') where the Wilson coefficients are specified directly as model parameters, and scanned over.…”

Section: Interfaces To External Codesmentioning

confidence: 99%

“…In this article we present FlavBit, a flavour physics library designed in the context of the Global And Modular BSM Inference Tool (GAMBIT) framework [4], but also usable in standalone form. FlavBit allows users to predict flavour physics observables in various models, using external programs such as SuperIso [5][6][7], and then calculate combined likelihoods for arbitrary combinations of the observables.…”

Section: Introductionmentioning

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%

“…In Sect. 4 we discuss the predictions and measurements of individual observables included in FlavBit 1.0.0, and highlight aspects of new physics models to which the different measurements are sensitive. Section 5 gives details of the likelihood calculations that FlavBit performs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

FlavBit: a GAMBIT module for computing flavour observables and likelihoods

Bernlochner¹,

Chrząszcz²,

Dal³

et al. 2017

Eur. Phys. J. C

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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.

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Section: Computational Frameworkmentioning

confidence: 99%

Section: Interfaces To External Codesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 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

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Likelihood analysis of the flavour anomalies and g – 2 in the general two Higgs doublet model

Athron

Balázs

Gonzalo

et al. 2022

J. High Energ. Phys.

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We present a likelihood analysis of the general two Higgs doublet model, using the most important currently measured flavour observables, in view of the anomalies in charged current tree-level and neutral current one-loop rare decays of B mesons in b → cl$$ \overline{\nu} $$ ν ¯ and b → sμ+μ− transitions, respectively. We corroborate that the model explains the latter and it is able to simultaneously fit the experimental values of the R(D) charged current ratio at 1σ, but it can not accommodate the D* charmed meson observables R(D*) and FL(D*). We find that the fitted values for the angular observables in b → sμ+μ− transitions exhibit better agreement with the general two Higgs double model in comparison to the SM. We also make predictions for future collider observables BR(t → ch), BR(h → bs), BR(h → τμ), BR(Bs → τ+τ−), BR(B+ → K+τ+τ−) and the flavour violating decays of the τ lepton, BR(τ → 3μ) and BR(τ → μγ). The model predicts values of BR(t → ch), BR(Bs → τ+τ−) and BR(B+ → K+τ+τ−) that are out of reach of future experiments, but its predictions for BR(h → bs) and BR(h → τμ) are within the future sensitivity of the HL-LHC or the ILC. We also find that the predictions for the τ → 3μ and τ → μγ decays are well within the projected limits of the Belle II experiment. Finally, using the latest measurement of the Fermilab Muon g − 2 Collaboration, we performed a simultaneous fit to ∆aμ constrained by the charged anomalies, finding solutions at the 1σ level. Once the neutral anomalies are included, however, a simultaneous explanation is unfeasible.

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Does SUSY have friends? A new approach for LHC event analysis

Mullin

Nicholls

Pacey

et al. 2021

J. High Energ. Phys.

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We present a novel technique for the analysis of proton-proton collision events from the ATLAS and CMS experiments at the Large Hadron Collider. For a given final state and choice of kinematic variables, we build a graph network in which the individual events appear as weighted nodes, with edges between events defined by their distance in kinematic space. We then show that it is possible to calculate local metrics of the network that serve as event-by-event variables for separating signal and background processes, and we evaluate these for a number of different networks that are derived from different distance metrics. Using a supersymmetric electroweakino and stop production as examples, we construct prototype analyses that take account of the fact that the number of simulated Monte Carlo events used in an LHC analysis may differ from the number of events expected in the LHC dataset, allowing an accurate background estimate for a particle search at the LHC to be derived. For the electroweakino example, we show that the use of network variables outperforms both cut-and-count analyses that use the original variables and a boosted decision tree trained on the original variables. The stop example, deliberately chosen to be difficult to exclude due its kinematic similarity with the top background, demonstrates that network variables are not automatically sensitive to BSM physics. Nevertheless, we identify local network metrics that show promise if their robustness under certain assumptions of node-weighted networks can be confirmed.

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GAMBIT: the global and modular beyond-the-standard-model inference tool

Cited by 91 publications

References 259 publications

FlavBit: a GAMBIT module for computing flavour observables and likelihoods

FlavBit: a GAMBIT module for computing flavour observables and likelihoods

Likelihood analysis of the flavour anomalies and g – 2 in the general two Higgs doublet model

Does SUSY have friends? A new approach for LHC event analysis

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